└── README.md /README.md: -------------------------------------------------------------------------------- 1 | ## **2023 Note: This is an outdated list of references.** 2 | 3 | # Machine Learning Ethics References 4 | References about Machine Learning and Data Science discrimination, bias, ethics. 5 | 6 | ## Discussion 7 | * [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) 8 | 9 | ## Discussion 10 | * [AI Ethics on Reddit](https://www.reddit.com/r/AIethics/) 11 | * [(HN) Attacking discrimination with smarter machine learning](https://news.ycombinator.com/item?id=13004790) 12 | * [(HN) Cathy O’Neil on Weapons of Math Destruction](https://news.ycombinator.com/item?id=12642432) 13 | * [(HN) on Neural Net Trained on Mugshots Predicts Criminals](https://news.ycombinator.com/item?id=13034116) 14 | * [(HN) Justice.exe: Bias in Algorithmic sentencing ](https://news.ycombinator.com/item?id=14285116) 15 | 16 | ## Podcast 17 | 18 | * [EconTalk Episode with Cathy O'Neil](http://www.econtalk.org/archives/2016/10/cathy_oneil_on_1.html) 19 | * [Machine Ethic Podcasts](http://machine-ethics.net/podcast/) 20 | 21 | ## Videos 22 | 23 | * [Ethics of Artificial Intelligence conference NYU 2016](https://livestream.com/nyu-tv/ethicsofAI/) 24 | * [A Story of Discrimination and Unfairness - Aylin Caliskan 33c3 2016](https://media.ccc.de/v/33c3-8026-a_story_of_discrimination_and_unfairness) 25 | * [AI Now 2017 Symposium](https://www.youtube.com/watch?v=npL_UsK_npE) 26 | * [The Trouble with Bias - NIPS 2017 Keynote](https://www.youtube.com/watch?v=fMym_BKWQzk) 27 | * [Eyeo 2018 - Meredith Whittaker - DATA GENESIS: AI'S PRIMORDIAL SOUP](https://vimeo.com/287094149) 28 | * ["Privacy: the Last Stand for Fair Algorithms" by Katharine Jarmul](https://www.youtube.com/watch?v=j4WRv6GNuDM) 29 | 30 | ## Papers 31 | 32 | * [Bias in Computer Systems](https://www.nyu.edu/projects/nissenbaum/papers/biasincomputers.pdf) 33 | * [Equality of Opportunity in Supervised Learning](https://drive.google.com/file/d/0B-wQVEjH9yuhanpyQjUwQS1JOTQ/view) 34 | * [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) 35 | * [The Ethics of Artificial Intelligence](http://www.nickbostrom.com/ethics/artificial-intelligence.pdf) 36 | * [Automated Inference on Criminality using Face Images](https://arxiv.org/abs/1611.04135) 37 | * [Semantics derived automatically from language corpora contain human-like biases](http://opus.bath.ac.uk/55288/) 38 | * [European Union regulations on algorithmic decision-making and a "right to explanation"](https://arxiv.org/abs/1606.08813) 39 | * [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://homes.cs.washington.edu/~my89/publications/bias.pdf) 40 | * [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://arxiv.org/abs/1607.06520) 41 | * [Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.](https://osf.io/zn79k/) 42 | * [Delayed Impact of Fair Machine Learning](http://bair.berkeley.edu/blog/2018/05/17/delayed-impact/) 43 | * [Bias detectives: the researchers striving to make algorithms fair](https://www.nature.com/articles/d41586-018-05469-3) 44 | * [ No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://arxiv.org/abs/1711.08536) 45 | ## Books 46 | 47 | * [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&qid=1479818920&sr=8-1&keywords=Weapons-Math-Destruction-Increases-Inequality) 48 | * [Interpretable Machine Learning A Guide for Making Black Box Models Explainable.](https://christophm.github.io/interpretable-ml-book/) 49 | * [Fairness and machine learning book](http://fairmlbook.org/) 50 | 51 | ## Articles 52 | * [Algorithms: AI’s creepy control must be open to inspection](https://www.theguardian.com/commentisfree/2017/jan/01/algorithms-ai-artificial-intelligence-facebook-accountability) 53 | * [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) 54 | * [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) 55 | * [ProPublica series on Machine Bias](https://www.propublica.org/series/machine-bias) 