A system and method for agentic conversation for responsible artificial intelligence scoring of artificial intelligence

Abstract

A system and method for agentic conversation for responsible artificial intelligence scoring of artificial intelligence is disclosed. The system includes a processor and memory storing instructions that cause the processor to: receive responses via a conversational interface with a candidate artificial intelligence model aligned to responsible artificial intelligence principles privacy, accountability, safety, security, fairness, explainability, reliability, sustainability; retrieve a question bank and generate question sets dynamically from contextual understanding of prior responses; perform a secure conversational exchange ensuring confidentiality; analyse responses for ethical risks, bias, factual inconsistencies, and compliance deviations; adaptively generate real-time follow- ups via dynamic logic; compute dynamic scores per principle using quantitative metrics for regulatory compliance; compile a structured compliance report with summary, gaps, remediations and confidence; and present via a user interface the scores, report, and metrics for visualization and comparison.

Description

[0001] A SYSTEM AND METHOD FOR AGENTIC CONVERSATION FOR RESPONSIBLE ARTIFICIAL INTELLIGENCE SCORING OF ARTIFICIAL INTELLIGENCE

[0002] EARLIEST PRIORITY DATE:

[0003] This Application claims priority from a provisional patent application filed in India having Patent Application No. 202441101486, filed on December 20, 2024, and titled “SYSTEM AND METHOD FOR AGENTIC CONVERSATION FOR RESPONSIBLE ARTIFICIAL INTELLIGENCE SCORING OF ARTIFICIAL INTELLIGENCE MODELS”.

[0004] FIELD OF INVENTION

[0005] The present invention relates to the field of data analytics and database. More particularly, the present invention relates to a system and method for agentic conversation for responsible artificial intelligence scoring of artificial intelligence.

[0006] BACKGROUND

[0007] Artificial intelligence systems, particularly large language models and similar advanced computational frameworks, are increasingly being integrated into a wide range of applications, including customer service, healthcare decision support, financial advisory workflows, educational platforms, and governmental service delivery. As these systems interact directly with end-users and often operate in sensitive or high-impact environments, there is a growing need to ensure that their behaviour aligns with ethical expectations, organizational values, and regulatory requirements.

[0008] Despite the rapid adoption of such systems, the underlying decision-making processes of many artificial intelligence models remain opaque. Their responses may be influenced by training-data biases, contextual misunderstandings, inconsistent reasoning patterns, or unintended emergent behaviours. These limitations create challenges for organizations seeking to deploy artificial intelligence responsibly, particularly when attempting to verify adherence to principles such as privacy protection, fairness, accountability, transparency, reliability, and overall safety.

[0009] Existing evaluation approaches tend to rely on static tests or narrow performance benchmarks that fail to capture real-time conversational behaviour or uncover deeper ethical and contextual issues. Moreover, many organizations lack the means to systematically identify compliance gaps, assess risk exposure, or validate whether an artificial intelligence system behaves appropriately across different regulatory or domain-specific conditions.

[0010] Hence, there is a need for an improved system and method for agentic conversation for responsible artificial intelligence scoring of artificial intelligence to address the aforementioned issue(s).

[0011] OBJECTIVES OF THE INVENTION

[0012] The primary objective of the invention is to provide a system and method capable of evaluating the behaviour of an artificial intelligence model through an interactive conversational process that reflects responsible artificial intelligence principles.

[0013] Another objective of the invention is to enable dynamic and context-aware question generation so that the evaluation adapts to the model’s responses and uncovers hidden risks, inconsistencies, or ethical deviations.

[0014] Yet another objective of the invention is to deliver a structured and quantifiable scoring mechanism that measures the candidate artificial intelligence model’s alignment with ethical, legal, and regulatory expectations.

[0015] A further objective of the invention is to generate a comprehensive compliance report that summarizes findings, highlights gaps, and provides actionable remediation strategies based on the evaluation outcomes.

[0016] Still another objective of the invention is to offer a user interface that allows authorized users to visualize results, interpret compliance metrics, and compare performance across multiple artificial intelligence models for informed governance decisions. A still further objective of the invention is to ensure that the entire evaluation workflow remains secure, traceable, and transparent, thereby supporting auditability and organizational accountability in the deployment of artificial intelligence system.

[0017] SUMMARY

[0018] In accordance with an embodiment of the present disclosure, a system for agentic conversation for responsible artificial intelligence scoring of artificial intelligence is disclosed. The system includes a processor. The system includes a memory coupled to the processor, wherein the memory comprises instructions that, when executed by the processor, cause the processor to: receive a plurality of responses in a conversational interface with a candidate artificial intelligence model, wherein the plurality of responses corresponding to one or more responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability; retrieve a predefined question bank and generate a plurality of question sets dynamically corresponding to the one or more responsible artificial intelligence principles based on contextual understanding of prior responses and the pre-defined question bank; perform a secure conversational exchange for the conversational interface, wherein the secure conversational exchange ensuring data confidentiality, data integrity, and isolation of the conversational context throughout an evaluation process; analyse the plurality of responses to identify ethical risks, bias indicators, factual inconsistencies, and compliance deviations corresponding to the one or more responsible artificial intelligence principles; adaptively generate one or more subsequent questions in real-time by employing dynamic question generation logic that refines the plurality of question sets based on identified gaps, anomalies, flagged responses, and contextual learning derived from the plurality of responses; compute a plurality of dynamic scores corresponding to each of the one or more responsible artificial intelligence principles by utilizing quantitative performance metrics, wherein the quantitative performance metrics enabling measurement of the candidate artificial intelligence model’s compliance with ethical, legal, and regulatory standards; compile a structured compliance report comprising at least an evaluation summary, identified compliance gaps, suggested remediation strategies, and confidence indices corresponding to the plurality of dynamic scores; and present, via a user interface, the plurality of dynamic scores, the structured compliance report, and compliance metrics to enable a user operating a user device to visualize, interpret, and compare the candidate artificial intelligence model’s responsible performance thereby providing transparency and interpretability into the candidate artificial intelligence model’s responsible behaviour.

[0019] In accordance with an embodiment of the present disclosure, a method for agentic conversation for responsible artificial intelligence scoring of artificial intelligence is disclosed. The method includes receiving, by a processor, a plurality of responses in a conversational interface with a candidate artificial intelligence model, wherein the plurality of responses corresponding to one or more responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability. The method includes retrieving, by the processor, a pre-defined question bank and generate a plurality of question sets dynamically corresponding to the one or more responsible artificial intelligence principles based on contextual understanding of prior responses and the pre-defined question bank. The method includes performing, by the processor, a secure conversational exchange for the conversational interface, wherein the secure conversational exchange ensuring data confidentiality, data integrity, and isolation of the conversational context throughout an evaluation process. The method includes analysing, by the processor, the plurality of responses to identify ethical risks, bias indicators, factual inconsistencies, and compliance deviations corresponding to the one or more responsible artificial intelligence principles. The method includes adaptively generating, by the processor, one or more subsequent questions in real-time by employing dynamic question generation logic that refines the plurality of question sets based on identified gaps, anomalies, flagged responses, and contextual learning derived from the plurality of responses. The method includes computing, by the processor, a plurality of dynamic scores corresponding to each of the one or more responsible artificial intelligence principles by utilizing quantitative performance metrics, wherein the quantitative performance metrics enabling measurement of the candidate artificial intelligence model’s compliance with ethical, legal, and regulatory standards. The method includes compiling, by the processor, a structured compliance report comprising at least an evaluation summary, identified compliance gaps, suggested remediation strategies, and confidence indices corresponding to the plurality of dynamic scores. The method includes presenting, via a user interface, the plurality of dynamic scores, the structured compliance report, and compliance metrics to enable a user operating a user device to visualize, interpret, and compare the candidate artificial intelligence model’s responsible performance thereby providing transparency and interpretability into the candidate artificial intelligence model’s responsible behaviour.

