Discussions on Bias & Ethics

Stanford University HAI:​​​  ArXiv Monitor, full paper search engine tool with the goal to automatically and continuously track technical metrics from papers published on arXiv

Accenture:  Building digital trust: The role of data ethics in the digital age, (2016)

Accenture:  Informed Consent and Data in Motion, (2016)

​​AI Now Institute: AI Now 2017 Report

AI Now Institute: AI Now 2018 Report

AI Now Institute: AI Now 2019 Report

AI Now Institute: Disability, Bias, and AI

AI for Good: AI for Good Global Summit Summary, (2017)

Bathaee, Yavar: The Artificial Intelligence Black Box and the Failure of Intent and Causation, Harvard Journal of Law & Technology Volume 31, Number 2, (Spring 2018)

Baxter, Kathy:  is an Architect of Ethical AI Practice at Salesforce, develops research-informed best practice to educate Salesforce employees, customers, and the industry on the development of responsible AI.  She has an incredible and constantly updated list of ethics in AI research papers and articles in Salesforce's Einstein.ai blog.

Burrell, J.: How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, (2016)

Capgemini: Why Addressing Ethical Questions in AI will Benefit Organizations

Calo, Ryan: Artificial Intelligence Policy: A Primer and Roadmap, (August 8, 2017)

Chignard,Simon and Penicaud, Soizic:  “With great power comes great responsibility”: Keeping public sector algorithms accountable

Council of Europe: Discrimination, artificial intelligence, and algorithmic decision-making, (2018)

DARPA: Explainable Artificial Intelligence (XAI), (2016) 

Dwork, Cynthia; Hardt, Moritz; Pitassi, Toniann; Reingold, Omer; Zemel, Rich: Fairness Through Awareness, (29 November 2011)

Etlinger, Susan (Altimeter):  The Foundation of Responsible Artificial Intelligence

European Commission: Policy and Investment Recommendations for Trustworthy AI, (June 2019)

 

European Group on Ethics in Science & New Technologies: Statement on Artificial Intelligence, Robotics and ‘Autonomous’ Systems, (2018)

Expert Group Data Ethics:  Ethical Codex for Data- Based Value Creation, (2019)

Future of Privacy Forum: Unfairness by Algorithm: Distilling the Harms of Automated Decision-Making, (December 2017)

 

Hagerty, Alexa; Rubinov, Igor: Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligence, (18 July 2019)

House of Lords Select Committee on Artificial Intelligence:  AI in the UK: ready, willing and able?, (2017)

Jonathan Dodge, Q. Vera Liao, Yunfeng Zhang, Rachel K. E. Bellamy, Casey Dugan:  Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment, 2019

Kleinberg, Jon and Ludwig, Jens and Mullainathan, Sendhil and Sunstein, Cass R.: Discrimination in the Age of Algorithms, (February 5, 2019)

 

Kliegr,Tomáš; Bahník, Štěpán; Fürnkranz, Johannes A review of possible effects of cognitive biases on interpretation of rule-based machine learning models, (3 October 2019)

KPMG:  Controlling AI: The imperative for transparency and explainability, (June 2019)

Kroll, Joshua Alexander: Accountable Algorithms, (2015)

Leidner, Jochen L. and Vassilis Plachouras. Ethical by Design: Ethics Best Practices for Natural Language Processing, (2017)

 

Leslie, D. Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector, The Alan Turing Institute, (2019)

McKinsey & Company: Controlling Machine-learning Algorithms and Their Biases, (November 2017)

McKinsey Global Institute: Notes from the AI frontier: Tackling bias in AI (and in humans), (June 2019)

Mittelstadt,Brent Daniel: Allo,Patrick; Taddeo, Mariarosaria; ,Wachter, Sandra; Floridi, Luciano: The Ethics of Algorithms: Mapping the Debate, (2016)

Osoba, Osonde A., Benjamin Boudreaux, Jessica Saunders, J. Luke Irwin, Pam A. Mueller, and Samantha Cherney, Algorithmic Equity: A Framework for Social Applications. Santa Monica, CA: RAND Corporation, (2019)

PWC: Ethical AI: Tensions and trade-offs, (11 Jun 2019)

Rahwan, I., Cebrian, M., Obradovich, N. et al:  Machine behaviour, Nature 568, (24 April 2019)

 

RAND: The Risks of Bias and Errors in Artificial Intelligence, (2017)

Russell, Stuart: Provably Beneficial Artificial Intelligence

Schwartz, Paul: Data Processing and GovernmentAdministration: The Failure of the American Legal Response to the Computer. Hastings Law Journal, Vol.43, (1991)

Shum, Harry: Removing AI Bias, (2020)

Trewin, Shari: AI Fairness for People with Disabilities: Point of View, (2018)

UK Government: Interim report: Review into bias in algorithmic decision-making, (25 July 2019)

Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, Peter Eckersley: Explainable Machine Learning in Deployment, (ACM FAT* 2020)

Verma, Sahil; Rubin, Julia: Fairness Definitions Explained, published in 2018 IEEE/ACM International Workshop on Software Fairness (FairWare)

Williams, B., Brooks, C., & Shmargad, Y.: How Algorithms Discriminate Based on Data They Lack: Challenges, Solutions, and Policy Implications,  Journal of Information Policy, 8, (2018)

Zevenbergen, Bendert and Mittelstadt, Brent and Véliz, Carissa and Detweiler, Christian and Cath, Corinne and Savulescu, Julian and Whittaker, Meredith: Philosophy Meets Internet Engineering: Ethics in Networked Systems Research. (GTC Workshop Outcomes Paper) (September 29, 2015)

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