Information Retrieval System with User-Selected ML Models
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Solution Overview
Problem
Existing information retrieval systems using multiple machine learning models lack user control and transparency, often selecting models without user input and hiding AI usage, leading to confusing and unsatisfactory results for users.
Innovation Solution
An information retrieval system that allows users to select and provide feedback on machine learning models, storing this feedback for model training and customization, enabling personalized results and increased transparency through a graphical user interface and feedback database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models are used for information retrieval, then retrieval accuracy and adaptability are improved, but system complexity and lack of user control increase
Solution Approach 1:
The system segments the machine learning model selection process into distinct components: a plurality of available ML models, a selection mechanism that presents models to users, and a user feedback interface. This segmentation allows users to interact with and control the complex system without being overwhelmed by its full complexity.
Solution Approach 2:
The system introduces an intermediary selection mechanism between the complex ML models and the user. This intermediary presents simplified model options to users, receives their selections and feedback, and translates these into appropriate model choices, thereby reducing the perceived complexity for users while maintaining access to multiple sophisticated models.
2Extent of automation
If machine learning models are selected automatically without user input, then system automation is improved, but user control and transparency deteriorate
Solution Approach 1:
The system implements dynamic model selection where the degree of automation adjusts based on user input. Initially, the system can operate with automatic selection, but as users provide feedback and preferences, the system dynamically adapts to incorporate user control, allowing the automation level to flex between fully automatic and user-directed modes.
Solution Approach 2:
The system incorporates feedback mechanisms where user selections and responses about retrieved information are captured and used to refine future model selections. This feedback loop maintains automation by learning from user preferences while simultaneously improving user control as the system adapts to individual user needs over time.
3Device complexity
If AI usage is hidden from users, then system simplicity is maintained, but transparency and user trust decrease
Solution Approach 1:
The system uses visual indicators (analogous to color changes) to signal when and how AI models are being applied. Through the user interface, the system presents information about which ML models are available, which are selected, and how they contribute to retrieval results, making the previously hidden AI processes visible and understandable to users.
Solution Approach 2:
The system introduces an intermediary information layer between the AI processing and the user that explains AI usage without exposing underlying complexity. This intermediary provides transparent information about model selection and AI involvement in a simplified format that maintains user understanding while preserving system simplicity.
4Measurement precision
If user feedback is collected and stored for model training, then model performance is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system performs preliminary organization and structuring of user feedback data as it is collected, preparing it in advance for model training processes. By pre-processing and categorizing feedback information during user interaction rather than attempting to process raw data later, the system reduces the complexity of subsequent data processing while maintaining the quality and utility of training data.
Data Source
AI summary
An information retrieval system is provided. The system comprises: a client device, a server device connected to the client device, the server device being configured to retrieve information from a data storage, an artificial intelligence, AI, component, in communication with the client device, and the server device, and a feedback database accessible by the AI component, wherein the server device is configured to: receiving an input from a user of the client device, select at least one machine learning model, based at least in part on the input from the user, from among a plurality of machine learning models that are available for retrieving information from the data storage, retrieve information from the data storage using the at least one machine learning model, and provide at least a part of the retrieved information to the client device, and wherein the AI component is configured to: provide, for display at the client device, a graphical user interface, GUI, for the user to input user feedback on the retrieved information; receive the user feedback via the GUI, and store, in the feedback database, the received user feedback in association with the user and with the at least one machine learning model used for retrieving the information.