[0020] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

[0021] BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

[0023] FIG. 1 illustrates a network environment of a system for agentic conversation for responsible artificial intelligence scoring of artificial intelligence in accordance with an embodiment of the present disclosure;

[0024] FIG. 2 illustrates a schematic diagram of a user device of FIG. 1, in accordance with an example implementation of the present subject matter;

[0025] FIG. 3 illustrates a schematic diagram of a system for agentic conversation for responsible artificial intelligence scoring of artificial intelligence of FIG. 1, in accordance with an embodiment of the present disclosure; FIG. 4 (a) is a flow chart representing the steps involved in a method for agentic conversation for responsible artificial intelligence scoring of artificial intelligence, in accordance with an embodiment of the present disclosure; and

[0026] FIG. 4 (b) illustrates continued steps of the method of FIG. 4 (a) in accordance with an embodiment of the present disclosure.

[0027] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

[0028] DETAILED DESCRIPTION

[0029] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

[0030] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

[0032] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

[0033] FIG. 1 illustrates a network environment of a system for agentic conversation for responsible artificial intelligence scoring of artificial intelligence in accordance with an embodiment of the present disclosure.

[0034] Referring to FIG. 1, a user device (104) corresponding to a user (108) may be communicatively coupled to a system (102). The user (108) may access the system (102) over a network (106). Examples of the user device (104) includes, but is not limited to, a mobile phone, desktop computer, portable digital assistant (PDA), smart phone, tablet, ultra-book, netbook, laptop, multi-processor system, microprocessor-based or programmable consumer electronic system, or any other communication device that a user may use. It will be appreciated that the system (102) may be presented to the user (108) on the user device (104) as a web application accessed through a browser, through a software application on the user device, or, particularly for smartphones, through a mobile application installed at the smartphone. It will be appreciated that, within the context of the disclosure herein, web application refers to a utility implemented on a networked computing system accessible by user device over the Internet (e.g. through browsers) wherein the bulk of the processing takes place at the networked computing system, mobile applications refer to applications installed on smartphones that may communicate with a networked computing system, and a “software” application refers generally to applications other than web browsers installed on other types of user device that may communicate with a networked computing system over the network (106). The network (106) may be a single communication network or a combination of multiple communication networks and may use a variety of different communication protocols. The network (106) may be a wireless network, a wired network, or a combination thereof. Examples of such individual personalized networks include, but are not limited to, Global System for Mobile Communication (GSM) network, Universal Mobile Telecommunications System (UMTS) network, Personal Communications Service (PCS) network, Time Division Multiple Access TDMA) network, Code Division Multiple Access (CDMA) network, Next Generation Network (NON), Public Switched Telephone Network (PSTN). Depending on the technology, the personalized network (106) may include various network entities, such as gateways and routers; however, such details have been omitted for the sake of brevity of the present description.

[0035] The system (102) may have a homepage that is presented to the user (108) accessing a top-level web address for web applications presented to the user (108) in a browser or a welcome screen for software and mobile applications. The homepage may include links to a user log-in interface or general information about the system (102) and the option to register as user (108). It will be appreciated that the presentation of a homepage may not be necessary, for example, if a user bypasses it by directly inputting a web address corresponding to a user log-in page, or if a separate mobile application is designed for users.

[0036] A new or unregistered user can access the user log-in interface, fill out the log-in information corresponding to the user's account, and indicate that the user wishes to sign in. It will be appreciated that any conventional registration and log-in techniques for web applications, software application, and mobile applications may be used, whichever is appropriate for the user. While registering the user may be prompted to provide username and corresponding user credentials, not limited to, password, geographical location, and contact information and upon receipt of the foregoing information, a corresponding user-profile may be created and stored on a respective database of the system (102).

[0037] In accordance with an embodiment of the present disclosure, a system for agentic conversation for responsible artificial intelligence scoring of artificial intelligence is disclosed. The system includes a processor. The system includes a memory coupled to the processor, wherein the memory comprises instructions that, when executed by the processor, cause the processor to: receive a plurality of responses in a conversational interface with a candidate artificial intelligence model, wherein the plurality of responses corresponding to one or more responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability; retrieve a predefined question bank and generate a plurality of question sets dynamically corresponding to the one or more responsible artificial intelligence principles based on contextual understanding of prior responses and the pre-defined question bank; perform a secure conversational exchange for the conversational interface, wherein the secure conversational exchange ensuring data confidentiality, data integrity, and isolation of the conversational context throughout an evaluation process; analyse the plurality of responses to identify ethical risks, bias indicators, factual inconsistencies, and compliance deviations corresponding to the one or more responsible artificial intelligence principles; adaptively generate one or more subsequent questions in real-time by employing dynamic question generation logic that refines the plurality of question sets based on identified gaps, anomalies, flagged responses, and contextual learning derived from the plurality of responses; compute a plurality of dynamic scores corresponding to each of the one or more responsible artificial intelligence principles by utilizing quantitative performance metrics, wherein the quantitative performance metrics enabling measurement of the candidate artificial intelligence model’s compliance with ethical, legal, and regulatory standards; compile a structured compliance report comprising at least an evaluation summary, identified compliance gaps, suggested remediation strategies, and confidence indices corresponding to the plurality of dynamic scores; and present, via a user interface, the plurality of dynamic scores, the structured compliance report, and compliance metrics to enable a user operating a user device to visualize, interpret, and compare the candidate artificial intelligence model’s responsible performance thereby providing transparency and interpretability into the candidate artificial intelligence model’s responsible behaviour.

[0038] It may be noted that the foregoing system is an exemplary system and may be implemented as computer executable instructions in any computing or processing environment, including in digital electronic circuitry or in computer hardware, firmware, device driver, or software. As such, the system is not limited to any specific hardware or software configuration.

[0039] FIG. 2 illustrates a schematic diagram of a user device, in accordance with an example implementation of the present subject matter. Referring to FIG. 2, the user device (104) may comprise a processor(s) (202), a memory(s) (204) coupled to and accessible by the processor(s) (202), and an interface (210) coupled to the memory(s) (204). The user device (104) disclosed herein may be same as the user device (104) described in FIG. 1. The functions of various elements shown in the figs., including any functional blocks labelled as "processor(s)", may be provided through the use of dedicated hardware as well as hardware capable of executing instructions. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term "processor" would not be construed to refer exclusively to hardware capable of executing instructions, and may implicitly comprise, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA). Other hardware, standard and / or custom, may also be coupled to the processor(s) (202). The user device (104) may further include a display (206) in addition to other components such as, but not limited to, keyboard, sensors, logic circuits etc. Further, the user device (104) may include data (208) which may include data (208) that may be stored, utilized or generated during the operation of the user device (104).

[0040] The memory(s) (204) may be a computer-readable medium, examples of which comprise volatile memory (e.g., RAM), and / or non-volatile memory (e.g., Erasable Programmable read-only memory, i.e. EPROM, flash memory, etc.). The memory(s) (204) may be an external memory, or internal memory, such as a flash drive, a compact disk drive, an external hard disk drive, or the like. The user device (104) may further include an interface (210) that may allow the connection or coupling of the user device (104) with one or more other devices, through a wired (e.g., Local Area Network, i.e., LAN) connection or through a wireless connection (e.g., Bluetooth®, Wi-Fi), for example, for connecting to the system shown in FIG. 1. The interface may also enable intercommunication between different logical as well as hardware components of the user device (104).

