Add research document for tools like AI Verify (#57)

docs/Projects/TAD/tools.md

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+# Research of tools for transparency of algorithmic decision making
+
+In our ongoing research on AI validation and transparency, we are seeking tools to support assessments.
+Ideal tools would combine various technical tests with checklists and questionnaires and have the ability to generate
+reports in both human-friendly and machine-exchangeable formats.
+
+This document contains a list of tools we have found and may want to investigate further.
+
+## AI Verify
+
+AI Verify is an AI governance testing framework and software toolkit that validates the performance of AI systems against
+a set of  internationally recognised principles through standardised tests, and is consistent with international AI governance
+frameworks such as those from European Union, OECD and Singapore.
+
+Links:
+[AI Verify Homepage](https://aiverifyfoundation.sg/),
+[AI Verify documentation](https://imda-btg.github.io/aiverify/),
+[AI Verify Github](https://github.com/IMDA-BTG/aiverify).
+
+## To investigate further
+
+### AI Assessment Tool Belgium
+
+**What is it?** The tool is based on the
+[ALTAI recommendations](https://digital-strategy.ec.europa.eu/en/library/assessment-list-trustworthy-artificial-intelligence-altai-self-assessment)
+published by the European Commission.
+
+**Why interesting?** Although it only includes questionnaires it does give an interesting way
+of reporting the end results. Also this project can still be expanded with technical tests.
+
+**Remarks** Does not include any technical tests at this point.
+
+Links: [ALTAI ai4belgium Homepage](https://altai.ai4belgium.be/),
+[Altai Github](https://github.com/AI4Belgium/ai-assessment-tool).
+
+### IBM Research 360 Toolkit
+
+**What is it?** Open source Python libraries that supports interpretability and explainability of
+datasets and machine learning models. Most relevant tookits are the AI Fairness 360 and AI Explainability 360.
+
+**Why interesting?** Seems to encompass extensive fairness and explainability tests. Codebase seems
+to be active.
+
+**Remarks** It comes as Python and R libraries.
+
+Links: [AI Fairness 360 Github](https://github.com/Trusted-AI/AIF360),
+[AI Explainability 360 Github](https://github.com/Trusted-AI/AIX360?tab=readme-ov-file).
+
+### Hollisticai
+
+**What is it?** Open source tool to assess and improve the trustworthiness of AI systems. Offers
+tools to measure and mitigate bias across numerous tasks. Will be extended to include tools for
+efficacy, robustness, privacy and explainability.
+
+**Why interesting?** Although it is not entirely clear what exactly this tool does (see Remarks)
+it does seem (according to their website) to provide reports on bias and fairness. The Github rep
+does not seem to include any report generating code, but mainly technical tests.
+[Here](https://holisticai.readthedocs.io/en/latest/tutorials/measuring_bias_tutorials/measuring_bias_classification.html)
+is an example in which bias is measured in a classification model.
+
+**Remarks** Website seems to suggest the possibility to generate reports, but this is not directly
+reflected in the codebase. Possibly reports are only available with some sort of licenced product?
+
+Links:
+[Hollisticai homepage](https://www.holisticai.com/),
+[Hollisticai Github](https://github.com/holistic-ai/holisticai).
+
+## Interesting to mention
+
+* [What-if](https://github.com/pair-code/what-if-tool). Provides interface for expanding understanding
+of a black-box classifaction or regression ML model. Can be accessed through TensorBoard or as an
+extension in a Jupyter or Colab notebook. Does not seem to be an active codebase.
+
+* [Aequitas](https://github.com/dssg/aequitas). Open source bias auditing and Fair ML toolkit.
+This already seems to be contained within AI Verify, at least the
+'[fairness tree](https://imda-btg.github.io/aiverify/how-to/use-fairness-tree/)'.
+
+* [Facets](https://github.com/PAIR-code/facets). Open source toolkit for understanding and analyzing
+ML datasets. Note that does not include ML models.
+
+* [Fairness Indicators](https://github.com/tensorflow/fairness-indicators). Open source Python
+package which enables easy computation of commonly-identified fairness metrics for binary and
+multiclass classifiers. Part of TensorFlow.
+k
+* [Fairlearn](https://github.com/fairlearn/fairlearn). Open source Python package that empowers
+developers of AI systems to assess their system's fairness and mitigate any observed unfairness
+issuess.
+
+* [Dalex](https://dalex.drwhy.ai/). The DALEX package xrays any model and helps to explore and
+explain its behaviour, helps to understand how complex models are working. The main function
+explain() creates a wrapper around a predictive model. Wrapped models may then be explored and
+compared with a collection of local and global explainers. Recent developments from the area of
+Interpretable Machine Learning/eXplainable Artificial Intelligence.
+
+* [SigmaRed](https://www.sigmared.ai). SigmaRed platform enables comprehensive third-party AI
+risk management (AI TPRM) and rapidly reduces the cycle time of conducting AI risks assessments
+while providing deep visibility, control, stakeholder based reporting, and detailed evidence repository.
+Does not seem to be open source.
+
+* [Anch.ai](https://anch.ai/about/). The end-to-end cloud solution empowers global data-driven
+organizations to govern and deploy responsible, transparent, and explainable AI aligned with
+upcoming EU regulation AI Act. Does not seem to be open source.
+
+* [CredoAI](https://www.credo.ai/). Credo AI is an AI governance platform that helps companies adopt,
+scale, and govern AI safely and effectively. Does not seem to be open source.
+
+## The FATE system
+
+Paper by TNO about the FATE system. Acronym stands for "FAir, Transparent and Explainable Decision Making."
+
+Tools mentioned include some of the above: Aequitas, AI Fairness 360, Dalex, Fairlean,
+Responsibly, and What-If-Tool
+
+Links:
+[Paper](https://ceur-ws.org/Vol-2846/paper35.pdf),
+[Article](https://www.sciencedirect.com/science/article/pii/S2666920X23000310),
+[Microsoft links](https://www.sciencedirect.com/science/article/pii/S2666920X23000310).