56 | * [Artificial Intelligence’s White Guy Problem](http://www.nytimes.com/2016/06/26/opinion/sunday/artificial-intelligences-white-guy-problem.html) 57 | * [Neural Net Trained on Mugshots Predicts Criminals](https://www.technologyreview.com/s/602955/neural-network-learns-to-identify-criminals-by-their-faces/) 58 | * [Attacking discrimination with smarter machine learning](http://research.google.com/bigpicture/attacking-discrimination-in-ml/) 59 | * [The Ethical Data Scientis](http://www.slate.com/articles/technology/future_tense/2016/02/how_to_bring_better_ethics_to_data_science.html) 60 | * [Machine Bias](https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing) 61 | * [Machine Bias - How We Analyzed the COMPAS Recidivism Algorithm](https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm) 62 | * [ProPublica Responds to Company’s Critique of Machine Bias Story](https://www.propublica.org/article/propublica-responds-to-companys-critique-of-machine-bias-story) 63 | * [Are Machines Biased, or Are We Biased Against Machines?](http://alex.miller.im/posts/are-we-biased-against-machines-propublica-recidivism/) 64 | * [Artificial Intelligence’s White Guy Problem](http://www.nytimes.com/2016/06/26/opinion/sunday/artificial-intelligences-white-guy-problem.html) 65 | * [Buyer Beware: A hard look at police ‘threat scores.’](https://www.equalfuture.us/2016/01/14/buyer-beware-police-threat-scores/) 66 | * [Computer and Information Ethics](http://plato.stanford.edu/entries/ethics-computer/) 67 | * [Social Networking and Ethics](http://plato.stanford.edu/entries/ethics-social-networking/) 68 | * [Internet Research Ethics](http://plato.stanford.edu/entries/ethics-internet-research/) 69 | * [Search Engines and Ethics](http://plato.stanford.edu/entries/ethics-search/) 70 | * [How a Machine Learns Prejudice](https://www.scientificamerican.com/article/how-a-machine-learns-prejudice/) 71 | * [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/) 72 | * [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) 73 | * [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) 74 | * [Inspecting Algorithms for Bias](https://www.technologyreview.com/s/607955/inspecting-algorithms-for-bias/) 75 | * [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/) 76 | * [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/) 77 | * [Turns Out Algorithms Are Racist](https://newrepublic.com/article/144644/turns-algorithms-racist) 78 | * [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/) 79 | * [How Tech Giants Are Devising Real Ethics for Artificial Intelligence](https://www.nytimes.com/2016/09/02/technology/artificial-intelligence-ethics.html) 80 | * [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) 81 | * [Something is wrong on the internet, on youtube automated videos by James Bridle](https://medium.com/@jamesbridle/something-is-wrong-on-the-internet-c39c471271d2) 82 | * [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/) 83 | * [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) 84 | * [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/) 85 | * [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/) 86 | * [Prescription: AI - Quartz series](https://qz.com/se/prescription-ai/) 87 | * [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) 88 | * [A skeptic’s guide to thinking about AI - on AI Now 2018](https://www.fastcompany.com/90252753/a-skeptics-guide-to-thinking-about-ai) 89 | 90 | ## Others 91 | * [Machine ethics: The robot’s dilemma](http://www.nature.com/news/machine-ethics-the-robot-s-dilemma-1.17881) 92 | * [Morals and the machine](http://www.economist.com/node/21556234) 93 | * [Robotics: Ethics of artificial intelligence](http://www.nature.com/news/robotics-ethics-of-artificial-intelligence-1.17611) 94 | * [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) 95 | * ["RoboCop” assignment Columbia University NYPD’s “Stop, Question and