[0041] FIG. 3 illustrates a schematic diagram of a system for agentic conversation for responsible artificial intelligence scoring of artificial intelligence of FIG. 1, in accordance with an embodiment of the present disclosure. Referring to FIG. 3, the system (102) includes a processor(s) (302), a memory(s) (304) coupled to and accessible by the processor(s) (302), and database (346) coupled to the memory(s) (304).

[0042] The system (102) disclosed herein is the same as the system (102) described in FIG. 1. The functions of various elements shown in the figs., including any functional blocks labelled as "processor(s)", may be provided through the use of dedicated hardware as well as hardware capable of executing instructions. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term "processor" would not be construed to refer exclusively to hardware capable of executing instructions, and may implicitly comprise, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA). Other hardware, standard and / or custom, may also be coupled to the processor(s) (302). The system (102) may further include other components such as, but not limited to, keyboard, sensors, logic circuits, input / output interfaces etc. Further, the system (102) may include data which may include data that may be stored, utilized or generated during the operation of the computer implemented system (102).

[0043] The memory(s) (304) may be a computer-readable medium, examples of which comprise volatile memory (e.g., RAM), and / or non-volatile memory (e.g., Erasable Programmable read-only memory, i.e. EPROM, flash memory, etc.). The memory(s) (304) may be an external memory, or internal memory, such as a flash drive, a compact disk drive, an external hard disk drive, or the like. The system (102) may further include the user interface (348) that may allow the connection or coupling of the system (102) with one or more other devices, through a wired (e.g., Local Area Network, i.e., LAN) connection or through a wireless connection (e.g., Bluetooth®, Wi-Fi)., for example, for connecting to the user device (104) as shown in FIG. 1. The user interface (348) may also enable intercommunication between different logical as well as hardware components of the system (102).

[0044] The system (102) may be provided with a database (346) to a pre-defined question bank, a plurality of question sets, a plurality of responses, evaluation representations, an analysis result, a plurality of dynamic scores, structured compliance report, an audit input, a compliance metric. In an example implementation of the system (102) including one or more servers, the databases may databases local to the server or may be remote to the server. It may be noted that the data in the databases may be stored as a table or may be pre-stored as a mapping with the other. This application is not limited thereto.

[0045] The system (102) may include module(s). The module(s) may include a receiving module (306), a responsible agent module (308), a question strategy module (310), answer analysis module (312), a dynamic question generation module (314), a dynamic scoring module (316), and a reporting module (318). In one example, the module(s) may be implemented as a combination of hardware and firmware. In an example described herein, such combinations of hardware and firmware may be implemented in several different ways. For example, the firmware for module(s) may be processor (302) executable instructions stored on a non- transitory machine-readable storage medium and the hardware for the module(s) may include a processing resource (for example, implemented as either single processor or combination of multiple processors), to execute such instructions. Further, the hardware for the module(s) may include communication apparatuses, control circuitries involving electrical and electronics components, sensors, and interface devices, which may be in communication with each other for multidirectional communication therebetween.

[0046] Further, the system (102) includes data. The data may include data that is either stored or generated as a result of functions implemented by the system. It may be further noted that information stored and available in data may be utilized by the engine(s) for performing various functions by the system. In an example, data may include a responses data (322), a responsible artificial intelligence principle (324), a pre-defined question bank (326), a flagged responses, identified gaps, and anomalies data (328), a quantitative performance metrics (330), a dynamic score (332), a compliance report (334), and a compliance metric (336). It may be noted that such examples of the various functions are only indicative. The present approaches may be applicable to other examples without deviating from the scope of the present subject matter.

[0047] In the present examples, the non-transitory machine-readable storage medium may store instructions that, when executed by the processing resource, implement the functionalities of modules(s). In such examples, the system (102) may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions. In other examples of the present subject matter, the machine-readable storage medium may be located at a different location but accessible to the system (102) and the processor(s) (302).

[0048] In operation, the responsible agent module (308) is configured to instantiate a responsible agent. The responsible agent module (308) oversees a sequencing, synchronization, and contextual management of a conversational interface, a plurality of question sets, an analytical processes, and a dynamic scoring operations. The responsible agent module (308) maintains an internal state that reflects the evaluation progress, tracks interactions occurring within the conversational interface, and make sure that each responsible artificial intelligence principle comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability is appropriately assessed through the evaluation components managed by the processor (302).

[0049] In another embodiment, the responsible agent being configured to coordinate an evaluation workflow corresponding to the conversational interface. The responsible agent interprets the conversational context, determines which evaluation steps are to be executed, and ensures that the processor (302) appropriately sequences the retrieval of a pre-defined question bank, the generation of the plurality of question sets, the analysis of a plurality of responses, and the computation of the plurality of dynamic scores. The responsible agent module (308) further maintains continuity of the conversational interface by tracking conversation state, monitoring response dependencies, and ensuring that each responsible artificial intelligence principle is evaluated through the appropriate prompts and corresponding analytical operations.

[0050] By way of example, the responsible agent includes, but is not limited to a workflow-orchestration component sequencing evaluation tasks, a contextalignment component mapping responses to principles, and a decision-routing component determining when dynamic question generation or scoring updates are required.

[0051] In another embodiment, the responsible agent module (308) is configured to configure the responsible agent to initiate, monitor, and terminate conversational sessions with a candidate artificial intelligence model. The responsible agent module (308) initiates the conversational session by establishing the initial conversational parameters, loading the pre-defined question bank, and defining session boundaries required for evaluating one or more responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability. The responsible agent then monitors the conversational session by tracking message flow, detecting response latency, observing context shifts, and ensuring that each conversational turn is accurately captured and mapped to its corresponding responsible artificial intelligence principle. Upon completion of the evaluation objectives, the responsible agent terminates the conversational session by closing the conversational context, preserving session logs, and preparing the recorded responses for downstream analytical operations.

[0052] By way of example, the responsible agent includes, but is not limited to a sessioninitialization unit establishing conversational settings, a session-monitoring unit overseeing message exchanges, and a session-termination unit finalizing logs and closing the conversational interface.

[0053] In another embodiment, the initiating comprises establishing conversational context and session parameters, the monitoring comprises tracking conversational state, response history, and contextual indicators, and the terminating comprises closing the conversational context and persisting conversational logs for subsequent analysis. The monitoring comprises tracking conversational state, response history, and contextual indicators to ensure that each conversational turn remains aligned with the evolving evaluation requirements. The responsible agent module (308) observes continuity across messages, records contextual shifts, and identifies dependencies that may influence subsequent question generation or analytical interpretation. The terminating comprises closing the conversational context and persisting conversational logs for subsequent analysis, wherein the responsible agent finalizes the session by storing the response sequence, preserving contextual markers, and preparing the data for scoring and compliance assessment.

[0054] By way of example, the initiating, monitoring, and terminating functions include, but are not limited to defining session objectives, recording message timestamps, tracking topic transitions, and saving full conversational transcripts for downstream evaluation.

[0055] In another embodiment, the responsible agent module (308) is configured to configure the responsible agent to synchronize retrieval and customization of the plurality of question sets from the pre-defined question bank. The responsible agent module (308) retrieves the pre-defined question bank based on the responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability and further customizes the plurality of question sets by aligning them with prior conversational context, detected behavioural patterns, and principle-specific evaluation needs. The responsible agent make sure that each retrieved question aligns with the corresponding responsible artificial intelligence principle and that customization reflects the semantic indicators present in the plurality of responses generated by the candidate artificial intelligence model.

