{"id":13713088,"url":"https://github.com/radames/Machine-Learning-Ethics-References","last_synced_at":"2025-05-06T23:30:32.636Z","repository":{"id":137671849,"uuid":"74471359","full_name":"radames/Machine-Learning-Ethics-References","owner":"radames","description":"List of references about Machine Learning bias and ethics ","archived":true,"fork":false,"pushed_at":"2023-04-26T17:25:40.000Z","size":54,"stargazers_count":61,"open_issues_count":0,"forks_count":11,"subscribers_count":7,"default_branch":"master","last_synced_at":"2025-04-26T14:09:46.282Z","etag":null,"topics":["ai","ai-ethics","artificial-intelligence","awesome-list","bias","data-science","discrimination","ethics","fairness","machine-ethics","machine-learning","machines-biased","references"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/radames.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2016-11-22T12:42:12.000Z","updated_at":"2025-04-01T23:51:17.000Z","dependencies_parsed_at":"2024-01-07T18:17:32.614Z","dependency_job_id":"498567c5-dff5-42f4-9c85-d09cfd1d731c","html_url":"https://github.com/radames/Machine-Learning-Ethics-References","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/radames%2FMachine-Learning-Ethics-References","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/radames%2FMachine-Learning-Ethics-References/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/radames%2FMachine-Learning-Ethics-References/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/radames%2FMachine-Learning-Ethics-References/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/radames","download_url":"https://codeload.github.com/radames/Machine-Learning-Ethics-References/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252787158,"owners_count":21804209,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","ai-ethics","artificial-intelligence","awesome-list","bias","data-science","discrimination","ethics","fairness","machine-ethics","machine-learning","machines-biased","references"],"created_at":"2024-08-02T23:01:27.265Z","updated_at":"2025-05-06T23:30:32.311Z","avatar_url":"https://github.com/radames.png","language":null,"funding_links":[],"categories":["Table of Contents","AI Incidents, Critiques, and Research Resources"],"sub_categories":["List of Lists"],"readme":"## **2023 Note: This is an outdated list of references.**\n\n# Machine Learning Ethics References\nReferences about Machine Learning and Data Science discrimination, bias, ethics.\n\n## Discussion\n* [Cathy O’Neil Twitter Discussion 'Algorithms are a threat to society and so far, academia is asleep at the wheel.'](https://twitter.com/mathbabedotorg/status/930429461165760512)\n\n## Discussion\n* [AI Ethics on Reddit](https://www.reddit.com/r/AIethics/)\n* [(HN) Attacking discrimination with smarter machine learning](https://news.ycombinator.com/item?id=13004790)\n* [(HN) Cathy O’Neil on Weapons of Math Destruction](https://news.ycombinator.com/item?id=12642432)\n* [(HN) on Neural Net Trained on Mugshots Predicts Criminals](https://news.ycombinator.com/item?id=13034116)\n* [(HN) Justice.exe: Bias in Algorithmic sentencing ](https://news.ycombinator.com/item?id=14285116)\n\n## Podcast\n\n* [EconTalk Episode with Cathy O'Neil](http://www.econtalk.org/archives/2016/10/cathy_oneil_on_1.html)\n* [Machine Ethic Podcasts](http://machine-ethics.net/podcast/)\n\n## Videos\n\n* [Ethics of Artificial Intelligence conference NYU 2016](https://livestream.com/nyu-tv/ethicsofAI/)\n* [A Story of Discrimination and Unfairness - Aylin Caliskan 33c3 2016](https://media.ccc.de/v/33c3-8026-a_story_of_discrimination_and_unfairness)\n* [AI Now 2017 Symposium](https://www.youtube.com/watch?v=npL_UsK_npE)\n* [The Trouble with Bias - NIPS 2017 Keynote](https://www.youtube.com/watch?v=fMym_BKWQzk)\n* [Eyeo 2018 - Meredith Whittaker - DATA GENESIS: AI'S PRIMORDIAL SOUP](https://vimeo.com/287094149)\n* [\"Privacy: the Last Stand for Fair Algorithms\" by Katharine Jarmul](https://www.youtube.com/watch?v=j4WRv6GNuDM)\n\n## Papers \n\n* [Bias in Computer