Organizations Focused on AI Fairness
- We Count: Community-driven project to address the inherent bias against small minorities and outliers in AI and data analytics.
- OpenAI: Discovering and enacting the path to safe artificial general intelligence.
- AI Now Institute: Examining the social implications of artificial intelligence.
- Algorithmic Justice League: A collective that aims to highlight algorithmic bias.
- Algorithmic Fairness for People with Disabilities, Institute for Tech Law & Policy, Georgetown University.
- EqualAI: “It’s up to us to write and right the future with equal AI.”
- Institute for Ethical AI & Machine Learning, The: Research in processes and frameworks for responsible development, deployment, and operation.
- Partnership on AI: Fair, Transparent, and Accountable AI.
- Open Ethics Initiative: Engages in transparent AI design: Inclusive Dialog, Vertical impact assessment, Self-disclosure.
- Google PAIR (People + AI Research): “Human-centered research and design to make AI partnerships productive, enjoyable, and fair.”
- Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics, Harvard Law School.
- Artificial Intelligence for the American People: 2020 U.S. AI standards, including ethical.
- Center for Humane Technology: We envision a world where technology supports our shared well-being, sense-making, democracy, and ability to tackle complex global challenges.
- IBM Research Science for Social Good: “Applied science can help solve the world’s toughest problems and inspire business innovation.”
- Fairness and machine learning (Book in Development): Limitations and Opportunities
- AI Ethics (collection of articles): MIT Technology Review.
- Machine learning systems, fair or biased, reflect our moral standards: South China Morning Post.
- Stories of AI Failure and How to Avoid Similar AI Fails: Lexalytics
- Artificial intelligence in a crisis needs ethics with urgency: Nature.
- Beyond the AI hype cycle: Trust and the future of AI: MIT Technology Review.
- Biased AI perpetuates racial injustice: Tech Crunch.
- Fighting AI Bias – Digital Rights are Human Rights: Forbes.
- How can we make sure that algorithms are fair?: The Conversation.
- Discriminating Systems: Gender, Race, and Power in AI (PDF): AI Now Institute.
- Artificial Intelligence Learns to Talk Back to Bigots: Scientific American.
- AI thinks like a corporation—and that’s worrying: The Economist.
- DeepMind is Using This Old Technique to Evaluate Fairness in Machine Learning Models: Towards Data Science.
- Smart Cities Urged to Look ‘Beyond Rich, White Men’: The Irrawaddy.
- What is bias in AI really, and why can’t AI neutralize it?: ZDNet.
- A.I. Bias Isn’t the Problem. Our Society Is: Fortune.
- What Unstructured Data Can Tell You About Your Company’s Biases: Fortune.
- Artificial Intelligence Has A Problem With Bias, Here’s How To Tackle It: Forbes.
- Bias in AI and Machine Learning: Sources and Solutions: Lexalytics.
- Understanding and Reducing Bias in Machine Learning: Towards Data Science.
- Bias in, bias out: the Stanford scientist out to make AI less white and male: Post Magazine.
- Sidewalk Toronto and Why Smarter is Not Better: Medium.
- AI Fairness for People with Disabilities: Point of View: IBM.
- How This Google Team Is Trying to Make the Company’s Products More Inclusive: Fortune.
- Big Data Ethics and 10 Controversial Data Science Experiments: Data Science Dojo.
- To regulate AI we need new laws, not just a code of ethics: The Guardian.
- Definition: machine learning bias (AI bias): Tech Target.
- ‘A white mask worked better’: why algorithms are not colour blind: The Guardian, 2017.
- How white engineers built racist code – and why it’s dangerous for black people: The Guardian, 2017.
- Artificial Intelligence’s White Guy Problem: New York Times, 2016.
- Semantics derived automatically from language corpora necessarily contain human biases: Cornell University, 2016.