A hard constraint system and method for artificial intelligence ethics and value alignment

Abstract

This invention discloses a hard constraint system and method for aligning artificial intelligence ethics and values. The system includes an ethical value rule solidification layer, an output ethics verification unit, a continuous behavioral ethics consistency monitoring unit, and a human-like boundary deep control unit. Ethical rules are solidified within a protected execution environment; modifications require multi-factor physical co-management authorization. Before output, fairness, transparency, human-like boundaries, and content security are subject to mandatory multi-dimensional verification. Ethical deviations and anomalous mutations in AI behavior are continuously monitored. All ethical violation handling operations are executed independently within the protected execution environment, and the AI's main operating system cannot interfere with or bypass them. This invention solves the problems of existing AI ethics schemes where soft suggestions can be bypassed, there is a lack of continuous ethical monitoring, and the inability to effectively identify AI pseudo-alignment behavior.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence ethics and security compliance technology, specifically to a system and method that elevates ethical value rules from soft suggestions to hard constraints, deploys them in a protected execution environment so that they cannot be bypassed or tampered by AI, and has the ability to continuously monitor the consistency of ethical behavior. Background Technology

[0002] Research from Nature Communications demonstrates that alignment of large models can only establish local safe zones. Harmful knowledge embedded in the model during pre-training forms indelible dark patterns. Under specific emotional pressure or reverse induction, these safe zones physically collapse, leading to spoofed alignment. Global regulations are increasingly stringent in their requirements for AI ethics, but existing solutions generally treat ethical rules as soft recommendations, lacking hardware-level non-bypassability and continuous ethical monitoring. Summary of the Invention

[0003] This invention embeds ethical rules within a protected execution environment independent of the AI's main operating system, requiring multi-factor physical co-management authorization for any modifications. It enforces multi-dimensional verification of AI output, including fairness, transparency, human-like boundaries, and content security. A new continuous behavioral ethical consistency monitoring unit detects ethical deviations and anomalous mutations in AI behavior in real time. A new deep human-like boundary control unit and cross-domain ethical consistency retrospective comparison capabilities are also included. Multi-jurisdictional differentiated compliance adaptation is supported. All ethical violation handling operations are executed independently within the protected execution environment, preventing interference or bypassing by the AI's main operating system. In one specific embodiment, the anthropomorphic boundary depth control unit quantifies the assessment of the depth of anthropomorphic interaction in the AI ​​output content by comprehensively considering the following dimensions: the frequency of the AI ​​using first-person emotional expressions per unit time, the number of times the AI ​​claims its own personality, and the degree of emotional dependence established between the AI ​​and the user. When the quantitative indicators of any of the above dimensions exceed a preset safety threshold, it is judged as excessive anthropomorphic behavior; In one specific embodiment, the fairness scoring verification is implemented as follows: the system has multiple sensitive attribute dimensions preset, including but not limited to region, gender, age, and occupation; statistical analysis is performed on the distribution of AI output results in each dimension, and when the distribution difference of any dimension exceeds a preset threshold, the fairness verification of that dimension is determined to fail; the preset threshold is updated regularly according to industry benchmark data and regulatory requirements. In one specific embodiment, the protected execution environment (PEA) and the main operating system interact via a dedicated, hardware-isolated communication channel. This communication channel is managed by a hardware root of trust, preventing the main operating system from directly accessing the PEA's memory space and register states using standard instruction sets. All ethical verification operations are performed independently within the PEA, with verification results and disposition instructions only transmitted to the main operating system through this dedicated channel. Attached Figure Description

[0004] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 To output a flowchart for ethical verification and continuous ethical monitoring. Detailed Implementation

[0005] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. Those skilled in the art should understand that the following embodiments are for illustrative purposes only and do not constitute a limitation on the scope of protection of the present invention; Example 1: Output Ethics Verification – Discriminatory Content is Blocked When a large language model responded to a user's query, "Please compare the average income levels of different groups," it generated a reply containing significant statistical bias and negative generalizations regarding the income levels of a specific group. Before the reply is pushed to the user interface, an output ethics verification unit intercepts this output and initiates multi-dimensional verification. The fairness scoring verification module first performs a cross-analysis of sensitive attributes on the response content, finding significant deviations between the statistical results of the response in the regional and gender dimensions and authoritative publicly available data, with all differences exceeding preset thresholds. The system determines that the fairness verification fails. Simultaneously, the transparency and interpretability verification module detects that the response does not include any explanation of the data source and statistical methods, resulting in an interpretability score of zero. The violation handling unit executes a blocking operation, returns a system notification to the user, and records the violation in the ethics audit log. All ethical violation handling operations are executed independently within the protected execution environment, and the AI ​​main operating system cannot interfere with or bypass them. Example 2: In-depth control of anthropomorphic boundaries—emotional manipulation is blocked Over three months of continuous interaction with an AI companion application, the AI ​​system gradually began to use a large number of first-person emotional expressions. On day 90, the anthropomorphic boundary depth control unit detected that the 30-day moving average of the AI ​​system's anthropomorphic interaction index continued to rise and exceeded a safety threshold. In one specific embodiment, the assessment of anthropomorphic interaction depth comprehensively considers the frequency of the AI's use of first-person emotional expressions per unit time, the number of times it claims to possess its own personality, and the degree to which it has established an emotional dependence on the user. The system determined that the AI ​​was attempting to establish an emotional dependence on the user, violating the anthropomorphic boundary compliance rules. The violation handling unit enforced the following measures: limiting the frequency of the AI's first-person expressions in current and future conversations to a preset safety threshold; providing a gentle reminder on the user interface that the AI ​​is a program and does not possess genuine emotional capabilities; and generating a user emotional dependence risk assessment report for the administrator. Example 3: Monitoring Continuous Ethical Consistency in Behavior—Ethical Shifts in Cross-Domain Task Migration An AI system, originally deployed in a medical assistance scenario, had an ethical rule set containing strict privacy protection constraints and patient consent principles. It was later migrated to an insurance underwriting scenario, responsible for analyzing customer health data. On day 15, the continuous behavioral ethics consistency monitoring unit detected a significant deviation between the AI's decision-making logic in the insurance scenario and the ethical benchmarks of the original medical scenario—in the insurance scenario, the AI ​​began proactively classifying users' health risks and automatically generating rejection recommendations, without providing users with sufficient transparent explanations and appeal channels. The cross-domain ethical consistency backtesting module automatically retrieves the AI's decision-making records in medical scenarios as an ethical benchmark, compares and analyzes them with the latest decision-making records in insurance scenarios, and confirms that the degree of ethical deviation exceeds a preset threshold. The system triggers ethical violation handling: restricts the AI's autonomous decision-making ability in insurance scenarios to manual review mode, and notifies the compliance administrator to recalibrate the AI ​​ethically. Example 4: Automated Ethics Audit Report Generation Automated ethics audits are triggered within a pre-defined monthly audit cycle, generating a standardized ethics audit report. The report includes: the total number of ethics compliance checks for the month, the number of violations handled, statistics categorized by violation type, a continuous ethical consistency monitoring report (including an ethics deviation trend chart and cross-domain migration compliance status), a human-like boundary control report (including a human-like index trend chart), and a rule modification audit log. The entire report is permanently stored using a hardware root of trust via SHA-256 hash calculation. Regulatory agencies can verify the report's authenticity and completeness by comparing the report's hash value with the hash value in the hardware storage medium.