Systems](https://www.nyu.edu/projects/nissenbaum/papers/biasincomputers.pdf)\n* [Equality of Opportunity in Supervised Learning](https://drive.google.com/file/d/0B-wQVEjH9yuhanpyQjUwQS1JOTQ/view)\n* [Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models](https://sites.google.com/site/zliobaitefiles2/Zliobaite_fair_regression.pdf?attredirects=1)\n* [The Ethics of Artificial Intelligence](http://www.nickbostrom.com/ethics/artificial-intelligence.pdf)\n* [Automated Inference on Criminality using Face Images](https://arxiv.org/abs/1611.04135)\n* [Semantics derived automatically from language corpora contain human-like biases](http://opus.bath.ac.uk/55288/)\n* [European Union regulations on algorithmic decision-making and a \"right to explanation\"](https://arxiv.org/abs/1606.08813)\n* [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://homes.cs.washington.edu/~my89/publications/bias.pdf)\n* [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://arxiv.org/abs/1607.06520)\n* [Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.](https://osf.io/zn79k/)\n* [Delayed Impact of Fair Machine Learning](http://bair.berkeley.edu/blog/2018/05/17/delayed-impact/)\n* [Bias detectives: the researchers striving to make algorithms fair](https://www.nature.com/articles/d41586-018-05469-3)\n* [ No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://arxiv.org/abs/1711.08536)\n## Books\n\n* [Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy](https://www.amazon.com/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815/ref=sr_1_1?ie=UTF8\u0026qid=1479818920\u0026sr=8-1\u0026keywords=Weapons-Math-Destruction-Increases-Inequality)\n* [Interpretable Machine Learning A Guide for Making Black Box Models Explainable.](https://christophm.github.io/interpretable-ml-book/)\n* [Fairness and machine learning book](http://fairmlbook.org/)\n\n## Articles\n* [Algorithms: AI’s creepy control must be open to inspection](https://www.theguardian.com/commentisfree/2017/jan/01/algorithms-ai-artificial-intelligence-facebook-accountability)\n* [AI watchdog needed to regulate automated decision-making, say experts](https://www.theguardian.com/technology/2017/jan/27/ai-artificial-intelligence-watchdog-needed-to-prevent-discriminatory-automated-decisions)\n* [Scholars Delve Deeper Into The Ethics Of Artificial Intelligence](http://www.npr.org/sections/alltechconsidered/2016/11/21/502905772/scholars-delve-deeper-into-the-ethics-of-artificial-intelligence)\n* [ProPublica series on Machine Bias](https://www.propublica.org/series/machine-bias)\n* [Artificial Intelligence’s White Guy Problem](http://www.nytimes.com/2016/06/26/opinion/sunday/artificial-intelligences-white-guy-problem.html)\n* [Neural Net Trained on Mugshots Predicts Criminals](https://www.technologyreview.com/s/602955/neural-network-learns-to-identify-criminals-by-their-faces/)\n* [Attacking discrimination with smarter machine learning](http://research.google.com/bigpicture/attacking-discrimination-in-ml/)\n* [The Ethical Data Scientis](http://www.slate.com/articles/technology/future_tense/2016/02/how_to_bring_better_ethics_to_data_science.html)\n* [Machine Bias](https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing)\n* [Machine Bias - How We Analyzed the COMPAS Recidivism Algorithm](https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm)\n* [ProPublica Responds to Company’s Critique of Machine Bias Story](https://www.propublica.org/article/propublica-responds-to-companys-critique-of-machine-bias-story)\n* [Are Machines Biased, or Are We Biased Against Machines?](http://alex.miller.im/posts/are-we-biased-against-machines-propublica-recidivism/)\n* [Artificial Intelligence’s White Guy Problem](http://www.nytimes.com/2016/06/26/opinion/sunday/artificial-intelligences-white-guy-problem.html)\n* [Buyer Beware: A hard look at police ‘threat scores.’](https://www.equalfuture.us/2016/01/14/buyer-beware-police-threat-scores/)\n* [Computer and Information Ethics](http://plato.stanford.edu/entries/ethics-computer/)\n* [Social Networking