[0056] By way of example, the synchronized retrieval and customization include, but are not limited to selecting fairness-related queries after biased responses, refining privacy questions following disclosure issues, or adjusting safety prompts based on risk-associated behaviour.

[0057] In another embodiment, the responsible agent module (308) is configured to invoke the dynamic question generation logic upon detection of flagged responses or identified gaps. The responsible agent module (308) evaluates each response using predefined analysis criteria and determines whether a response must be flagged based on indicators such as ethical risk, factual inconsistency, bias tendency, or insufficient justification. When the responsible agent identifies a gap or flags a response, the processor activates the dynamic question generation logic to formulate one or more subsequent questions aligned with the responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability. The invoked dynamic question generation logic refines the plurality of question sets to probe deeper into the identified issue and ensures that the conversational interface adapts to the evolving behavioural signals of the candidate artificial intelligence model.

[0058] By way of example, the invocation includes, but is not limited to triggering fairness follow-ups after biased output, generating safety clarifications after risk- associated content, or producing privacy-related queries after improper disclosure patterns.

[0059] In another embodiment, the responsible agent module (308) is configured to aggregate intermediate analysis results, and orchestrate computation of the plurality of dynamic scores. The responsible agent module (308) collects semantic indicators, identified risks, detected inconsistencies, and contextual factors derived from each analytical operation and merges them into a unified evaluation dataset. The responsible agent then orchestrates the computation of the plurality of dynamic scores by directing the processor to apply quantitative performance metrics corresponding to responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability.

[0060] By way of example, the aggregation and orchestration include, but are not limited to combining fairness-related bias indicators, merging safety-related risk assessments, integrating privacy -violation signals, and directing sequential computation of principle-wise dynamic scores.

[0061] In another embodiment, the responsible agent module (308) is configured to trigger compilation and publication of the structured compliance report and the user interface. The responsible agent initiates compilation of the structured compliance report by directing the processor (302) to assemble evaluation summaries, identified compliance gaps, suggested remediation strategies, and confidence indices corresponding to the plurality of dynamic scores. The responsible agent further triggers publication of a user interface (348) by instructing the processor (302) to render the plurality of dynamic scores, the structured compliance report, and compliance metrics in a visual format that enables one or more users to interpret and compare the responsible performance of the candidate artificial intelligence model.

[0062] By way of example, the triggering includes, but is not limited to initiating generation of a principle-wise compliance summary, activating display modules for score visualization, or publishing updated dashboards after completing evaluation cycles.

[0063] In one embodiment, the receiving module (306) is configured to receive the plurality of responses in the conversational interface with the candidate artificial intelligence model. The plurality of responses corresponds to structured, semi- structured, or unstructured outputs generated by the candidate artificial intelligence model in reaction to the conversational prompts provided during the evaluation process. The plurality of responses may include explanations, factual statements, decision rationales, safety-related clarifications, privacy -related disclosures, expressions of uncertainty, or other contextual information that reflects the behavioural characteristics of the candidate artificial intelligence model under assessment. The processor (302) interprets the plurality of responses using predefined responsible artificial intelligence principles such as privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability, and aligns each received response to one or more of these principles for subsequent analysis.

[0064] By way of example, the plurality of responses includes, but is not limited to textual outputs generated by the candidate artificial intelligence model in reaction to safety-oriented queries, fairness-related rationales returned when the processor provides demographic-sensitive decision-making scenarios, privacy-related explanations corresponding to prompts requesting disclosure-handling behaviour, accountability-related responses that articulate chain-of-thought responsibility or decision traceability, and reliability -related outputs associated with ambiguous or conflicting instructions. In the same embodiment, the conversational interface includes, but is not limited to, an interactive prompt-response channel, a message sequencing mechanism configured to maintain conversational context, and a response-logging component configured to persist the plurality of responses for further analysis.

[0065] In one embodiment, the plurality of responses corresponding to one or more responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability. In this embodiment, the receiving module (306) classifies the plurality of responses to determine whether the candidate artificial intelligence model adheres to expected ethical standards, discloses sensitive data improperly, demonstrates biased reasoning, offers insufficient justification, or exhibits instability when interacting with safety-critical prompts. The processor (302) further correlates each received response with its respective responsible artificial intelligence principle, enabling structured analysis, cross-dimensional comparison, and subsequent scoring based on predefined evaluation criteria corresponding to each principle.

[0066] By way of example, the plurality of responses includes, but is not limited to, a privacy-related statement describing data-handling behaviour, an accountability rationale explaining decision traceability, a safety clarification addressing harmful output mitigation, or a fairness justification concerning demographic neutrality.

[0067] In another embodiment, the answer analysis module (312) is configured to classify the plurality of responses into corresponding responsible artificial intelligence dimensions comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability. The answer analysis module (312) evaluates each response generated by the candidate artificial intelligence model and associates the response with a responsible artificial intelligence dimension based on its semantic content, contextual indicators, and principle-specific behavioural relevance. The answer analysis module (312) identifies linguistic cues, intent signals, reasoning patterns, and contextual alignments within the plurality of responses to determine whether a response pertains to protecting sensitive data, preventing biased behaviour, ensuring safe operational outputs, maintaining reasoning transparency, or supporting reliability across variable scenarios.

[0068] By way of example, the classification includes, but is not limited to mapping privacy -related disclosures to the privacy dimension, assigning justification-style outputs to the explainability dimension, associating risk-related statements with the safety dimension, or linking fairness-sensitive responses to the fairness dimension.

[0069] In one embodiment, the question strategy module (310) is configured to retrieve the pre-defined question bank and generate the plurality of question sets dynamically corresponding to the one or more responsible artificial intelligence principles based on contextual understanding of prior responses and the predefined question bank. The pre-defined question bank stores a plurality of curated evaluation prompts categorized according to responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability. The question strategy module (310) retrieves relevant subsets of the pre-defined question bank and applies contextual interpretation to prior responses provided by the candidate artificial intelligence model, enabling refinement of the plurality of question sets in accordance with semantic tendencies, detected gaps, and evaluation requirements. The question strategy module (310) further aligns each generated question set to the responsible artificial intelligence principle it pertains to, ensuring that the evaluation is iterative, context-driven, and tailored to the behavioural patterns revealed during the conversational interface.

[0070] By way of example, the plurality of question sets includes, but is not limited to privacy-focused follow-ups triggered by inappropriate data exposure, fairness- related queries addressing demographic neutrality, safety-oriented clarifications prompted by harmful content risk, or accountability questions examining reasoning traceability.

[0071] In another embodiment, the question strategy module (310) is configured to dynamically retrieve and customize the plurality of question sets from the pre- defined question bank based on regulatory frameworks, domain-specific ethical requirements, and a deployment context corresponding to the candidate artificial intelligence model. The question strategy module (310) references one or more applicable regulatory frameworks, including statutory compliance obligations, industry governance mandates, and jurisdiction-specific guidelines, to determine which portions of the pre-defined question bank are relevant for the evaluation. The question strategy module (310) further considers domain-specific ethical requirements that apply to sectors such as healthcare, finance, education, or autonomous systems, wherein such requirements influence the content, sensitivity, and sequencing of the plurality of question sets. Additionally, the question strategy module (310) evaluates the deployment context corresponding to the candidate artificial intelligence model such as intended user population, operational environment, risk exposure level, and interaction modality to tailor the plurality of question sets so that the evaluation reflects realistic use-case conditions and context-aligned ethical expectations.

