Ethics resources

• Books
• Academic papers
• Reports
• Media articles
• Ethics guidelines and frameworks
• Tools
• Book clubs
• Courses
• Misc.

BOOKS

• Weapons of math destruction: How Big Data Increases Inequality and Threatens Democracy Cathy O'Neill (2016) Link to site

• Bit by Bit: Social Research in the Digital Age Matthew Salgnanik (2017) Read online

• We Are Data: Algorithms and the Making of Our Digital Selves John Cheney-Lippold (2018)

• Automating Inequality: How High-Tech Tools Profile, Police and Punish the Poor Virginia Eubanks (2018)

• Algorithms of Oppression: How Search Engines Reinforce Racism Safiya Noble 2018

ACADEMIC PAPERS

• Big Data’s Disparate Impact Barocas & Selbst (2016), from NYU course read here

• Algorithmic Transparency for the Smart City Brauneis & Goodman (2017), from NYU course read here

• Accountable Algorithms Kroll et al., UPenn Law Review (2017), from NYU course, the view from the legal side read here

• Bias in computer systems Frieman & Nissembaum (ACM) (1996) read here

• Datasheets for Datasets Microsoft (2018) read here

• A Harm-Reduction Frameworkfor Algorithmic Fairness Berkman Klein Center (2018) read here

• The ethics of algorithms: Mapping the debate Mittlestadt et al. (Big Data & Society) (2016) read here

• Algorithmic Decision Making and the Cost of Fairness Corbett-Davies et. al (2017) read here

• Wrong side of the tracks: Big Data and Protected Categories Simon DeDeo (2016) de-biasing vs accuracy read here

• Envisioning Systemic Effects on Persons and Society Throughout Interactive System Design Nathan et. al (2008) read here

REPORTS

• Future data-driven technologies and the implications for use of patient data Academy of Medical Sciences (2018) read here

• White House Report on Big Data “Big Data: Seizing Opportunities, Preserving Values” White House (2014) read here

• Draft Ethics guidelines for trustworthy AI European Commission’s High-Level Expert Group on Artificial Intelligence (AI HLEG) Floridi led (2018/2019?) read here

• algo:aware - State-of-the-Art Report | Algorithmic decision-making European Commission’s Directorate - General for Communications Networks, Content and Technology (2019) read here / broken link?

MEDIA / ARTICLES

• Regulating AI in the era of big tech Melody Guan (Stanford) / The Gradient 2018 read here

• Democrats aren’t buying a proposal for big tech to write its own privacy rules The Verge 2019, response to the ITIF report read here

• Algorithmic Justice League ongoing, newsletter access here

• AI can be sexist and racist — it’s time to make it fair Zou & Schiebinger (Nature 2018) read here

• The Mythos of Model Interpretability Zachary C. Lipton (2018) read here

• Notes on Algorithmic decision making and the cost of fairness Gerhard Schimpf (2018) relates to the above read here

• A Survey of Value Sensitive Design Methods Friedman et al. (2017) read here

• AI Nationalism Ian Hogarth (2018) geo-political and economic take on ML and AI read here

• Some Background on Our Views Regarding Advanced Artificial Intelligence Open Philanthropy Project 2016 read here

• What Our Tech Ethics Crisis Says About the State of Computer Science Education Casey Fiesler (2019) addendum to the previous Ethics Curricula list read here

TOOLS

• AI Fairness 360 Open Source Toolkit, IBM access here
• Ethics and Algorithms toolkit, Center for Gov access here
• Machine Ethics Toolkit, James Zhou download here

ETHICS GUIDELINES AND FRAMEWORKS

For reference only, DK does not officially sign off on any of those! In fact there are some that we strongly disagree with, but it is all interesting.

• A Grand Bargain on Data Privacy Legislation for America, ITIF (Information Technology and Innovation Foundation) (2019) read here

• Rigour Respect Responsibility, UK Government Office for Science (2007) read here

• Axon AI and Policing Technology Ethics Board, Axon read here

• Ethics Framework, Machine Intelligence Garage (2019) read here

• AI4People - An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations, Floridi et al (2018) access here

• Introducing Unity’s Guiding Principles for Ethical AI, Unity Technologies (2018) read here

• First, Do No Harm. Ethical Guidelines for Applying Predictive Tools within Human Services, Metrolab (2017) read here

• An Ethical Toolkit for Engineering/Design Practice, Markkula Center for Applied Ethics (2018) read here

• Tenets, The Partnership on AI read here

• Ten simple rules for responsible big data research, Zook et. al (2017) read here

• UK National Statistician’s Data Ethics Advisory Committee principles, UK Stats Authority access here

• Universal principles of data ethics Accenture (2016)read here

• Ethical Decision-Making and Internet Research AoIR (Association of Internet Researchers) read here

• AI Policy Principles, ITI (Information Technology Industry Council) read here

BOOK CLUBS and reading lists

• AI Safety reading group reading list
• Artificial Intelligence, Global Catastrophic Risk Institute reading list
• AI safety syllabus, 80,000 hours reading list
• AI safety resources, Victoria Krakovna reading list
• AI Ethics Resources, Rachel Thomas resources list

COURSES

• Artificial Intelligence - Philosophy, Ethics, and Impact [CS122] Stanford (2014) Link to course site

• Ethical Issues Surrounding Artificial Intelligence Systems and Big Data UMass (2018) Link to GitHub

• Special Topics in Data Science: Responsible Data Science [DS-GA 3001.009] NYU (2019) Link to GitHub

• An introduction to data ethics Santa Clara University (2017?) Link to pdf

• The Ethics and Governance of AI MIT (2018) Link to course site

• [META!] Tech Ethics Curricula: A Collection of Syllabi Casey Fiesler (2018) Link to Medium article about the resource and Link to the spreadsheet itself

MISC.

• podcast: Potential Risks from Advanced Artificial Intelligence:
  The Philanthropic Opportunity on how OpenAI is developing real solutions to the ‘AI alignment problem’, and his vision of how humanity will progressively hand over decision-making to AI systems Paul Christiano / 80,000 hours listen here
• podcasts DataBites podcast by Data & Society Data & Society listen here
• video 21 Fairness Definitions, Princeton (2018), 1h youtube
• video Machine Learning and Human Bias, Google (2017) 2.5h youtube