and Ethics](http://plato.stanford.edu/entries/ethics-social-networking/)\n* [Internet Research Ethics](http://plato.stanford.edu/entries/ethics-internet-research/)\n* [Search Engines and Ethics](http://plato.stanford.edu/entries/ethics-search/)\n* [How a Machine Learns Prejudice](https://www.scientificamerican.com/article/how-a-machine-learns-prejudice/)\n* [Courts Are Using AI to Sentence Criminals. That Must Stop Now](https://www.wired.com/2017/04/courts-using-ai-sentence-criminals-must-stop-now/)\n* [Sent to Prison by a Software Program’s Secret Algorithms](https://www.nytimes.com/2017/05/01/us/politics/sent-to-prison-by-a-software-programs-secret-algorithms.html)\n* [Even artificial intelligence can acquire biases against race and gender](http://www.sciencemag.org/news/2017/04/even-artificial-intelligence-can-acquire-biases-against-race-and-gender)\n* [Inspecting Algorithms for Bias](https://www.technologyreview.com/s/607955/inspecting-algorithms-for-bias/)\n* [If you’re not a white male, artificial intelligence’s use in healthcare could be dangerous](https://qz.com/1023448/if-youre-not-a-white-male-artificial-intelligences-use-in-healthcare-could-be-dangerous/)\n* [Biased Algorithms Are Everywhere, and No One Seems to Care](https://www.technologyreview.com/s/608248/biased-algorithms-are-everywhere-and-no-one-seems-to-care/)\n* [Turns Out Algorithms Are Racist](https://newrepublic.com/article/144644/turns-algorithms-racist)\n* [Machines Taught by Photos Learn a Sexist View of Women](https://www.wired.com/story/machines-taught-by-photos-learn-a-sexist-view-of-women/)\n* [How Tech Giants Are Devising Real Ethics for Artificial Intelligence](https://www.nytimes.com/2016/09/02/technology/artificial-intelligence-ethics.html)\n* [New AI can guess whether you're gay or straight from a photograph](https://www.theguardian.com/technology/2017/sep/07/new-artificial-intelligence-can-tell-whether-youre-gay-or-straight-from-a-photograph)\n* [Something is wrong on the internet, on youtube automated videos by James Bridle](https://medium.com/@jamesbridle/something-is-wrong-on-the-internet-c39c471271d2)\n* [Trump’s “extreme-vetting” software will discriminate against immigrants “under a veneer of objectivity,” say experts](https://theintercept.com/2017/11/16/trumps-extreme-vetting-software-will-discriminate-against-immigrants-under-a-veneer-of-objectivity-say-experts/)\n* [ACLU calls out Amazon, Washington Co. sheriff's office for facial recognition tech](https://www.kgw.com/article/money/aclu-calls-out-amazon-washington-co-sheriffs-office-for-facial-recognition-tech/283-557099068)\n* [This startup’s racial-profiling algorithm shows AI can be dangerous way before any robot apocalypse](https://qz.com/1286533/a-startup-selling-racial-profiling-software-shows-how-ai-can-be-dangerous-way-before-any-robot-apocalypse/)\n* [Facial recognition software is not ready for use by law enforcement](https://techcrunch.com/2018/06/25/facial-recognition-software-is-not-ready-for-use-by-law-enforcement/)\n* [Prescription: AI - Quartz series](https://qz.com/se/prescription-ai/)\n* [Amazon scraps secret AI recruiting tool that showed bias against women](https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G)\n* [A skeptic’s guide to thinking about AI -  on AI Now 2018](https://www.fastcompany.com/90252753/a-skeptics-guide-to-thinking-about-ai)\n\n## Others\n* [Machine ethics: The robot’s dilemma](http://www.nature.com/news/machine-ethics-the-robot-s-dilemma-1.17881)\n* [Morals and the machine](http://www.economist.com/node/21556234)\n* [Robotics: Ethics of artificial intelligence](http://www.nature.com/news/robotics-ethics-of-artificial-intelligence-1.17611)\n* [Do no harm, don't discriminate: official guidance issued on robot ethics](https://www.theguardian.com/technology/2016/sep/18/official-guidance-robot-ethics-british-standards-institute)\n* [\"RoboCop” assignment Columbia University NYPD’s “Stop, Question and Frisk” records](http://columbialion.com/colorcode-statement-on-coms-4771-stop-and-frisk-competition/)\n* [Professor