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Responsible design of AI systems is a shared goal across HCI and AI communities.
InterpretML: A Unified Framework for Machine Learning Interpretability
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Building a Scientific Community: The Need for Replication
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Using thematic analysis in psychology
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Principles to Practices for Responsible AI: Closing the Gap
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Using community-based participatory research to address health disparities
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From Mice to Men-24 Years of Evaluation in CHI. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , Vol. 10. ACM New York, NY
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Evaluating User Interface Systems Research. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology . ACM, Newport Rhode Island USA
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Developing and evaluating complex interventions: the new Medical Research Council guidance
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Usability Evaluation Considered Harmful (Some of the Time). In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . ACM, Florence Italy
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Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, and Ann Yuan. 2020 · 2008
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What is this thing called ’efficacy’?
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A social theory of learning
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The Limitations of Randomized Controlled Trials in Predicting Effectiveness
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Privacy by Design: essential for organizational accountability and strong business practices
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The weirdest people in the world?
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Racial profiling in decisions to search: A preliminary analysis using propensity-score matching
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Defining a Catalog of Indicators to Support Process Performance Analysis
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Evaluation and measurement of software process improvement—a systematic literature review
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Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference . 214–226
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Toward a comparative sociology of valuation and evaluation
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RepliCHI SIG: From a panel to a new submission venue for replication
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Scaling up what works: Experimental evidence on external validity in Kenyan education
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Replichi: the workshop
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Knowing by Doing: Action Research as an Approach to HCI
Gillian R Hayes. 2014 · 2014
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Ways of Knowing in HCI . Vol. 2
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Auditing algorithms: Research methods for detecting discrimination on internet platforms
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A Practical Guide to Controlled Experiments of Software Engineering Tools with Human Participants
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Local standards for sample size at CHI. In Proceedings of the 2016 CHI conference on human factors in computing systems . 981–992
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Comparison of convenience sampling and purposive sampling
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A Confidence-Based Approach for Balancing Fairness and Accuracy
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Impact evaluation in practice
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Effects of sensemaking translucence on distributed collaborative analysis. In Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work & Social Computing . 288–302
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Integrating community-based participatory research and informatics approaches to improve the engagement and health of underserved populations
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An Ethical Framework for Evaluating Experimental Technology
Ibo van de Poel. 2016 · 2016
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Randomized Controlled Trials: How Can We Know “What Works”?
Nick Cowen, Baljinder Virk, Stella Mascarenhas-Keyes, and Nancy Cartwright. 2017 · 2017
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Body-worn cameras and citizen interactions with police officers: Estimating plausible effects given varying compliance levels
Eric C Hedberg, Charles M Katz, and David E Choate. 2017 · 2017
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Thematic Analysis: Striving to Meet the Trustworthiness Criteria
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Flipper: A Systematic Approach to Debugging Training Sets. In Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics (Chicago, IL, USA) (HILDA’17, Article 5) . Association for Computing Machinery, New York, NY, USA, 1–5
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Data Ethics Framework
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A Reductions Approach to Fair Classification. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80) , Jennifer Dy and Andreas Krause (Eds.). PMLR, 60–69
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Algorithmic Accountability Policy Toolkit
AI Now Institute. 2018 · 2018
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Data statements for natural language processing: Toward mitigating system bias and enabling better science
Emily M Bender and Batya Friedman. 2018 · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency . PMLR, 77–91
Joy Buolamwini and Timnit Gebru. 2018 · 2018
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The trials of evidence-based practice in education: A systematic review of randomised controlled trials in education research 1980–2016
Paul Connolly, Ciara Keenan, and Karolina Urbanska. 2018 · 2018
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The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel. 2018 · 2018
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Designs for the Pluriverse
Arturo Escobar. 2018 · 2018
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Artificial Intelligence Faces Reproducibility Crisis
Matthew Hutson. 2018 · 2018
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Evaluation Strategies for HCI Toolkit Research. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18, Paper 36) . Association for Computing Machinery, New York, NY, USA, 1–17
David Ledo, Steven Houben, Jo Vermeulen, Nicolai Marquardt, Lora Oehlberg, and Saul Greenberg. 2018 · 2018
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The preregistration revolution
Brian A Nosek, Charles R Ebersole, Alexander C DeHaven, and David T Mellor. 2018 · 2018
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Algorithmic Impact Assessments: A Practical Framework for Public Agency
Dillon Reisman, Jason Schultz, Kate Crawford, and Meredith Whittaker. 2018 · 2018
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Aequitas: A Bias and Fairness Audit Toolkit
Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T Rodolfa, and Rayid Ghani. 2018 · 2018
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Randomised controlled trials (RCTs) in education research–methodological debates, questions, challenges
Ben Styles and Carole Torgerson. 2018 · 2018
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Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18, Paper 440) . Association for Computing Machinery, New York, NY, USA, 1–14
Michael Veale, Max Van Kleek, and Reuben Binns. 2018 · 2018
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Fairness Testing: A Comprehensive Survey and Analysis of Trends
Zhenpeng Chen, Jie M Zhang, Max Hort, Federica Sarro, and Mark Harman. 2022 · 2022
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Behavioral Use Licensing for Responsible AI. In 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 778–788
Danish Contractor, Daniel McDuff, Julia Katherine Haines, Jenny Lee, Christopher Hines, Brent Hecht, Nicholas Vincent, and Hanlin Li. 2022 · 2022
