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A collection of media and additional resources related to AI and technology ethics

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Contributors Forks Stargazers Issues

AI Ethics Resources

A collection of media, content, and additional resources related to AI and technology ethics

Table of Contents
  1. Websites
  2. Talks
  3. Podcasts and YouTube Channels
  4. Articles and White Papers
  5. Books
  6. Conferences and Events
  7. Published Research
  8. Contact

Websites

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Talks

Podcasts, YouTube Channels, & Documentaries

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Articles and White Papers

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Books

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Conferences and Events

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Published Research

Note: this is not an exhaustive list by any means and reflects some of my own research interests. Feel free to request additional Key Topics tags!

Authors Title Year Key Topics
Ananny & Crawford Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability 2016 Algorithmic Fairness
Aroyo & Welty Truth is a Lie: Crowd Truth and the Seven Myths of Human Annotation 2015 Data NLP
Aroyo et al. Data Excellence for AI: Why Should You Care 2021 Data
Barocas, S. Data Mining and the Discourse on Discrimination 2014 Data
Barocas & Nissenbaum Big Data's End Run Around Procedural Privacy Protections 2014 Data Privacy Policy
Bartl et al. Unmasking Contextual Stereotypes: Measuring and Mitigating BERT's Gender Bias 2020 NLP LLM Bias Mitigation
Basta et al. Evaluating the Underlying Gender Bias in Contextualized Word Embeddings 2019 NLP LLM
Berk et al. Fairness in Criminal Justice Risk Assessments: The State of the Art 2017 Algorithmic Fairness
Bender & Friedman Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science 2018 Guidelines & Recommendations
Bender et al. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 2021 NLP LLM
Bertrand & Mullainathan Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination 2004 Social Justice
Birhane, A. Algorithmic injustice: a relational ethics approach 2021 Algorithmic Fairness Feminism
Bolukbasi et al. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings 2016 NLP Bias Mitigation LLM
Brandusescu & Reia Artificial Intelligence in the City: Building Civic Engagement and Public Trust 2022 Data Policy
Brey et al. An ethical framework for the development and use of AI and robotics technologies 2020 Guidelines & Recommendations
Burrell, J. How the machine "thinks": Understanding opacity in machine learning algorithms 2016 XAI
Buolamwini & Gebru Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification 2018 Computer Vision Algorithmic Fairness Data
Caliskan et al. Semantics derived automatically form language corpora contain human-like biases 2017 NLP
Chen et al. Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals 2017 Data Privacy
Cho et al. DALL-Eval: Probing the Reasoning Skills and Social Biased of Text-to-Image Generative Transformers 2022 Computer Vision
Citron & Pasquale The Scored Society: Due Process for Automated Predictions 2014 Data
Cooper, J. Separation Anxiety 2017 Privacy Policy
Crawford & Schultz Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms 2014 Data Privacy Policy
Danks, D. The Value of Trustworthy AI 2019 Guidelines & Recommendations
Davis & Osoba Privacy Preservation in the Age of Big Data 2016 Privacy Data
Devinney et al. Theories of "Gender" in NLP Bias Research 2022 NLP Bias Mitigation
Diakopoulos, N. Algorithmic Accountability: Journalistic investigation of computational power structures 2014 Algorithmic Fairness
Dietvorst et al. Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err 2015 Policy
Dignum et al. On the importance of AI research beyond disciplines 2023 Guidelines & Recommendations
Dillon, S. The Eliza Effect and Its Dangers: From Demystification to Gender Critique 2020 Virtual Assistants Feminism
Doshi-Velez & Kim Towards A Rigorous Science of Interpretable Machine Learning 2017 XAI
Drosou et al. Diversity in Big Data: A Review 2017 Data
Du et al. VOS: Learning What You Don't Know by Virtual Outlier Synthesis 2022 XAI
Dwork et al. Fairness Through Awareness 2011 Algorithmic Fairness
Edwards & Veale Slave to the Algorithm? Why a "Right to an Explanation" is Probably Not the Remedy You Are Looking For 2017 XAI Algorithmic Fairness Policy
Fazelpour & De-Arteaga Diversity in Sociotechnical Machine Learning Systems 2021 Algorithmic Fairness
Floridi & Cowls A Unified Framework of Five Principles for AI in Society 2021 Guidelines & Recommendations
Friedler et al. On the (im)possibility of fairness 2016 Algorithmic Fairness
Gebru et al. Datasheets for Datasets 2021 Data XAI
Gillespie, T. The Relevance of Algorithms 2014 Algorithmic Fairness XAI
Grgić-Hlača et al. The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making 2016 Algorithmic Fairness Guidelines & Recommendations
Griesbach et al. Algorithmic Control in Platform Food Delivery Work 2019 Algorithmic Fairness
Griffin et al. The ethical agency of AI developers 2023 Algorithmic Fairness Policy
Grimmelmann & Westreich Incomprehensible Discrimination 2017 Data Algorithmic Fairness
Gonen & Goldberg Lipstick on a Pig: Debiasing Methods Cover up Systemic Gender Biases in Word Embeddings But Do Not Remove Them 2019 NLP LLM
Goodman, B. Economic Models of (Algorithmic) Discrimination 2016 Algorithmic Fairness
Guo & Caliskan Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases 2021 NLP LLM
Haggerdy, K. Methodology as a Knife Fight: The Process, Politics, and Paradox of Evaluating Surveillance 2009 Policy Surveillance
Hanna et al. Towards a Critical Race Methodology in Algorithmic Fairness 2019 Critical Race Theory Algorithmic Fairness
