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Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems.
“On the Legal Compatibility of Fairness Definitions”
Alice Xiang and Inioluwa Raji · 1912
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“Do Artifacts Have Politics?”
Langdon Winner · 1980
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“Transforming Qualitative Data: Description, Analysis, and Interpretation”
Harry. Wolcott · 1994
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“The Audit Society”
Michael Power · 1999
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“The Ethnography of Infrastructure”
Susan Star · 1999
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“Generalizing Generalizability in Information Systems Research”
Allen. Lee and Richard. Baskerville · 2003
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“Materializing Morality: Design Ethics and Technological Mediation”
Peter-Paul Verbeek · 2006
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“Analysing and Assessing Accountability: A Conceptual Framework 1”
Mark Bovens · 2007
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“Standards and Their Stories: How Quantifying, Classifying, and Formalizing Practices Shape Everyday Life”
Martha Lampland and Susan Star · 2009
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“Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms”
Christian Sandvig, Kevin Hamilton, Karrie Karahalios and Cedric Langbort · 2014
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“Constructing Grounded Theory”, Introducing Qualitative Methods
Kathy Charmaz · 2014
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“YouTube Regrets”, 2021
Mozilla Foundation · 2014
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“ModelTracker: Redesigning Performance Analysis Tools for Machine Learning”
Saleema Amershi et al · 2015
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“Machine Bias”
Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner · 2016
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“Regulation (EU) 2016/679 of the European Parliament and of the Council. of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation)”, 2016
European Parliament and Council of the European Union · 2016
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“A Unified Approach to Interpreting Model Predictions”
Scott. Lundberg and Su-In Lee · 2017
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“Community Engagement Toolkit for Planning”, 2017
Queensland Government · 2017
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“Discovery of Grounded Theory: Strategies for Qualitative Research”
Barney Glaser and Anselm Strauss · 2017
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“Trust but Verify: A Guide to Algorithms and the Law”
Deven. Desai and Joshua. Kroll · 2017
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“A Qualitative Exploration of Perceptions of Algorithmic Fairness”
Allison Woodruff, Sarah. Fox, Steven Rousso-Schindler and Jeffrey Warshaw · 2018
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“AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias”
Rachel.. Bellamy et al · 2018
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“Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification”
Joy Buolamwini and Timnit Gebru · 2018
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“The Mythos of Model Interpretability: In Machine Learning, the Concept of Interpretability Is Both Important and Slippery.”
Zachary. Lipton · 2018
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“Algorithmic Accountability Act of 2022”, 2019
Yvette. Clarke · 2019
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“Designing Theory-Driven User-Centric Explainable AI”
Danding Wang, Qian Yang, Ashraf Abdul and Brian. Lim · 2019
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“Doing More to Protect Against Discrimination in Housing, Employment and Credit Advertising”
Sheryl Sandberg · 2019
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“Facebook Settles Civil Rights Cases by Making Sweeping Changes to Its Online Ad Platform | ACLU of Northern CA”
Galen Sherwin and Esha Bhandari · 2019
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“Fairness and Abstraction in Sociotechnical Systems”
Andrew. Selbst et al · 2019
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“Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?”
Kenneth Holstein et al · 2019
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“Model Cards for Model Reporting”
Margaret Mitchell et al · 2019
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“Sandvig v. Barr — Challenge to CFAA Prohibition on Uncovering Racial Discrimination Online”
American Civil Liberties Union · 2019
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“Toward Algorithmic Accountability in Public Services: A Qualitative Study of Affected Community Perspectives on Algorithmic Decision-making in Child Welfare Services”
Anna Brown et al · 2019
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“A New Way We’re Fighting Discrimination on Airbnb - Resource Centre”
Airbnb · 2020
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“Algorithmic Equity Toolkit”, 2020
Bissan Barghouti et al · 2020
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“Banning Government Use of Face Recognition Technology: 2020 Year in Review”
Nathan Sheard · 2020
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“Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing”
Inioluwa Raji et al · 2020
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“Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI”
Michael. Madaio, Luke Stark, Jennifer Wortman and Hanna Wallach · 2020
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“Contemporary Housing Discrimination: Facebook, Targeted Advertising, and the Fair Housing Act”
Chandler Spinks · 2020
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“Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach”
Upol Ehsan and Mark. Riedl · 2020
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“Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning”
Harmanpreet Kaur et al · 2020
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“No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML”
Alison Smith-Renner et al · 2020
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“Ongoing Face Recognition Vendor Test (FRVT) Part 6B: Face Recognition Accuracy with Face Masks Using Post-COVID-19 Algorithms”, 2020
Mei Ngan, Patrick Grother and Kayee Hanaoka · 2020
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“Participatory Approaches to Machine Learning”, International Conference on Machine Learning Workshop, 2020
Kulynych et al · 2020
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“Problems with Shapley-value-based Explanations as Feature Importance Measures”
I. Kumar, Suresh Venkatasubramanian, Carlos Scheidegger and Sorelle. Friedler · 2020
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“Questioning the AI: Informing Design Practices for Explainable AI User Experiences”
Q. Liao, Daniel Gruen and Sarah Miller · 2020
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URL: https://casetext.com/case/sandvig-v-barr
“Sandvig v. Bar”, 2020 · 2020
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“The Algorithmic Ecology: An Abolitionist Tool for Organizing Against Algorithms”
Stop LAPD Spying Coalition and Free Radicals · 2020
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“WES: Agent-based User Interaction Simulation on Real Infrastructure”
John Ahlgren et al · 2020
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“A Local Law to Amend the Administrative Code of the City of New York, in Relation to Automated Employment Decision Tools”, 2021
Laurie. Cumbo et al · 2021
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“A Silicon Valley Love Triangle: Hiring Algorithms, Pseudo-Science, and the Quest for Auditability”
Mona Sloane, Emanuel Moss and Rumman Chowdhury · 2021
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“AI, Algorithmic and Automation Incident and Controversy Repository (AIAAIC)”, 2021
Charlie Pownall · 2021
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“Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies”
Briana Vecchione, Karen Levy and Solon Barocas · 2021
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“An Overview of National AI Strategies and Policies”, 2021
“California Bill Proposes Regulating AI at State Level”
Billy Perrigo · 2023
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“Cataloguing LLM Evaluations”, 2023
Infocomm Media Development Authority and AI Verify Foundation · 2023
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URL: https://www.crunchbase.com
“Crunchbase”, 2023 · 2023
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“Delegated Regulation on Data Access Provided for the Digital Services Act: Response to the Call for Evidence DG CNECT-CNECT F2 by the European Commission”, 2023
Ulrike Klinger and Jakob Ohme · 2023
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“Digital Services Act: Summary Report on the Call for Evidence on the Delegated Regulation on Data Access”, 2023
Paddy Leerssen · 2023
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“Ethical AI Frameworks, Guidelines, Toolkits”
Merve Hickock · 2023
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Laura Galindo, Karine Perset and Francesca Sheeka · 2021
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“Auditing Algorithms: Understanding Algorithmic Systems from the Outside In”
Danaë Metaxa et al · 2021
Cited alongside, same era.
