GitHub - enceladosaurus/ethics-resources: A collection of media and additional resources related to AI and technology ethics

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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Contact

Jesse Shanahan - @enceladosaurus - jess.c.shanahan@gmail.com

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