Hardt et al. Equality of Opportunity in Supervised Learning 2016 Supervised Learning Algorithmic Fairness
Helveston, M. Consumer Protection in the Age of Big Data 2016 Data Privacy
Hine & Floridi Artificial Intelligence with American Values and Chinese Characteristics: A Comparative Analysis of American and Chinese Governmental AI Policies 2022 Policy
Hutchinson & Mitchell 50 Years of Test (Un)fairness: Lessons for Machine Learning 2019 Algorithmic Fairness
Hutchinson et al. Towards Accountability for Machine Learning Datasets: Practices from Sofftware Engineering and Infrastructure 2021 Data
Jacobs & Wallach Measurement and Fairness 2021 Algorithmic Fairness
Jacovi et al. Diagnosing AI Explanation Methods with Folk Concepts of Behavior 2022 XAI
Jobin et al. The global landscape of AI ethics 2019 Literature Review
Jones, M. The right to a human in the loop: Political constructions of computer automation and personhood 2017 Policy Algorithmic Fairness
Joseph et al. Fairness in Learning: Classic and Contextual Bandits 2016 Algorithmic Fairness RL
Jung et al. Simple Rules for Complex Decisions 2017 Guidelines & Suggestions
Kaptein & Eckles Selecting Effective Means to Any End; Futures and Ethics of Persuasion Profiling 2010 Policy
Kerr et al. Expectations of artificial intelligence and the performativity of ethics: implications for communication governance 2020 Policy
Khan & Hanna The Subjects and Stages of AI Dataset Development: A Framework for Dataset Accountability 2022 Data Guidelines & Recommendations
Kleinberg et al. Inherent Trade-Offs in the Fair Determination of Risk Scores 2016 Algorithmic Fairness
Kochelek, D. Data Mining and Antitrust 2009 Data Privacy
Kosinski et al. Private traits and attributes are predictable from digital records of human behavior 2013 Privacy Data
Kroll et al. Accountable Algorithms 2017 Algorithmic Fairness
Kurita et al. Measuring Bias in Contextualized Word Representations 2019 NLP LLM
Lee, M. Understanding Perceptions of Algorithmic Decisions: Fairness, Trust, and Emotion in Response to Algorithmic Management 2018 Algorithmic Fairness
Lippert-Rasmussen, K. "We are all Different": Statistical Discrimination and the Right to be Treated as in Individual 2010 Data
Lipton, Z. The Mythos of Model Interpretability 2016 XAI
Liu et al. Privacy and Security Issues in Deep Learning: A Survey 2021 Privacy
Matthews et al. Gender Bias in Natural Language Processing Across Human Languages 2021 NLP LLM
McGregor, S. Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database 2020 Datasets
Mehrabi et al. A Survey on Bias and Fairness in Machine Learning 2022 Algorithmic Fairness
Miceli et al. Documenting Data Production Processes: A Participatory Approach for Data Work 2022 Data Guidelines & Recommendations
Mitchell et al. Model Cards for Model Reporting 2019 Data XAI
Mueller, M. A Critique of 'Surveillance Capitalism' Thesis: Toward a Digital Political Economy 2022 Guidelines & Recommendations
Nadeem et al. StereoSet: Measuring stereotypical bias in pretrained language models 2020 NLP LLM Datasets
Nangia et al. CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models 2020 NLP LLM Datasets
Prabhakaran et al. A Human Rights-Based Approach to Responsible AI 2022 Algorithmic Fairness Guidelines & Recommendations
Prabhu & Birhane Large Datasets: A Pyrrhic Win for Computer Vision? 2020 Computer Vision Data
Raji & Buolamwini Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products 2019 Algorithmic Fairness
Raji et al. You Can't Sit With Us: Exclusionary Pedagogy in AI Ethics Education 2021 Algorithmic Fairness Policy Guidelines & Recommendations
Ryan & Stahl Artificial intelligence ethics guidelines for developers and users: clarifying their content and normative implications 2020 Guidelines & Recommendations
Sandvig et al. Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms 2014 Algorithmic Fairness Guidelines & Suggestions
Selbst et al. Fairness and Abstraction in Sociotechnical Systems 2019 Algorithmic Fairness
Sloane et al. Participation is not a Design Fix for Machine Learning 2020 Guidelines & Recommendations
Suresh et al. A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle 2021 Algorithmic Fairness
Swedloff, R. Risk Classification's Big Data (R)evolution 2014 Data Algorithmic Fairness
Taddeo & Floridi How AI can be a force for good 2018 Guidelines & Recommendations
Tene & Polonetsky Taming the Golem: Challenges of Ethical Algorithmic Decision Making 2017 Algorithmic Fairness
Thylstrup, N. The ethics and politics of data sets in the age of machine learning: deleting traces and encountering remains 2022 Data Privacy
Tsamados et al. The ethics of algorithms: key problems and solutions 2021 Algorithmic Fairness
Vakkuri et al. AI Ethics in Industry: A Research Framework 2019 Guidelines & Suggestions XAI
Webster et al. Measuring and Reducing Gendered Correlations in Pre-trained Models 2020 NLP Bias Mitigation LLM
Whitman, M. "We called that a behavior": The making of institutional data 2020 Data
Wu & Zhang Responses to Critiques on Machine Learning of Criminality Perceptions 2017 Algorithmic Fairness Computer Vision
Zarsky, T. The Trouble with Algorithmic Decisions: An Analytic Road Map to Examine Efficiency and Fairness in Automated and Opaque Decision Making 2015 Privacy XAI
Zarsky, T. Transparent Predictions 2013 Data Privacy Policy
Zhang & Zhao Online Decision Trees with Fairness 2020 Algorithmic Fairness
Zhang et al. Mitigating Unwanted Biases with Adversarial Learning 2018 Bias Mitigation
Zhao et al. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints 2017 NLP Bias Mitigation

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Jesse Shanahan - @enceladosaurus - [email protected]

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