AI Toolkits

(Tools, Checklists, Simulations)


Explainable AI Start-ups

The Alan Turing Institute and the Information Commissioner’s Office (ICO), Project ExplAIn

AI Now Institute, Algorithmic Accountability Policy Toolkit

Center for Democracy & technology (CDT), DD Tool


CIGREF, Digital Ethics

Driven Data, Deon ethics checklist


European Commission High-Level Expert Group on AI: Trustworthy AI Assessment List


Facets: Analyzing machine learning datasets by visualization

FactSheets: Increasing Trust in AI Services through Supplier’s Declarations of Conformity


Google, Playing with AI Fairness: What-if Tool

Google, Dataset Search Beta ( does not suggest that the datasets are free of bias, but only provides link to this Google tool)

Google, Explainable AI 

GovEx, the City and County of San Francisco, Harvard DataSmart, and Data Community DC, Ethics and Algorithms Toolkit

IBM, AI Fairness 360 Open Source Toolkit

IBM, IBM Watson OpenScale

Linux Foundation, Apache NiFi ‹› AI Fairness 360 (AIF360) Integration – Trusted AI Architecture Development Report 1

IDEO, AI Ethics Cards

The Institute and Faculty of Actuaries (IFoA) and the Royal Statistical Society (RSS), A Guide for Ethical Data Science

Kat Zhou, Design Ethically Toolkit

​Kathy Baxter (Salesforce Research), How to Build Ethics into AI — Part I Research-based recommendations to keep humanity in AI

Open Data Institute, The Data Ethics Canvas

Open Robo Ethics, AI Ethics Assessment Toolkit

ProPublica, Data Store

PWC, Responsible AI Toolkit


Responsible Research and Innovation, Self-Reflection Tool

Responsible Innovation Compass, Self-Check


Santa Clara University, Markkula Center, An Ethical Toolkit for Engineering/Design Practice


Smart Dubai, AI System Ethics Self-Assessment Tool

TensorFlow, Fairness Indicators

TensorFlow, Model Analyzer

University of Chicago Center for Data Science and Public Policy, Aequitas Bias & Fairness Audit

University of Washington, Lime

Utrecht University, Data Ethics Decision Aid (DEDA)

Value Sensitive Design and Information Systems

Wachter, Sandra, Brent Mittelstadt, and Chris Russell. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harvard Journal of Law & Technology 31.2 (2018).

Watson, David, and Luciano Floridi. “The Explanation Game: A Formal Framework for Interpretable Machine Learning.” (2020)


World Economic Forum (WEF) with Centre for the Fourth Industrial Revolution Fellows from Accenture, BBVA, IBM, Suntory Holdings, Australian Institute of Company Directors, Best Practice AI, Latham & Watkins, and Splunk, with contributions from AI4All, AI Board Toolkit

10 Simple Rules for Responsible Big Data Research



Lighthouse Career Consulting