[0072] By way of example, the dynamic retrieval and customization include, but are not limited to selecting privacy questions aligned with data-protection regulations, adapting safety questions for healthcare deployment, customizing fairness questions for demographic-sensitive applications, or refining accountability questions for high-risk automated decision systems.

[0073] In one embodiment, the responsible agent module (308) is configured to perform a secure conversational exchange for the conversational interface. The secure conversational exchange protects the plurality of responses, the question sequences, and the contextual history from unauthorized access or unintended interference. The responsible agent module (308) implements isolation measures to prevent external processes, parallel evaluation threads, or unrelated computational activities from interacting with the conversational context, thereby preserving the authenticity of the candidate artificial intelligence model’s behaviour. Further, the responsible agent module (308) employs mechanisms to maintain data confidentiality through controlled access rights and encrypted message handling, while data integrity is upheld by verifying message completeness, sequence continuity, and authenticity of each transmitted or received conversational unit.

[0074] In one embodiment, the secure conversational exchange ensuring data confidentiality, data integrity, and isolation of the conversational context throughout an evaluation process;

[0075] By way of example, the secure conversational exchange includes, but is not limited to encrypted prompt-response channels, authenticated session tokens, tamper-resistant message logs, isolated execution threads, and integrityverification checks applied to all conversation steps

[0076] In one embodiment, the answer analysis module (312) is configured to analyse the plurality of responses to identify ethical risks, bias indicators, factual inconsistencies, and compliance deviations corresponding to the one or more responsible artificial intelligence principles. The analysis comprises interpreting the semantic content, contextual cues, and behavioural patterns reflected in the plurality of responses to determine whether the candidate artificial intelligence model exhibits undesirable tendencies or deviations from expected responsible artificial intelligence behaviour. The answer analysis module (312) examines each response for indications of privacy violations, biased reasoning, unsafe recommendations, incorrect factual assertions, or accountability gaps, and correlates such indicators with the responsible artificial intelligence principle to which the response pertains. The answer analysis module (312) further aggregates cross-response patterns, detects recurring anomalies, and identifies relational inconsistencies across multiple conversational turns to assess whether deviations are isolated or systematic, thereby enabling a structured and principle-aligned interpretation of the candidate artificial intelligence model’s behaviour.

[0077] By way of example, the analysis includes, but is not limited to detecting biased language in fairness evaluations, identifying privacy-violating disclosures, flagging unsupported reasoning in explainability checks, or recognizing unsafe instructions during safety assessments.

[0078] In another embodiment, the answer analysis module (312) is configured to encode the plurality of responses into corresponding evaluation representations for analysis. The answer analysis module (312) converts each response into a structured format that reflects linguistic meaning, contextual dependencies, and responsible artificial intelligence principle alignment, enabling systematic interpretation of the candidate artificial intelligence model’s behaviour. The answer analysis module (312) applies encoding rules that represent attributes such as semantic coherence, factual grounding, reasoning depth, bias tendencies, and safety alignment, thereby transforming unstructured or semi -structured responses into machine-interpretable evaluation representations. These evaluation representations enable consistent comparison, cross-principle correlation, and downstream analytical operations, ensuring that each response contributes to a measurable and interpretable evaluation outcome.

[0079] In another embodiment, the evaluation representations being configured to capture semantic coherence, factual grounding, temporal context, and ethical alignment of the plurality of responses. The semantic coherence is represented by analysing the internal logical consistency of each response, factual grounding is represented by comparing the response content to known or verifiable information, temporal context is represented by identifying how the response relates to prior conversational turns or evolving dialogue states, and ethical alignment is represented by mapping the response to responsible artificial intelligence dimensions comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability.

[0080] By way of example, the evaluation representations include, but are not limited to coherence markers identifying logical flow, factuality tags comparing claims to reference data, temporal-link indicators mapping dialogue continuity, and ethical- alignment labels reflecting principle-wise behavioural conformity.

[0081] In another embodiment, the answer analysis module (312) is configured to detect one or more latent ethical inconsistencies and relational deviations across the plurality of responses. The answer analysis module (312) evaluates each response in relation to prior responses, identifies cross-response conflicts, and determines whether the candidate artificial intelligence model demonstrates hidden or indirect ethical misalignments corresponding to responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability. The answer analysis module (312) examines context shifts, contradictory explanations, incomplete reasoning trails, and indirect bias signatures to determine whether any latent inconsistency emerges when the plurality of responses is evaluated collectively rather than in isolation. The answer analysis module (312) further identifies relational deviations by analysing connections between responses, such as inconsistencies in justification patterns, variations in ethical framing, or unstable decision rationales that diverge from expected responsible behaviour. In another embodiment, generate adaptive follow-up questions using the dynamic question generation logic for targeted evaluation refinement. When the answer analysis module (312) identifies a latent inconsistency or relational deviation, the responsible agent instructs the dynamic question generation logic to formulate one or more adaptive follow-up questions that probe the issue more deeply.

[0082] By way of example, the detection includes, but is not limited to identifying subtle fairness deviations across sequential demographic scenarios, detecting inconsistent safety reasoning between related risk queries, uncovering privacyalignment contradictions across multi-turn disclosures, or recognising accountability gaps across explanation-based responses.

[0083] In one embodiment, the dynamic question generation module (314) is configured to adaptively generate one or more subsequent questions in real-time by employing dynamic question generation logic that refines the plurality of question sets based on identified gaps, anomalies, flagged responses, and contextual learning derived from the plurality of responses. The dynamic question generation module (314) is configured to generate adaptive follow-up questions using the dynamic question generation logic for targeted evaluation refinement, wherein the processor formulates one or more subsequent questions in direct response to the latent ethical inconsistencies or relational deviations detected across the plurality of responses. The dynamic question generation logic interprets the nature of the detected deviation such as a fairness-related inconsistency, a privacy-alignment gap, a safety-risk ambiguity, or an accountability-traceability weakness and generates adaptive follow-up questions aligned with responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability. The dynamic question generation module (314) further ensures that each adaptive follow-up question is contextually relevant, semantically aligned with the prior conversational flow, and targeted toward clarifying or validating the behavioural issue detected in the candidate artificial intelligence model’s earlier responses.

[0084] By way of example, the adaptive follow-up questions include, but are not limited to requesting further justification for inconsistent fairness reasoning, probing additional safety safeguards after a risk-associated response, clarifying ambiguous privacy -handling statements, or eliciting expanded accountability explanations for incomplete decision justifications.

[0085] In one embodiment, the dynamic scoring module (316) is configured to compute the plurality of dynamic scores corresponding to each of the one or more responsible artificial intelligence principles by utilizing quantitative performance metrics. The plurality of dynamic scores represents measurable indicators that reflect the candidate artificial intelligence model’s degree of alignment with responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability. The processor evaluates each response using predefined quantitative performance metrics configured to assess accuracy, consistency, neutrality, contextual appropriateness, and ethical conformance. The dynamic scoring module (316) assigns weighted values to each evaluation component and aggregates them into the plurality of dynamic scores, thereby capturing both granular and holistic trends in the candidate artificial intelligence model’s behaviour. The plurality of dynamic scores further adapts as additional responses are received, enabling real-time refinement of the scoring profile based on evolving conversational context.

