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Vector AIXpert: Responsible AI Infrastructure for Fairness, Explainability, and Evaluation

Vector Institute's contribution to the AIXpert Project: tools, benchmarks, and research for explainable, accountable, and fair AI.

This project represents the Vector Institute's research contributions to the AIXpert Horizon Europe initiative. It focuses on developing tools, datasets, and evaluation pipelines for fairness-aware generative AI and explainable AI systems.

What we do

Vector's contribution to AIXpert spans four core areas:

  • Explainable & accountable AI — Tools and benchmarks for interpretability, fairness, and transparency in generative and multimodal AI.
  • Trustworthy agentic AI — Transparent, auditable, human-in-the-loop agentic systems with measurable trustworthiness metrics.
  • Multimodal evaluation — Benchmarks and datasets for audio-video understanding, vision-language assessment, and fairness across domains and demographics.
  • Open, reproducible research — Code, datasets, and documentation shared openly to support governance-ready research.

For the full AIXpert vision, consortium, and funding details, see About.

System Architecture

Vector's responsible AI pipeline moves data through five stages — from raw inputs to governed, explainable outputs.

View pipeline

Synthetic Data Generation Fairness-aware multimodal data: images, VQA pairs, text scenes, and video — with demographic metadata and reproducible seeds.
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Multimodal Pipelines Parallel text, vision, video, and audio agents with attribution hooks and Risk-VQA for bias and toxicity detection.
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Agentic AI Evaluation Traceable planning and execution agents with RAG/memory, tool registry, and sandboxed task execution.
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Fairness Metrics + Explainability Statistical parity, equal opportunity, attribution and trace-based diagnostics — with disparity plots and explanation bundles.
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Responsible AI Insights Human-in-the-loop review, signed Governance Log (prompts, tool calls, safety decisions), and final explainable outputs.

Recent Updates

View full list

A snapshot of Vector's key contributions within AIXpert. Each project has its own repository, documentation, and quickstart.

  • UnBias-Plus

    AI-driven toolkit for bias detection and debiasing in text: biased spans, severity, reasoning, neutral replacements, and a full neutral rewrite for more trustworthy workflows.

    Project page · Code · PyPI

  • DIA (Data & Impact Accounting)

    Open-source toolkit to track energy, water, and CO₂ impact of open-source AI models and their derivatives, with a public dashboard.

    Project page · Code · PyPI · Dashboard

  • FairSense-AgentiX

    Agentic workflows for bias detection and risk assessment on text, images, and datasets—planning, tool use, self-critique, and telemetry-backed explanations.

    Project page · Code · PyPI

  • SONIC-O1

    Real-world benchmark for evaluating MLLMs on audio-video understanding, with a public leaderboard.

    Dataset · Code · Leaderboard

  • SONIC-O1 Multi-Agent

    Multi-agent framework for audio-video understanding with chain-of-thought reasoning, self-reflection, and temporal grounding.

    Code

  • Explainable Agentic Evaluation Framework

    Analyzes reasoning traces and interpretability of agentic AI across static and agentic settings.

    Code · Project page

  • Factual Preference Alignment (F-DPO)

    Factuality-aware preference learning to reduce LLM hallucinations without a separate reward model.

    Paper · Project page · Code

  • HumaniBench

    Fairness-focused vision-language benchmark evaluating foundation models across human-centric demographics.

    Project page · Preprint

  • Agentic Transparency

    Survey and framework on interpretability, explainability, and governance of agentic AI systems.

    Project page

View all papers Projects & quickstarts

Citation

If you use any of our tools, datasets, or benchmarks, please cite the relevant work. BibTeX entries are available on each paper's entry in the Papers page.

Responsible AI Notice

This project may generate synthetic data containing demographic attributes for fairness research. These datasets are designed for controlled bias analysis and responsible AI evaluation only. They are not intended to represent or target real individuals. All data generation follows Vector Institute's responsible AI guidelines and AIXpert's ethical framework.

Have feedback or want to contribute? See the Team section on About and open an issue or pull request.