Prometheus-X Components & Services

AffectLog’s Trustworthy AI (ALT-AI) - Design Document

The AffectLog’s Trustworthy AI (ALT-AI) provides tools for the explanation and visualization of machine learning models, enabling data scientists and stakeholders to interpret model predictions and understand feature importance. ALT-AI integrates with popular machine learning frameworks, handling a range of models within its supported capabilities.

Technical Usage Scenarios & Features

Features/Main Functionalities

• Model Explanation: ALT-AI generates explanations for model predictions, offering insights into how different factors contribute to specific outcomes.
• Feature Importance: It quantifies the contribution of each feature to the model’s predictions, helping users understand which features are most influential.

Technical Usage Scenarios

• Model Development and Validation: Data scientists can use ALT-AI to interpret and validate model decisions within the limitations of the supported model types.
• Audit and Compliance: ALT-AI aids in providing explanations for model decisions, which can be useful in ensuring transparency and compliance with regulatory requirements.
• Feature Engineering: Insights into feature importance can help refine features to potentially improve model performance.

Requirements

• R1: ALT-AI must support integration with Python-based machine learning libraries like scikit-learn.
• R2: ALT-AI must provide simple APIs for generating model explanations and feature importance scores.
• R3: ALT-AI must ensure the privacy and security of data used during the explanation process.
• R4: ALT-AI should be optimized for small to medium-sized datasets to ensure efficient processing within typical infrastructure constraints.

Integrations

Direct Integrations with Other BBs

• Decentralized AI Training BB (#1): ALT-AI integrates with training components to retrieve trained models for analysis.
• Data Value Chain Tracker BB (#6): Integrates with the data value chain tracker to ensure consistent data transformations during the explanation process.

Integrations via Connector

• Distributed Data Visualization BB (#7): ALT-AI can be linked with visualization components to enhance the graphical representation of explanation outputs.

Relevant Standards

• Data Format Standards: Supports common formats like JSON and CSV for input and output to ensure compatibility with other systems.
• Data Cards: ALT-AI adheres to practices for providing metadata about datasets, enhancing transparency.
• Model Cards: ALT-AI supports the documentation of machine learning models, outlining their intended use and performance.
• Security and Privacy Standards: Ensures that data handling complies with relevant privacy and security regulations.

Mapping to Data Space Reference Architecture Models

• DSCC:
  • Data Interoperability: Data Exchange
  • Data Sovereignty and Trust: Trust Framework

Input / Output Data

• Input Data: Models and datasets should be provided in supported formats like Python DataFrames.
• Output Data: Generates explanation results, including feature importance, in JSON format.

Architecture

ALT-AI includes the following components:

• Model Adapter: Converts machine learning models into a standardized format for analysis.
• Explanation Generator: Core component responsible for generating explanations and feature importance scores.
• Results Processor: Formats and organizes output for clear visualization and interpretation.
• Security Layer: Ensures data is handled securely throughout the process.

Dynamic Behaviour

Sequence Diagram for Generating Model Explanations:

1. User Submission: The user submits a model and dataset to the ModelAdapter.
2. Model Adaptation: The ModelAdapter standardizes the model for analysis by the Explanation Generator.
3. Data Security: The Explanation Generator ensures data security through the Security Layer.
4. Explanation Generation: The Explanation Generator produces explanations and feature importance scores.
5. Result Processing: The Results Processor formats the output.
6. User Feedback: The formatted results are provided to the user.

Configuration and Deployment Settings

ALT-AI allows for basic configuration options, such as selecting model types and explanation methods. Logging tracks processes, errors, and performance metrics. Usage limits are set based on the complexity of models and dataset size, with a focus on ensuring efficiency and accuracy.

Third-Party Components & Licenses

• Pandas/Numpy: Used for data manipulation, licensed under BSD-3-Clause.
• Scikit-learn: Integrated for model processing, licensed under BSD-3-Clause.

Implementation Details

Implementation is focused on creating a secure and efficient architecture deployable within standard infrastructure. ALT-AI is optimized for medium-sized datasets and models, ensuring reliable performance without significant resource strain. Data privacy and security are prioritized to meet compliance requirements.

Partners & Roles

• Prometheus-X Organization: Provides governance and framework for ALT-AI development.
• AffectLog: Leads the development and integration of ALT-AI tools within Prometheus-X.
• Data Providers: Supply datasets for model training, ensuring data quality and compliance.
• Model Developers: Develop machine learning models for analysis by ALT-AI.
• End Users: Include data scientists and business analysts who use ALT-AI to interpret model outputs.

Usage in the Dataspace

• Focused Analysis: ALT-AI provides clear insights into machine learning models, focusing on specific predictions and feature contributions within the supported scope.
• Data Governance: Prioritizes secure and compliant data handling throughout the analysis process.
• Collaborative Insights: Helps bridge the gap between technical data scientists and business stakeholders by offering straightforward explanations of model outputs.
• Scalability: While optimized for small to medium datasets, ALT-AI ensures that analyses are both efficient and informative.

Example of Dataspace Usage: Skill Gap Analysis

In the skills domain, ALT-AI can be applied to assess and understand skill gaps within an organization:

• Model Explanation: Provides explanations for AI model predictions related to skill gaps, helping HR professionals understand underlying factors, such as insufficient training hours or lack of certifications.
• Feature Contribution: Highlights key factors influencing skill gap predictions, allowing for targeted employee development.
• Visualization: Outputs are presented in a straightforward dashboard, using simple visual elements to communicate insights effectively.

Input Format Example:

• Model Input: CSV file with employee data, including EmployeeID, Department, ExperienceYears, TrainingHours, and Certifications.
• Output Example: A dashboard showing feature importance and explanations for skill gap predictions, utilizing basic visual elements like bar charts or scatter plots.

OpenAPI Specification

An OpenAPI specification for ALT-AI’s RESTful API will be provided in the future, detailing endpoints for submitting models, retrieving results, and managing security.

Test Specification

Test Plan

Testing will include unit, integration, and UI tests (where applicable), using both automated and manual methods to ensure comprehensive coverage.

• Unit Tests: Validate correct model adaptation and accuracy of generated explanations.
• Integration Tests: Ensure smooth operation from model submission to result retrieval.
• UI Test (Where Relevant): Validate usability and correctness of any web interface provided.

Disclaimers

The integration of AffectLog’s ALT-AI into Organizational Skill Gap Analysis, or similar applications, requires careful feasibility studies to assess technical compatibility and resource availability. Discussions with stakeholders are necessary to align on objectives, privacy considerations, and contextual suitability.

This document outlines ALT-AI’s capabilities realistically, ensuring that all deliverables are achievable within the current scope, while maintaining a focus on usability, security, and compliance.

This comprehensive test specification aims to ensure that the ALT-AI operates reliably and efficiently, providing valuable insights into machine learning model behavior while maintaining high standards of usability, security, and compliance.