Reading List on Ethical AI and Machine Learning

Source

Compiled by Eirini Malliaraki at https://medium.com/@eirinimalliaraki/toward-ethical-transparent-and-fair-ai-ml-a-critical-reading-list-d950e70a70ea

"This reading list is made for engineers, scientists, designers, policy makers and those interested in machine learning and AI. It’s an open ended document that examines machine learning as a sociotechnical system and contextualises its critical discourse."

Bibliography

Copied without the links:

CRITICAL AI

Must:

• Manufacturing an Artificial Intelligence Revolution, Yarden Katz, 2017 (paper)
• The critical engineering manifesto, Julian Oliver, Gordan Savičić, Danja Vasiliev
• Resisting Reduction: Designing our Complex Future with Machines, Joi Ito, 2017 (article)
• The Seven Deadly Sins of Predicting the Future of AI, Rodney Brooks, 2017 (article)
• Remarks on the Hole of Representation in Computer ‘Vision’, Benjamin Bratton, 2017 (video)

Optional:

• Linguistic Capitalism and Algorithmic Mediation, F Kaplan, 2014 (article)
• Defining Algorithmic Ideology: Using Ideology Critique to Scrutinize Corporate Search Engines, Astrid Mager (paper)
• Toward a Critical Technical Practice: Lessons Learned in Trying to Reform AI, Philip E. Agre, 1997 (paper)
• Don’t Call AI “Magic”, M.C.Elish, 2017 (article)
• Deep Learning: A Critical Appraisal, Gary Marcus, 2017 (paper)
• A list of lists and literature review on critical algorithm studies: https://socialmediacollective.org/reading-lists/critical-algorithm-studies/#0.5
• AI and Alchemy, Hubert Dreyfus, 1965

AI ACCOUNTABILITY & GOVERNANCE

Must:

• Algorithmic Accountability: On the Investigation of Black Boxes, 2014, Nicholas Diakopoulos (article)
• Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability, Mike Ananny, Kate Crawford, 2016 (paper)
• The Scored Society: Due Process for Automated Predictions, 2014, Danielle Keats Citron & Frank Pasquale (paper)

Optional:

• Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction (We Robot 2016), M. C. Elish, 2016 (paper)
• Transparent, Explainable, and Accountable AI for Robotics, Sandra Wachter,Brent Mittelstadt, Luciano Floridi (paper)
• The Threat of Algocracy: Reality, Resistance and Accommodation, John Danaher, 2016 (paper)

List of articles on Fairness, Accountability, and Transparency in Machine Learning

AI TRANSPARENCY, EXPLAINABILITY & BIAS

Bias

Must:

• The Trouble with Bias — NIPS 2017 Keynote — Kate Crawford (video)
• Technology Is Biased Too. How Do We Fix It?, Laura Hudson, 2017 (article)
• Machine Bias: There’s software used across the country to predict future criminals. And it’s biased against blacks, julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, 2016 (article)

Optional:

• Is AI Sexist?, Erika Hayasaki, 2017 (article)
• Artificial Intelligence’s White Guy Problem, Kate Crawford, 2016 (article)
• Why Stanford Researchers Tried to Create a ‘Gaydar’ Machine, Heather Murphy, 2017 (article)
• The Dark Secret at the Heart of AI, Will Knight, 2017 (article)

Transparency and Explainability

Must:

• Data’s Disparate Impact, Solon Barocas & Andrew D. Selbst, 2016 (paper)
• Principles for Accountable Algorithms and a Social Impact Statement for Algorithms, FATML.org
• The Mythos of Model Interpretability, Zachary C. Lipton, 2017 (paper)
• Towards A Rigorous Science of Interpretable Machine Learning, Finale Doshi-Velez and Been Kim, 2017 (paper)

Optional:

• Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability, Mike Ananny, Kate Crawford, 2016 (paper)
• Algorithmic Transparency for the Smart City, Robert Brauneis and Ellen P. Goodman (paper)
• Weapons Of Math Destruction: How Big Data Increases Inequality And Threatens Democracy (New York: Crown; 2016)
• Digital Decisions, CDT, 2017
• The Intuitive Appeal of Explainable Machines, Andrew D. Selbst and Solon Barocas, 2017 (paper)

AI FAIRNESS

Must:

• Computer says no: why making AIs fair, accountable and transparent is crucial, Ian Sample, 2017 (article)
• On the (im)possibility of fairness, Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian (paper)

Optional:

• Algorithmic decision making and the cost of fairness, Sam CorbeŠ-Davies, Emma Pierson, Avi Feller, 2017 (paper)
• Algorithmic Justice League
• Understanding unintended sources of unfairness in data driven decision making, Moritz Hardt, 2014 (article)
• Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, Virginia Eubanks, 2018

AI OWNERSHIP & CONTROL

• Big data: Bringing competition policy to the digital era, OECD (paper)
• Big Other: Surveillance Capitalism and the Prospects of an Information Civilization, 2015, Shoshana Zuboff (paper)
• Why Big Tech Companies Are Open-Sourcing Their AI Systems, Patrick Shafto (article)
• Artificial Intelligence Pushes the Antitrust Envelope, Michaella Ross, 2017 (article)

AI ETHICS

Must:

• Why Artificial Intelligence Is Still Waiting For Its Ethics Transplant, Scott Rosenberg, 2017 (article)
• Ethics in AI, debate by Imperial College London (video)
• The ethics of algorithms: Mapping the debate, Brent Daniel Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter and Luciano Floridi, 2016 (paper)
• ASILOMAR AI principles (article)
• The Ethics of Artificial Intelligence, Nick Bostrom and Eliezer Yudkowsky, Machine Intelligence Research Institute (paper)
• Toward an Ethics of Algorithms: Convening, Observation, Probability, and Timeliness, Mike Ananny, 2015 (paper)

Optional:

• EthicsNet, a dataset for machine ethics
• Moral Machine by MIT, a crowd-sourced picture of human opinion on how machines should make decisions when faced with moral dilemmas
• Crowdsourcing Empathy by MIT, 2017
• Ethically aligned design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems, IEEE, 2016 (paper)
• Towards a machine ethics, Prof. Dr. Oliver Bendel (paper)
• Moral Decision Making Frameworks for Artificial Intelligence, Vincent Conitzer, Walter Sinnott-Armstrong, Jana Schaich Borg, Yuan Deng, Max Kramer (paper)
• Toward Ensuring Ethical Behavior from Autonomous Systems: A Case-Supported Principle-Based Paradigm, Michael Anderson, Susan Leigh Anderson, 2015 (paper)
• Artificial Rhetorical Agents and the Computing of Phronesis, Jennifer Maher, 2016 (paper)
• The Philosophy of Science and its relation to Machine Learning, Jon Williamson (paper)

AI and LABOUR

Automation and inequality

Must:

• Prof. Michael Osborne — The Impact of Artificial Intelligence on Jobs, 2017 (video)
• The Relentless Pace of Automation, David Rodman, 2017 (article)
• Technological Unemployment and the Value of Work, John Danaher, 2015 (articles)

Optional:

• Society-in-the-Loop: Programming the Algorithmic Social Contract, Iyad Rahwan, 2017 (paper)
• Machine learning and the politics of data, Ramon Amaro, 2017 (video)
• Theories of Technology and the Production of Value from Everyday Life, 2015 (videos)
• Managing the Machines AI is making prediction cheap, posing new challenges for managers, Ajay Agrawal, Joshua Gans, and Avi Goldfarb, 2017 (paper)
• REGULATING THE LOOP: IRONIES OF AUTOMATION LAW, MEG LETA AMBROSE, 2014 (paper)