Frisk” records](http://columbialion.com/colorcode-statement-on-coms-4771-stop-and-frisk-competition/) 96 | * [Professor Satyen Kale Responds to ‘RoboCop’ Machine Learning Assignment](http://columbialion.com/professor-satyen-kale-responds-to-robocop-ml-assignment/) 97 | * [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) 98 | * [Justice.exe - Educative Game](http://justiceexe.com/index.html) 99 | * [mathwashing](http://www.mathwashing.com/) 100 | * [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/) 101 | * [Research on Algorithmic Fairness, haverford](http://fairness.haverford.edu/) 102 | * [NORMAN World's first psychopath AI](http://norman-ai.mit.edu/) 103 | * [AI can be sexist and racist — it’s time to make it fair](https://www.nature.com/articles/d41586-018-05707-8) 104 | 105 | ## Reports 106 | * [ARTIFICIAL INTELLIGENCE AND LIFE IN 2030 - 2016 Report](https://ai100.stanford.edu/2016-report) 107 | * [AI Now Report 2018.pdf](https://ainowinstitute.org/AI_Now_2018_Report.pdf) 108 | * [AI Now 2019 Discriminating Systems: Gender, Race, and Power in AI](https://ainowinstitute.org/discriminatingsystems.pdf) 109 | 110 | ## Conferences, Workshops, Symposiums 111 | 112 | * [Workshop on Fairness, Accountability, and Transparency in Machine Learning](http://www.fatml.org/) 113 | * [AI Now](https://artificialintelligencenow.com/schedule/2017-symposium) 114 | * [Ethics of Artificial Intelligence](https://wp.nyu.edu/consciousness/ethics-of-artificial-intelligence/) 115 | * [Black in AI](http://ai.stanford.edu/~tgebru/blackAI) 116 | * [Algorithms and Explanations](http://www.law.nyu.edu/centers/ili/events/algorithms-and-explanations) 117 | * [Machine Learning and the Law](http://www.mlandthelaw.org/) 118 | * [Ethics in Mathematics - Cambridge University](http://www.ethics.maths.cam.ac.uk/EiM1/) 119 | * [AI Now 2018 Symposium - video](https://www.youtube.com/watch?v=NmdAtfcmTNg) 120 | * [AI Now 2019 Symposium - video](https://ainowinstitute.org/symposia/2019-symposium.html) 121 | 122 | ## Classes 123 | 124 | * [Fairness in Machine Learning](https://fairmlclass.github.io/) 125 | * [INFO 4270: ETHICS AND POLICY IN DATA SCIENCE](https://docs.google.com/document/d/1GV97qqvjQNvyM2I01vuRaAwHe9pQAZ9pbP7KkKveg1o/) 126 | * [CS109: Ethical Foundations of Computer Science](https://www.cs.utexas.edu/~ans/classes/cs109/schedule.html) 127 | * [An Introduction to Data Ethics](https://www.scu.edu/ethics/focus-areas/technology-ethics/resources/an-introduction-to-data-ethics/) 128 | * [Machine Learning Fairness by Google](https://developers.google.com/machine-learning/crash-course/fairness/video-lecture) 129 | 130 | ## Lists 131 | 132 | * [A critical reading list for engineers, designers, and policy makers](https://github.com/rockita/criticalML) 133 | * [Awesome-Machine-Learning-Interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability) 134 | * [Fast AI Ethics Resources](https://www.fast.ai/2018/09/24/ai-ethics-resources/) 135 | ## People and Organizations 136 | 137 | * [Kate Crawford](http://www.katecrawford.net/) 138 | * [Meredith Whittaker](https://twitter.com/mer__edith) 139 | * [Kate Darling](https://twitter.com/grok_) 140 | * [Cathy O'Neil](https://mathbabe.org/) 141 | * [Alan Winfield](https://alanwinfield.blogspot.com/) 142 | * [AI Now](https://artificialintelligencenow.com/) 143 | * [Algorithm Watch](https://algorithmwatch.org) 144 | * [Moritz Hardt](http://moritzhardt.com/) 145 | * [Solon Barocas](http://solon.barocas.org/) 146 | * [Institute for Ethics and Emerging Technologies](https://ieet.org/) 147 | * [The Center for Technology, Society & Policy Berkley](https://twitter.com/CTSPBerkeley) 148 | * [Zeynep Tufekci](https://twitter.com/zeynep) 149 | * [Data & Society](https://datasociety.net/about/) 150 | * [PERVADE: Pervasive Data Ethics](https://pervade.umd.edu/) 151 | * [DataEthics](https://dataethics.eu/en/) 152 | --------------------------------------------------------------------------------