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A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22, Article 73) . Association for Computing Machinery, New York, NY, USA, 1–18
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Interactive Model Cards: A Human-Centered Approach to Model Documentation. In 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 427–439
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FactSheets: Increasing trust in AI services through supplier’s declarations of conformity
Matthew Arnold, Rachel KE Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilović, Ravi Nair, K Natesan Ramamurthy, Alexandra Olteanu, David Piorkowski, et al · 2019
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Judgment Call the Game: Using Value Sensitive Design and Design Fiction to Surface Ethical Concerns Related to Technology. In Proceedings of the 2019 on Designing Interactive Systems Conference (San Diego, CA, USA) (DIS ’19) . Association for Computing Machinery, New York, NY, USA, 421–433
Stephanie Ballard, Karen M Chappell, and Kristen Kennedy. 2019 · 2019
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AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilović, et al · 2019
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Consequence Scanning Manual Version 1
Sam Brown, Rachel Coldicutt, Hannah Kitcher, James Barclay, and Josh Kwan. 2019 · 2019
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Deon: An ethics checklist for data scientists
DrivenData. 2019 · 2019
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A comparative study of fairness-enhancing interventions in machine learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA) (FAT* ’19) . Association for Computing Machinery, New York, NY, USA, 329–338
Sorelle A Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P Hamilton, and Derek Roth. 2019 · 2019
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Deconstructing community-based collaborative design: Towards more equitable participatory design engagements
Christina Harrington, Sheena Erete, and Anne Marie Piper. 2019 · 2019
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Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse
Anna Lauren Hoffmann. 2019 · 2019
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Anamaria Crisan, Margaret Drouhard, Jesse Vig, and Nazneen Rajani. 2022 · 2022
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Dealing with disagreements: Looking beyond the majority vote in subjective annotations
Aida Mostafazadeh Davani, Mark Díaz, and Vinodkumar Prabhakaran. 2022 · 2022
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Wesley Hanwen Deng, Bill Boyuan Guo, Alicia Devos, Hong Shen, Motahhare Eslami, and Kenneth Holstein. 2022a · 2022
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Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits. In 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 473–484
Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Zhiwei Steven Wu, Kenneth Holstein, and Haiyi Zhu. 2022b · 2022
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On Measures of Biases and Harms in NLP. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022 . 246–267
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Toward User-Driven Algorithm Auditing: Investigating users’ strategies for uncovering harmful algorithmic behavior. In CHI Conference on Human Factors in Computing Systems . 1–19
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Tackling Documentation Debt: A Survey on Algorithmic Fairness Datasets. In Equity and Access in Algorithms, Mechanisms, and Optimization (Arlington, VA, USA) (EAAMO ’22, Article 2) . Association for Computing Machinery, New York, NY, USA, 1–13
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Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation
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All the tools, none of the motivation: Organizational culture and barriers to responsible AI work. In Cultures in AI/AI in Culture: a NeurIPS 2022 Workshop
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End-User Audits: A System Empowering Communities to Lead Large-Scale Investigations of Harmful Algorithmic Behavior
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Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning. (Jan. 2022)
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Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems
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Responsible AI Pattern Catalogue: A Multivocal Literature Review
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A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication. In 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 199–212
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Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support
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Reliable and Safe Use of Machine Translation in Medical Settings. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, South Korea)(FAccT’22). Association for Computing Machinery, New York, NY, USA
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Using model cards for ethical reflection: a qualitative exploration. In Proceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems (Diamantina, Brazil) (IHC ’22, Article 29) . Association for Computing Machinery, New York, NY, USA, 1–11
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Obligations to assess: Recent trends in AI accountability regulations
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Cultural Incongruencies in Artificial Intelligence
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A Dynamic Decision-Making Framework Promoting Long-Term Fairness. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (Oxford, United Kingdom) (AIES ’22) . Association for Computing Machinery, New York, NY, USA, 547–556
Bhagyashree Puranik, Upamanyu Madhow, and Ramtin Pedarsani. 2022 · 2022
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Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI. In 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 1776–1826
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The Fallacy of AI Functionality. In 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 959–972
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Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction
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The Model Card Authoring Toolkit: Toward Community-centered, Deliberation-driven AI Design. In 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 440–451
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SHIFTing Artificial Intelligence to Be Responsible in Healthcare: A Systematic Review
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Participation Is not a Design Fix for Machine Learning. In Equity and Access in Algorithms, Mechanisms, and Optimization (Arlington, VA, USA) (EAAMO ’22, Article 1) . Association for Computing Machinery, New York, NY, USA, 1–6
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From Ethical Artificial Intelligence Principles to Practice: A Case Study of University-Industry Collaboration. In 2022 International Joint Conference on Neural Networks (IJCNN) . 1–9
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Measuring representational harms in image captioning. In 2022 ACM Conference on Fairness, Accountability, and Transparency . 324–335
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Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications
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Algorithm Tips: resources and leads for investigating algorithms in society
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Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools. In Equity and Access in Algorithms, Mechanisms, and Optimization . ACM, Boston MA USA
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Artificial intelligence and the future of teaching and learning
MA Cardona, RJ Rodríguez, and K Ishmael. 2023 · 2023
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A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms. In 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) . IEEE, Raleigh, NC, USA
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Challenging anti-Black linguistic racism in schools amidst the ‘what works’ agenda
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