Discrimination

• Understanding Fair Labor Practices in a Networked Age by Tamara Kneese, Alex Rosenblat, and Danah Boyd, 2014 (paper)
• Networked Employment Discrimination, Alex Rosenblat, Tamara Kneese, and danah boyd, 2014 (paper)
• Economic Models of (Algorithmic) Discrimination, Bryce W. Goodman (paper)
• Data-Driven Discrimination at Work, Pauline T. Kim, 2017 (paper)

AI and SOCIAL IMPACT

Must:

• AI Now 2017 — Experts Workshop (video)
• Artificial Intelligence for Social Good, Gregory D. Hager, Ann Drobnis, Fei Fang, Rayid Ghani, Amy Greenwald, Terah Lyons, David C. Parkes, Jason Schultz, Suchi Saria, Stephen F. Smith, and Milind Tambe, 2017 (paper)
• AI for the Common Good: Sustainable, Inclusive and Trustworthy, Derek O’Halloran, 2017 (paper)
• Harnessing Artificial Intelligence for the Earth, PwC and the World Economic Forum, 2018 (paper)

Optional:

• An AI pattern language, M.C. Elish and Tim Hwang, Data & Society (paper)
• AI​ ​Now​ ​2017​ ​Report, Alex Campolo, Madelyn Sanfilippo, Meredith Whittaker, Kate Crawford (paper)
• Keeping track of what AI can do, and where it is being applied: https://deepindex.org/
• The current state of machine intelligence 3.0, Shivon Zilis and James Cham, 2016
• AI Progress Measurement: Measuring the Progress of AI Research, Electronic Frontier Foundation

AI POLICY & LAW

• Is effective regulation of AI possible? Eight potential regulatory problems,John Danaher, 2015 (articles)
• A Berkeley View of Systems Challenges for AI, Ion Stoica Dawn Song Raluca Ada Popa David A. Patterson, 2017 (paper)
• THE NATIONAL ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT STRATEGIC PLAN, National Science and Technology Council, 2016 (paper)
• AI Investment Framework, David Kelnar, 2017 (article)
• Encoded laws, policies, and virtues: the offspring of artificial intelligence and public-policy collaboration, Matt Chessen, 2017 (article)

AI and PROPAGANDA

• Weapons of Mass Persuasion, Tim Hwang, 2015 (article)
• The Observatory on Social Media
• The Computational Propaganda Research Project (COMPROP) investigates the interaction of algorithms, automation and politics.
• Conversation AI is a collaborative research effort exploring ML as a tool for better discussions online.
• Engineering the public: Big data, surveillance and computational politics, Tufekci, 2014 (article)

AI AND DESIGN

Must:

• Machine Learning for Designers, Patrick Hebron, 2016
• Experience Design in the Machine Learning Era, Fabien Girardin, 2016 (article)

Optional:

• Rethinking Design Tools in the Age of Machine Learning, Patrick Hebron, 2017 (article)
• Design, Philosophy and A.I., Benjamin Bratton. 2016 (video)
• The UX of AI, Google
• DESIGN IN THE ERA OF THE ALGORITHM, JOSH CLARK, 2017 (article)
• Human-Centered Machine Learning, Jess Holbrook, 2017 (article)

AI AUDITING & SECURITY

Must:

• Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms, Christian Sandvig , Kevin Hamilton , Karrie Karahalios , & Cedric Langbort, 2014
• Ten simple rules for responsible big data research, Matthew Zook ,Solon Barocas, danah boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, 2017 (article)
• The Malicious Use of Artificial Intelligence, University of Oxford, Centre for the Study of Existential Risk, Future of Life Institute, OpenAI, Center for a New American Security, EFF

Optional:

• https://pair-code.github.io/facets/: Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets.
• Adversarial ML (video)
• Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggioa , Fabio Rolia
• Machine Deception: Paper Roundup, Tim Hwang, 2018 (article)
• Practical Techniques for Interpreting Machine Learning Models: Introductory Open Source Examples Using Python, H2O, and XGBoost, Patrick Hall, 2018
• Auditing Algorithms

More information

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