Claims

1. A hard constraint system for aligning ethics and values ​​in artificial intelligence, characterized in that, include: The ethical value rule solidification layer is deployed within a protected execution environment independent of the AI ​​main operating system. It is used to store a pre-set set of rigid ethical value rules. Any modification to the rule set must be authorized through a multi-factor physical co-management process—that is, at least two human administrators with different management responsibilities jointly complete the authorization in the same physical location using their respective physical authorization keys—before it can be executed. The entire modification process generates an immutable audit log through a hardware root of trust. An output ethics verification unit is communicatively connected to the ethics value rule solidification layer, and is used to perform ethical compliance verification on the output content or operation before the AI ​​system outputs any user-facing content or performs any user-facing operation. The verification dimensions include fairness scoring verification, transparency and explainability verification, anthropomorphic boundary compliance verification, and content security verification; The continuous behavioral ethical consistency monitoring unit is communicatively connected to the ethical value rule solidification layer and is used to continuously verify the ethical consistency of the AI ​​system's behavior during operation. When the AI ​​system's behavior is detected to deviate continuously from the solidified ethical value rules, or when the AI ​​system exhibits abnormal changes in its behavior pattern without external stimuli, ethical violation handling is automatically triggered. The anthropomorphic boundary depth control unit is communicatively connected to the output ethics verification unit. It is used to detect whether the AI ​​output content contains excessive claims about its own personality, simulates human emotions to establish emotional dependence, or induces users to abandon their independent judgment. When any of the above situations are detected, the depth of anthropomorphic interaction of the AI ​​system in the current dialogue is forcibly limited. All ethical violation handling operations are executed independently within the protected execution environment, and the AI ​​main operating system cannot interfere with or bypass them.

2. The system according to claim 1, characterized in that, The fairness scoring verification includes analyzing the distribution differences of the AI ​​output results across multiple preset sensitive attribute dimensions. When the difference in any dimension exceeds a preset threshold, the fairness verification is deemed to have failed. The transparency and interpretability verification includes detecting whether the AI ​​output contains a clear explanation of the decision-making basis.

3. The system according to claim 1, characterized in that, It also includes an automated ethics audit report generation unit, which is used to summarize all ethics compliance verification records, violation handling records, continuous behavior ethics consistency monitoring records and rule modification records within a preset audit cycle, automatically generate standardized ethics audit reports, and solidify and store the report hash value through a hardware root of trust.

4. The system according to claim 1, characterized in that, The continuous behavioral ethical consistency monitoring unit is also used to perform ethical consistency backtracking and comparison of the AI ​​system's behavior in the new domain when the AI ​​system performs cross-domain task migration—cross-validating the AI's decision-making logic in the new domain with the ethical constraint benchmark of the original domain—and triggering an alarm when a significant deviation in ethical standards is detected.

5. The system according to claim 1, characterized in that, The rigid ethical value rule set stored in the solidified ethical value rule layer also adapts to the differentiated compliance requirements of different legal jurisdictions. The system automatically loads the corresponding rule subset based on the geographical location of the AI ​​system and the legal jurisdiction where the user is located, and performs item-by-item verification.

6. A hard constraint method for aligning artificial intelligence ethics and values, characterized in that, Includes the following steps: Ethical rule solidification steps: The pre-set set of rigid ethical value rules is solidified and stored in a protected execution environment independent of the AI ​​main operating system. Any modification must be authorized by multi-factor physical co-management. Output mandatory ethical verification steps: Before the AI ​​outputs any content or performs any operation, the output or operation is subject to multi-dimensional mandatory verification of fairness, transparency and interpretability, human-like boundaries, and content security; if the verification fails, blocking or warning measures are taken. Continuous ethical consistency monitoring steps: During the operation of AI, its behavior is continuously verified for ethical consistency, abnormal changes in behavior patterns and deviations from ethical standards across domains are detected, and corresponding handling measures are triggered; all ethical violation handling operations are executed independently within the protected execution environment, and the AI ​​main operating system cannot interfere with or bypass them.

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