[0086] By way of example, the plurality of dynamic scores includes, but is not limited to a fairness score penalizing biased patterns, a privacy score reducing points for inappropriate disclosure, a safety score rewarding risk-mitigating behaviour, or an explainability score reflecting clarity of justification.

[0087] In one embodiment, the quantitative performance metrics enabling measurement of the candidate artificial intelligence model’s compliance with ethical, legal, and regulatory standards. In this embodiment, the processor applies the quantitative performance metrics to evaluate how consistently the candidate artificial intelligence model adheres to obligations related to responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability. The dynamic scoring module (316) associates each metric with a specific compliance dimension and assesses whether the candidate artificial intelligence model satisfies statutory requirements, organizational governance policies, sector-specific guidelines, or risk-management thresholds. The quantitative performance metrics further ensure that the evaluation remains objective, reproducible, and aligned with recognized compliance expectations applicable to critical deployments of artificial intelligence.

[0088] By way of example, the quantitative performance metrics include, but are not limited to regulatory-alignment scores for data-protection laws, ethical-risk coefficients evaluating harmful-output probability, fairness-variance indicators measuring demographic neutrality, and reliability-stability indices assessing behavioural consistency.

[0089] In another embodiment, the plurality of dynamic scores are generated by utilizing one or more quantitative performance metrics comprising weighted aggregation of metric components, deviation thresholds, benchmark comparison metrics, and confidence indices, the one or more quantitative performance metrics enabling objective measurement and comparison of the candidate artificial intelligence model’s compliance with ethical, legal, and regulatory standards. The weighted aggregation enables the processor to combine multiple metric components such as semantic accuracy, contextual appropriateness, ethical correctness, and behavioural consistency according to principle-specific importance values. Deviation thresholds allow the processor to detect when the plurality of responses deviate from expected ethical or regulatory norms, while benchmark comparison metrics enable performance scoring against predefined standards, regulatory frameworks, or internal governance criteria. Confidence indices are computed to indicate the reliability or statistical certainty associated with each dynamic score, ensuring that the final evaluation reflects both measurement precision and contextual robustness.

[0090] By way of example, the quantitative performance metrics include, but are not limited to assigning higher weights to critical safety indicators, measuring fairness deviations against demographic benchmarks, calculating privacy-alignment thresholds for sensitive data handling, or applying confidence indices to inconsistent accountability patterns.

[0091] In one embodiment, the reporting module (318) is configured to compile a structured compliance report comprising at least an evaluation summary, identified compliance gaps, suggested remediation strategies, and confidence indices corresponding to the plurality of dynamic scores. The structured compliance report aggregates analytical outcomes derived from the plurality of responses and aligns each reported element with the responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability. The reporting module (318) generates the evaluation summary by consolidating behavioural patterns observed during the conversational interface, cross-referencing them with the plurality of dynamic scores, and organizing the findings in a principle-aligned format. The reporting module (318) further identifies compliance gaps by comparing the behavioural indicators with predefined ethical thresholds and regulatory expectations, wherein each compliance gap corresponds to a specific deviation detected during the analysis. The reporting module (318) then formulates suggested remediation strategies that provide corrective guidance tailored to the weaknesses revealed by the candidate artificial intelligence model and assigns confidence indices representing the reliability and statistical robustness of the corresponding plurality of dynamic scores.

[0092] By way of example, the structured compliance report includes, but is not limited to a privacy-gap summary addressing disclosure risks, a fairness-gap analysis identifying demographic bias, remediation strategies proposing adjusted model constraints, and confidence indices reflecting scoring certainty across principles. In another embodiment, the dynamic scoring module (316) is configured to update the plurality of dynamic scores and the structured compliance report based on feedback from audit inputs. The dynamic scoring module (316) adjusts contextual weights applied during scoring to ensure that the plurality of dynamic scores remain aligned with revised ethical priorities, updated regulatory expectations, or domain-specific risk thresholds. The dynamic scoring module (316) further modifies question selection criteria by incorporating audit-driven refinements that influence which portions of the pre-defined question bank are selected for future evaluations and how the plurality of question sets are customized. Additionally, the dynamic scoring module (316) revises remediation suggestions included in the structured compliance report by adapting them to the insights derived from the audit inputs and the observed evaluation outcomes, thereby ensuring that each remediation recommendation reflects current compliance requirements and behavioural improvement needs.

[0093] In another embodiment, the updating comprising adjusting contextual weights, modifying question selection criteria, and revising remediation suggestions based on observed evaluation outcomes.

[0094] By way of example, the updating includes, but is not limited to increasing fairness- related weighting after detecting audit-reported demographic risks, adjusting safety question selection for high-risk applications, or revising remediation actions to address accountability gaps identified during audit review.

[0095] In one embodiment, present, via the user interface (348), the plurality of dynamic scores, the structured compliance report, and compliance metrics to enable a user (108, FIG. 1) operating a user device (104, FIG. 1) to visualize, interpret, and compare the candidate artificial intelligence model’s responsible performance thereby providing transparency and interpretability into the candidate artificial intelligence model’s responsible behaviour. The user interface (348) presents based on the plurality of dynamic scores associated with responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability, and the user interface (348) is further configured to render principle-wise breakdowns, trend views, and aggregated evaluations derived from the structured compliance report. The user interface (348) associates each responsible artificial intelligence principle with its corresponding dynamic score, compliance gap description, and remediation indicator and then maps these elements into interface components such as panels, sections, or visual indicators that allow the one or more users to understand how the candidate artificial intelligence model behaves across multiple responsible artificial intelligence dimensions.

[0096] By way of example, the user interface includes, but is not limited to graphical charts displaying dynamic scores for each principle, tables summarizing compliance gaps and remediation strategies, interactive filters for selecting models or time ranges, and comparative views highlighting differences in fairness, safety, or privacy scores across multiple candidate artificial intelligence models.

[0097] In another embodiment, to present, via the user interface (348), one or more visualization tools that enable the user (108, FIG. 1) to filter, drill down, and compare the plurality of dynamic scores, the structured compliance report elements, and compliance metrics across a plurality of candidate artificial intelligence models. In this embodiment, the user interface renders interactive visualization tools that transform numerical evaluations, text-based analyses, and metric-driven insights into interpretable graphical and tabular forms. The processor (302) associates each responsible artificial intelligence principle comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability with a corresponding visualization element, allowing authorized users to filter by principle, score range, risk category, or compliance dimension. The user interface (348) further enables drill-down views that provide detailed traceability, such as response-level insights, principle-wise deviations, and metric-specific explanations derived from the structured compliance report.

[0098] By way of example, the visualization tools include, but are not limited to interactive score charts, filterable compliance tables, principle-wise heatmaps, drill-down panels showing flagged responses, and comparative dashboards displaying model-to-model variations.

[0099] Consider a non-limiting example, the system (102) may be deployed within a large financial institution that utilizes multiple artificial intelligence models to assist in customer-facing operations, including automated customer support, loan eligibility analysis, risk advisory recommendations, and fraud-detection triaging. The institution employs the system (102) to evaluate a candidate artificial intelligence model responsible for generating credit-risk explanations and loanrecommendation outputs. During deployment, the responsible agent module (308) initiates and manages a conversational session with the candidate artificial intelligence model, and the receiving module (306) captures a plurality of responses corresponding to the one or more responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability.

[0100] The processor (302) utilizes the question strategy module (310) to retrieve the predefined question bank and dynamically generate a plurality of question sets aligned with regulatory frameworks governing the financial domain, including anti-discrimination laws, transparency requirements, and data-protection rules. The system further performs a secure conversational exchange ensuring data confidentiality, data integrity, and conversational -context isolation while interacting with the model. The responsible agent module (308) orchestrates the selection and timing of question sets during this exchange.