Satyen Kale Responds to ‘RoboCop’ Machine Learning Assignment](http://columbialion.com/professor-satyen-kale-responds-to-robocop-ml-assignment/)\n* [White House document: Preparing for the Future of Artificial Intelligence](https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf)\n* [Justice.exe - Educative Game](http://justiceexe.com/index.html)\n* [mathwashing](http://www.mathwashing.com/)\n* [ConceptNet Numberbatch 17.04: better, less-stereotyped word vectors](https://blog.conceptnet.io/2017/04/24/conceptnet-numberbatch-17-04-better-less-stereotyped-word-vectors/)\n* [Research on Algorithmic Fairness, haverford](http://fairness.haverford.edu/)\n* [NORMAN World's first psychopath AI](http://norman-ai.mit.edu/)\n* [AI can be sexist and racist — it’s time to make it fair](https://www.nature.com/articles/d41586-018-05707-8)\n\n## Reports\n* [ARTIFICIAL INTELLIGENCE AND LIFE IN 2030 - 2016 Report](https://ai100.stanford.edu/2016-report)\n* [AI Now Report 2018.pdf](https://ainowinstitute.org/AI_Now_2018_Report.pdf)\n* [AI Now 2019 Discriminating Systems: Gender, Race, and Power in AI](https://ainowinstitute.org/discriminatingsystems.pdf)\n\n## Conferences, Workshops, Symposiums \n\n* [Workshop on Fairness, Accountability, and Transparency in Machine Learning](http://www.fatml.org/)\n* [AI Now](https://artificialintelligencenow.com/schedule/2017-symposium)\n* [Ethics of Artificial Intelligence](https://wp.nyu.edu/consciousness/ethics-of-artificial-intelligence/)\n* [Black in AI](http://ai.stanford.edu/~tgebru/blackAI)\n* [Algorithms and Explanations](http://www.law.nyu.edu/centers/ili/events/algorithms-and-explanations)\n* [Machine Learning and the Law](http://www.mlandthelaw.org/)\n* [Ethics in Mathematics - Cambridge University](http://www.ethics.maths.cam.ac.uk/EiM1/)\n* [AI Now 2018 Symposium - video](https://www.youtube.com/watch?v=NmdAtfcmTNg)\n* [AI Now 2019 Symposium - video](https://ainowinstitute.org/symposia/2019-symposium.html)\n\n## Classes\n\n* [Fairness in Machine Learning](https://fairmlclass.github.io/)\n* [INFO 4270: ETHICS AND POLICY IN DATA SCIENCE](https://docs.google.com/document/d/1GV97qqvjQNvyM2I01vuRaAwHe9pQAZ9pbP7KkKveg1o/)\n* [CS109: Ethical Foundations of Computer Science](https://www.cs.utexas.edu/~ans/classes/cs109/schedule.html)\n* [An Introduction to Data Ethics](https://www.scu.edu/ethics/focus-areas/technology-ethics/resources/an-introduction-to-data-ethics/)\n* [Machine Learning Fairness by Google](https://developers.google.com/machine-learning/crash-course/fairness/video-lecture)\n\n## Lists \n\n* [A critical reading list for engineers, designers, and policy makers](https://github.com/rockita/criticalML)\n* [Awesome-Machine-Learning-Interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability)\n* [Fast AI Ethics Resources](https://www.fast.ai/2018/09/24/ai-ethics-resources/)\n## People and Organizations\n\n* [Kate Crawford](http://www.katecrawford.net/)\n* [Meredith Whittaker](https://twitter.com/mer__edith)\n* [Kate Darling](https://twitter.com/grok_)\n* [Cathy O'Neil](https://mathbabe.org/)\n* [Alan Winfield](https://alanwinfield.blogspot.com/)\n* [AI Now](https://artificialintelligencenow.com/)\n* [Algorithm Watch](https://algorithmwatch.org)\n* [Moritz Hardt](http://moritzhardt.com/)\n* [Solon Barocas](http://solon.barocas.org/)\n* [Institute for Ethics and Emerging Technologies](https://ieet.org/)\n* [The Center for Technology, Society \u0026 Policy Berkley](https://twitter.com/CTSPBerkeley)\n* [Zeynep Tufekci](https://twitter.com/zeynep)\n* [Data \u0026 Society](https://datasociety.net/about/)\n* [PERVADE: Pervasive Data Ethics](https://pervade.umd.edu/)\n* [DataEthics](https://dataethics.eu/en/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fradames%2FMachine-Learning-Ethics-References","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fradames%2FMachine-Learning-Ethics-References","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fradames%2FMachine-Learning-Ethics-References/lists"}