{"list":"other","total":67,"rows":[{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"1602.04938","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","created":"2016-02-16","cited_by_count":5070},{"id":"1908.09635","title":"A Survey on Bias and Fairness in Machine Learning","created":"2019-08-23","cited_by_count":1917},{"id":"1910.10045","title":"Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI","created":"2019-10-22","cited_by_count":1552},{"id":"1803.09010","title":"Datasheets for Datasets","created":"2018-03-23","cited_by_count":1386},{"id":"1810.03993","title":"Model Cards for Model Reporting","created":"2018-10-05","cited_by_count":1080},{"id":"1812.05239","title":"Improving fairness in machine learning systems: What do industry practitioners need?","created":"2018-12-13","cited_by_count":262},{"id":"2101.04719","title":"Expanding Explainability: Towards Social Transparency in AI systems","created":"2021-01-12","cited_by_count":124},{"id":"1901.02547","title":"Problem Formulation and Fairness","created":"2019-01-08","cited_by_count":85},{"id":"2004.11440","title":"Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs","created":"2020-04-23","cited_by_count":74},{"id":"1901.04562","title":"Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements","created":"2019-01-14","cited_by_count":70},{"id":"2112.05675","title":"Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support","created":"2021-12-10","cited_by_count":63},{"id":"2011.02282","title":"\"What We Can't Measure, We Can't Understand\": Challenges to Demographic Data Procurement in the Pursuit of Fairness","created":"2020-10-30","cited_by_count":56},{"id":"2310.02521","title":"Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem","created":"2023-10-04","cited_by_count":52},{"id":"2205.07722","title":"How Different Groups Prioritize Ethical Values for Responsible AI","created":"2022-05-16","cited_by_count":49},{"id":"2304.14749","title":"Understanding accountability in algorithmic supply chains","created":"2023-04-28","cited_by_count":40},{"id":"2205.06922","title":"Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits","created":"2022-05-13","cited_by_count":34},{"id":"2209.09780","title":"Dislocated Accountabilities in the AI Supply Chain: Modularity and Developers' Notions of Responsibility","created":"2022-09-20","cited_by_count":33},{"id":"2210.03709","title":"Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice","created":"2022-10-07","cited_by_count":32},{"id":"2206.02923","title":"Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata","created":"2022-06-06","cited_by_count":28},{"id":"2206.11922","title":"Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance","created":"2022-06-23","cited_by_count":28},{"id":"2305.06415","title":"WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?","created":"2023-05-10","cited_by_count":28},{"id":"2402.15350","title":"Farsight: Fostering Responsible AI Awareness During AI Application Prototyping","created":"2024-02-23","cited_by_count":28},{"id":"2210.03535","title":"From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML","created":"2022-10-06","cited_by_count":27},{"id":"2203.10748","title":"Whose AI Dream? In search of the aspiration in data annotation","created":"2022-03-21","cited_by_count":24},{"id":"2301.12243","title":"Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook","created":"2023-01-28","cited_by_count":22},{"id":"2110.02932","title":"Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development","created":"2021-10-06","cited_by_count":21},{"id":"2209.04963","title":"Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering","created":"2022-09-12","cited_by_count":20},{"id":"2211.06753","title":"Seamful XAI: Operationalizing Seamful Design in Explainable AI","created":"2022-11-12","cited_by_count":20},{"id":"2109.07906","title":"Ethics of AI: A Systematic Literature Review of Principles and Challenges","created":"2021-09-12","cited_by_count":18},{"id":"2302.10395","title":"Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience","created":"2023-02-21","cited_by_count":18},{"id":"2306.06542","title":"Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice","created":"2023-06-10","cited_by_count":15},{"id":"2101.04271","title":"What Do We Mean by \"Accessibility Research\"? A Literature Survey of Accessibility Papers in CHI and ASSETS from 1994 to 2019","created":"2021-01-12","cited_by_count":14},{"id":"2112.07467","title":"AI Ethics Principles in Practice: Perspectives of Designers and Developers","created":"2021-12-14","cited_by_count":12},{"id":"2305.09573","title":"Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurs","created":"2023-05-16","cited_by_count":12},{"id":"2206.09978","title":"German AI Start-Ups and AI