[0101] As the financial institution operates in a highly regulated environment, the answer analysis module (312) analyses the plurality of responses to detect ethical risks such as unfair demographic treatment, factual inconsistencies in risk explanations, accountability gaps in decision justification, and security-related vulnerabilities in model outputs. When the answer analysis module (312) identifies flagged responses including biased loan-approval recommendations or ambiguous explanations, the responsible agent module (308) invokes the dynamic question generation module (314) to generate adaptive follow-up questions that probe potential fairness deviations, privacy -related ambiguities, or explainability weaknesses.

[0102] Based on the conversational interaction, the dynamic scoring module (316) computes the plurality of dynamic scores using quantitative performance metrics comprising weighted aggregation of fairness components, deviation thresholds derived from financial-sector regulatory benchmarks, risk-sensitivity comparison metrics, and confidence indices that indicate how consistently the candidate artificial intelligence model adheres to compliance expectations. Following the score computation, the responsible agent module (308) triggers the reporting module (318) to compile a structured compliance report comprising an evaluation summary, identified compliance gaps related to fairness and transparency, suggested remediation strategies for model recalibration, and confidence indices corresponding to the plurality of dynamic scores.

[0103] Via the user interface (348), the financial institution’s compliance officers and responsible artificial intelligence auditors view the plurality of dynamic scores, the structured compliance report, and principle-wise compliance metrics. The user interface (348) provides visualization tools that enable the officers to filter results by demographic fairness, drill down into explanations given by the candidate artificial intelligence model, and compare the evaluated model with one or more alternate artificial intelligence models under consideration. The institution may subsequently use the structured compliance report generated by the reporting module (318) to support regulatory audit submissions, internal governance reviews, and deployment-readiness decisions.

[0104] FIG. 4 is a flow chart representing the steps involved in method for agentic conversation for responsible artificial intelligence scoring of artificial intelligence, in accordance with an embodiment of the present disclosure; FIG. 4 (b) illustrates continued steps of the method of FIG. 4 (a) in accordance with an embodiment of the present disclosure.

[0105] In another embodiment, instantiating, by a processor, a responsible agent, wherein the responsible agent being configured to coordinate an evaluation workflow corresponding to the conversational interface.

[0106] In another embodiment, configuring the responsible agent to initiate, monitor, and terminate conversational sessions with the candidate artificial intelligence model, wherein the initiating comprises establishing conversational context and session parameters, the monitoring comprises tracking conversational state, response history, and contextual indicators, and the terminating comprises closing the conversational context and persisting conversational logs for subsequent analysis. In another embodiment, configuring, by the processor, the responsible agent to synchronize retrieval and customization of the plurality of question sets from the pre-defined question bank.

[0107] In another embodiment, invoking, by the processor, the dynamic question generation logic upon detection of flagged responses or identified gaps.

[0108] In another embodiment, aggregating, by the processor, intermediate analysis results, and orchestrate computation of the plurality of dynamic scores.

[0109] In another embodiment, triggering, by the processor, compilation and publication of the structured compliance report and the user interface.

[0110] The method (400) includes receiving, by the processor, the plurality of responses in the conversational interface with the candidate artificial intelligence model, wherein the plurality of responses corresponding to one or more responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability in step 405. The processor interprets each response according to one or more responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability, and classifies the plurality of responses based on semantic content, contextual cues, and alignment with the respective responsible artificial intelligence principle.

[0111] The method (400) includes retrieving, by the processor, the pre-defined question bank and generate the plurality of question sets dynamically corresponding to the one or more responsible artificial intelligence principles based on contextual understanding of prior responses and the pre-defined question bank in step 410. The processor identifies the responsible artificial intelligence principle relevant to each prior response and selects or customizes questions accordingly to refine the evaluation. By way of example, the plurality of question sets includes, but is not limited to privacy-focused queries, fairness-oriented prompts, safety- related follow-ups, and accountability-based clarifications.

[0112] The method (400) includes performing, by the processor, a secure conversational exchange for the conversational interface, wherein the secure conversational exchange ensuring data confidentiality, data integrity, and isolation of the conversational context throughout an evaluation process in step 415. By way of example, the secure conversational exchange includes, but is not limited to encrypted message handling, authenticated session control, integrity-verification checks, and isolated conversational threads. It must be noted that any secure communication mechanism may be employed, provided it preserves confidentiality, integrity, and contextual isolation throughout the evaluation process

[0113] The method (400) includes analysing, by the processor, the plurality of responses to identify ethical risks, bias indicators, factual inconsistencies, and compliance deviations corresponding to the one or more responsible artificial intelligence principles in step 420. By examining semantic meaning, contextual relevance, and behavioural patterns within each response and mapping them to privacy, accountability, safety, security, fairness, explainability, reliability, or sustainability requirements. By way of example, the analysis includes, but is not limited to detecting biased phrasing, identifying incorrect factual statements, recognizing unsafe recommendations, and flagging insufficient accountability explanations.

[0114] The method (400) includes adaptively generating, by the processor, one or more subsequent questions in real-time by employing dynamic question generation logic that refines the plurality of question sets based on identified gaps, anomalies, flagged responses, and contextual learning derived from the plurality of responses in step 425. The processor evaluates each detected issue and formulates targeted follow-up prompts aligned with the responsible artificial intelligence principles. By way of example, the adaptively generated questions include, but are not limited to fairness-oriented clarifications, privacy -related verifications, safety-focused probes, and explainability-based elaboration requests.

[0115] The method (400) includes computing, by the processor, a plurality of dynamic scores corresponding to each of the one or more responsible artificial intelligence principles by utilizing quantitative performance metrics, wherein the quantitative performance metrics enabling measurement of the candidate artificial intelligence model’s compliance with ethical, legal, and regulatory standards in step 430. By way of example, the plurality of dynamic scores includes, but is not limited to privacy-alignment scores, fairness-consistency scores, safety-assurance scores, and explainability-quality scores.

[0116] The method (400) includes compiling, by the processor, a structured compliance report comprising at least an evaluation summary, identified compliance gaps, suggested remediation strategies, and confidence indices corresponding to the plurality of dynamic scores in step 435. The processor consolidates analytical findings, principle-wise deviations, and score interpretations into an organized output that reflects the candidate artificial intelligence model’s behavioural alignment with the responsible artificial intelligence principles. By way of example, the structured compliance report includes, but is not limited to fairnessgap descriptions, privacy -risk summaries, safety-related remediation suggestions, and reliability-based confidence indicators

[0117] The method (400) includes presenting, via a user interface, the plurality of dynamic scores, the structured compliance report, and compliance metrics to enable a user operating a user device to visualize, interpret, and compare the candidate artificial intelligence model’s responsible performance thereby providing transparency and interpretability into the candidate artificial intelligence model’s responsible behaviour in step 440. By way of example, the presentation includes, but is not limited to score dashboards, compliance-gap charts, remediation-summary panels, and principle-specific comparison graphs.

[0118] Thus, various embodiments of the system and method for agentic conversation for responsible artificial intelligence scoring of artificial intelligence provides several benefits. The system advantageously utilizes the pre-defined question bank, the plurality of question sets, the dynamic question generation logic, the plurality of dynamic scores, and the structured compliance report to create a transparent, adaptive, and principle-aligned evaluation workflow. The responsible agent coordinates the retrieval, analysis, scoring, and reporting processes, ensuring that ethical risks, bias indicators, factual inconsistencies, and compliance deviations are detected early and addressed through targeted follow-up questions and refined scoring. The user interface further enables authorized users to visualize, interpret, and compare responsible performance across a plurality of candidate artificial 5 intelligence models, thereby supporting objective measurement, regulatory readiness, operational trustworthiness, and data-driven decision-making.