“Bias Preservation in Machine Learning: The Legality of Fairness Metrics Under EU Non-Discrimination Law”
Sandra Wachter, Brent Mittelstadt and Chris Russell · 2021
Cited alongside, same era.
“Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors”
Hong Shen, Alicia DeVos, Motahhare Eslami and Kenneth Holstein · 2021
Cited alongside, same era.
Meatspace Press, 2021
“Fake AI” · 2021
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In Gig Economy Data Hub , 2021
“Gig Economy Data Hub” · 2021
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“Governing Algorithmic Systems with Impact Assessments: Six Observations”
Elizabeth Watkins et al · 2021
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“Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence”
Joseph. Biden · 2023
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In FACETS , 2023
“Facets - Know Your Data” · 2023
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“Federal AI Legislation: An Analysis of Proposals from the 117th Congress Relevant to Generative AI Tools”, 2023
Anna Lenhart · 2023
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“GPT-4 Technical Report”
OpenAI · 2023
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“It’s about Power: What Ethical Concerns Do Software Engineers Have, and What Do They (Feel They Can) Do about Them?”
David Widder, Derrick Zhen, Laura Dabbish and James Herbsleb · 2023
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“On Selective, Mutable and Dialogic XAI: A Review of What Users Say about Different Types of Interactive Explanations”
Astrid Bertrand et al · 2023
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“Risky Analysis: Assessing and Improving AI Governance Tools”, 2023
Kate Kaye and Pam Dixon · 2023
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“Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics”
Richmond. Wong, Michael. Madaio and Nick Merrill · 2023
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In Selenium , 2023
“Selenium” · 2023
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“Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted Advertising”
Michelle. Lam et al · 2023
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“Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction”
Renee Shelby et al · 2023
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“The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements”
Michael Feffer, Nikolas Martelaro and Hoda Heidari · 2023
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“The Bureaucratic Challenge to AI Governance: An Empirical Assessment of Implementation at U.S. Federal Agencies”
Christie Lawrence, Isaac Cui and Daniel Ho · 2023
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“The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice”
Fernando Delgado, Stephen Yang, Michael Madaio and Qian Yang · 2023
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“The ROOTS Search Tool: Data Transparency for LLMs”
Aleksandra Piktus et al · 2023
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“Tracking Exposed Manifesto”
Claudio Agosti · 2023
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“Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice”
Wesley Deng et al · 2023
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“Why Everyone Is Mad about New York’s AI Hiring Law”
Tate Ryan-Mosley · 2023
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“Why We Need to Know More: Exploring the State of AI Incident Documentation Practices”
Violet Turri and Rachel Dzombak · 2023
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“A Safe Harbor for AI Evaluation and Red Teaming”
Shayne Longpre et al · 2024
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“A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations”
Glen Berman, Nitesh Goyal and Michael Madaio · 2024
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“AI and Algorithm Auditor Certification”, 2024
AI · 2024
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“AI Auditing: The Broken Bus on the Road to AI Accountability”
A. Birhane et al · 2024
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“AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference”
Arvind Narayanan and Sayash Kapoor · 2024
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“Auditing Work: Exploring the New York City Algorithmic Bias Audit Regime”
Lara Groves et al · 2024
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“Fairlearn: Assessing and Improving Fairness of AI Systems”
Hilde Weerts et al · 2024
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In The AI Safety Institute (AISI) , 2024
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“How UnitedHealth’s Playbook for Limiting Mental Health Coverage Puts Countless Americans’ Treatment at Risk”
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“Law and the Emerging Political Economy of Algorithmic Audits”
Petros Terzis, Michael Veale and Noëlle Gaumann · 2024
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“Mozilla Technology Fund (MTF)” · 2024
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“NTIA Artificial Intelligence Accountability Policy Report”, 2024
Ellen Goodman · 2024
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“Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability”
Lucas Wright et al · 2024
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“Public AI: Making AI Work for Everyone, by Everyone”, 2024
Nik Marda, Jasmine Sun and Mark Surman · 2024
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“Public Interest AI”
AI Action Summit · 2024
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“The Right to Audit and Power Asymmetries in Algorithm Auditing”
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