Ethics: Using A Social Practice Lens for Assessing and Implementing Socio-Technical Innovation","created":"2022-06-20","cited_by_count":11},{"id":"2407.17374","title":"Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts","created":"2024-07-24","cited_by_count":10},{"id":"2111.09478","title":"Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns","created":"2021-11-18","cited_by_count":9},{"id":"2206.00474","title":"Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness","created":"2022-06-01","cited_by_count":9},{"id":"2307.15158","title":"RAI Guidelines: Method for Generating Responsible AI Guidelines Grounded in Regulations and Usable by (Non-)Technical Roles","created":"2023-07-27","cited_by_count":9},{"id":"2501.18493","title":"Examining the Expanding Role of Synthetic Data Throughout the AI Development Pipeline","created":"2025-01-30","cited_by_count":8},{"id":"1906.07946","title":"Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study","created":"2019-06-19","cited_by_count":7},{"id":"2208.12616","title":"Macro Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions","created":"2022-08-12","cited_by_count":7},{"id":"2303.00673","title":"Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness","created":"2023-03-01","cited_by_count":7},{"id":"2306.00025","title":"Disentangling and Operationalizing AI Fairness at LinkedIn","created":"2023-05-30","cited_by_count":7},{"id":"2504.20185","title":"AI Supply Chains: An Emerging Ecosystem of AI Actors, Products, and Services","created":"2025-04-28","cited_by_count":7},{"id":"2401.17486","title":"A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations","created":"2024-01-30","cited_by_count":6},{"id":"2405.03855","title":"Strategies for Increasing Corporate Responsible AI Prioritization","created":"2024-05-06","cited_by_count":6},{"id":"2207.01493","title":"AI Ethics: An Empirical Study on the Views of Practitioners and Lawmakers","created":"2022-06-30","cited_by_count":5},{"id":"2409.12000","title":"\"It Might be Technically Impressive, But It's Practically Useless to us\": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry","created":"2024-09-18","cited_by_count":5},{"id":"2501.01441","title":"Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems","created":"2024-12-26","cited_by_count":5},{"id":"2203.01157","title":"Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups","created":"2022-03-02","cited_by_count":4},{"id":"2205.03946","title":"What does it mean to be a responsible AI practitioner: An ontology of roles and skills","created":"2022-05-08","cited_by_count":4},{"id":"2401.11131","title":"Towards a Non-Ideal Methodological Framework for Responsible ML","created":"2024-01-20","cited_by_count":4},{"id":"2505.17165","title":"A Toolkit for Compliance, a Toolkit for Justice: Drawing on Cross-sectoral Expertise to Develop a Pro-justice EU AI Act Toolkit","created":"2025-05-22","cited_by_count":4},{"id":"2306.06749","title":"Implementing AI Ethics: Making Sense of the Ethical Requirements","created":"2023-06-11","cited_by_count":3},{"id":"2402.01691","title":"Investigating Algorithm Review Boards for Organizational Responsible Artificial Intelligence Governance","created":"2024-01-23","cited_by_count":3},{"id":"2403.10438","title":"Data Ethics Emergency Drill: A Toolbox for Discussing Responsible AI for Industry Teams","created":"2024-03-15","cited_by_count":3},{"id":"2208.04122","title":"Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design","created":"2022-08-08","cited_by_count":2},{"id":"2302.11775","title":"Don't Look at the Data! How Differential Privacy Reconfigures the Practices of Data Science","created":"2023-02-23","cited_by_count":2},{"id":"2404.03995","title":"Balancing Progress and Responsibility: A Synthesis of Sustainability Trade-Offs of AI-Based Systems","created":"2024-04-05","cited_by_count":2},{"id":"2410.09985","title":"Responsible AI in the Global Context: Maturity Model and Survey","created":"2024-10-13","cited_by_count":2},{"id":"2403.19049","title":"Power and Play: Investigating \"License to Critique\" in Teams' AI Ethics Discussions","created":"2024-03-27","cited_by_count":1},{"id":"2409.02432","title":"Preliminary Insights on Industry Practices for Addressing Fairness Debt","created":"2024-09-04","cited_by_count":1},{"id":"2409.02433","title":"From Literature to Practice: Exploring Fairness Testing Tools for the Software Industry Adoption","created":"2024-09-04","cited_by_count":1},{"id":"2412.13846","title":"From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?","created":"2024-12-18","cited_by_count":1},{"id":"2504.10276","title":"Who Speaks for Ethics? How Demographics Shape Ethical Advocacy in Software Development","created":"2025-04-14","cited_by_count":1}],"next_cursor":null}