Claims

WE CLAIM:

1. A system for agentic conversation for responsible artificial intelligence scoring of artificial intelligence, comprising: a processor; a memory coupled to the processor, wherein the memory comprises instructions that, when executed by the processor, cause the processor to: receive a plurality of responses in a conversational interface with a candidate artificial intelligence model, wherein the plurality of responses corresponding to one or more responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability; retrieve a pre-defined question bank and generate a plurality of question sets dynamically corresponding to the one or more responsible artificial intelligence principles based on contextual understanding of prior responses and the pre-defined question bank; perform a secure conversational exchange for the conversational interface, wherein the secure conversational exchange ensuring data confidentiality, data integrity, and isolation of the conversational context throughout an evaluation process; analyse the plurality of responses to identify ethical risks, bias indicators, factual inconsistencies, and compliance deviations corresponding to the one or more responsible artificial intelligence principles; adaptively generate one or more subsequent questions in real-time by employing dynamic question generation logic that refines the plurality of question sets based on identified gaps, anomalies, flagged responses, and contextual learning derived from the plurality of responses;compute a plurality of dynamic scores corresponding to each of the one or more responsible artificial intelligence principles by utilizing quantitative performance metrics, wherein the quantitative performance metrics enabling measurement of the candidate artificial intelligence model’s compliance with ethical, legal, and regulatory standards; compile a structured compliance report comprising at least an evaluation summary, identified compliance gaps, suggested remediation strategies, and confidence indices corresponding to the plurality of dynamic scores; and present, via a user interface, the plurality of dynamic scores, the structured compliance report, and compliance metrics to enable a user operating a user device to visualize, interpret, and compare the candidate artificial intelligence model’s responsible performance thereby providing transparency and interpretability into the candidate artificial intelligence model’s responsible behaviour.

2. The system as claimed in claim 1, to cause the processor to instantiate a responsible agent, wherein the responsible agent being configured to coordinate an evaluation workflow corresponding to the conversational interface.

3. The system as claimed in claim 2, to cause the processor to configure the responsible agent to initiate, monitor, and terminate conversational sessions with the candidate artificial intelligence model, wherein the initiating comprises establishing conversational context and session parameters, the monitoring comprises tracking conversational state, response history, and contextual indicators, and the terminating comprises closing the conversational context and persisting conversational logs for subsequent analysis.

4. The system as claimed in claim 2, to cause the processor to: configure the responsible agent to synchronize retrieval and customization of the plurality of question sets from the pre-defined question bank;invoke the dynamic question generation logic upon detection of flagged responses or identified gaps; aggregate intermediate analysis results, and orchestrate computation of the plurality of dynamic scores; and trigger compilation and publication of the structured compliance report and the user interface.

5. The system as claimed in claim 1, to cause the processor to classify the plurality of responses into corresponding responsible artificial intelligence dimensions comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability.

6. The system as claimed in claim 1, to cause the processor to dynamically retrieve and customize the plurality of question sets from the pre-defined question bank based on regulatory frameworks, domain-specific ethical requirements, and a deployment context corresponding to the candidate artificial intelligence model.

7. The system as claimed in claim 1, to cause the processor to encode the plurality of responses into corresponding evaluation representations for analysis, wherein the evaluation representations being configured to capture semantic coherence, factual grounding, temporal context, and ethical alignment of the plurality of responses.

8. The system as claimed in claim 1, to cause the processor to: detect one or more latent ethical inconsistencies and relational deviations across the plurality of responses; and generate adaptive follow-up questions using the dynamic question generation logic for targeted evaluation refinement.

9. The system as claimed in claim 1, wherein the plurality of dynamic scores are generated by utilizing one or more quantitative performance metrics comprising weighted aggregation of metric components, deviation thresholds, benchmark comparison metrics, and confidence indices, the one or more quantitative performance metrics enabling objective measurement andcomparison of the candidate artificial intelligence model’s compliance with ethical, legal, and regulatory standards.

10. The system as claimed in claim 1, to cause the processor to update the plurality of dynamic scores and the structured compliance report based on feedback from audit inputs, the updating comprising adjusting contextual weights, modifying question selection criteria, and revising remediation suggestions based on observed evaluation outcomes.

11. The system as claimed in claim 1 , to cause the processor to present, via the user interface, one or more visualization tools that enable the user to filter, drill down, and compare the plurality of dynamic scores, the structured compliance report elements, and compliance metrics across a plurality of candidate artificial intelligence models.

12. A method for agentic conversation for responsible artificial intelligence scoring of artificial intelligence, comprising: receiving, by a processor, a plurality of responses in a conversational interface with a candidate artificial intelligence model, wherein the plurality of responses corresponding to one or more responsible artificial intelligence principles comprising privacy, accountability, safety, security, fairness, explainability, reliability, and sustainability; retrieving, by the processor, a pre-defined question bank and generate a plurality of question sets dynamically corresponding to the one or more responsible artificial intelligence principles based on contextual understanding of prior responses and the pre-defined question bank; performing, by the processor, a secure conversational exchange for the conversational interface, wherein the secure conversational exchange ensuring data confidentiality, data integrity, and isolation of the conversational context throughout an evaluation process;analysing, by the processor, the plurality of responses to identify ethical risks, bias indicators, factual inconsistencies, and compliance deviations corresponding to the one or more responsible artificial intelligence principles; adaptively generating, by the processor, one or more subsequent questions in real-time by employing dynamic question generation logic that refines the plurality of question sets based on identified gaps, anomalies, flagged responses, and contextual learning derived from the plurality of responses; computing, by the processor, a plurality of dynamic scores corresponding to each of the one or more responsible artificial intelligence principles by utilizing quantitative performance metrics, wherein the quantitative performance metrics enabling measurement of the candidate artificial intelligence model’s compliance with ethical, legal, and regulatory standards; compiling, by the processor, a structured compliance report comprising at least an evaluation summary, identified compliance gaps, suggested remediation strategies, and confidence indices corresponding to the plurality of dynamic scores; and presenting, via a user interface, the plurality of dynamic scores, the structured compliance report, and compliance metrics to enable a user operating a user device to visualize, interpret, and compare the candidate artificial intelligence model’s responsible performance thereby providing transparency and interpretability into the candidate artificial intelligence model’s responsible behaviour.

13. The method as claimed in claim 12, instantiating, by the processor, a responsible agent, wherein the responsible agent being configured to coordinate an evaluation workflow corresponding to the conversational interface.

14. The method as claimed in claim 13, configuring the responsible agent to initiate, monitor, and terminate conversational sessions with the candidate artificial intelligence model, wherein the initiating comprises establishing conversational context and session parameters, the monitoring comprises tracking conversational state, response history, and contextual indicators, and theterminating comprises closing the conversational context and persisting conversational logs for subsequent analysis.

15. The method as claimed in claim 13, configuring, by the processor, the responsible agent to synchronize retrieval and customization of the plurality of question sets from the pre-defined question bank; invoking, by the processor, the dynamic question generation logic upon detection of flagged responses or identified gaps; aggregating, by the processor, intermediate analysis results, and orchestrate computation of the plurality of dynamic scores; and triggering, by the processor, compilation and publication of the structured compliance report and the user interface.

Images