{"list":"other","total":74,"rows":[{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"1610.02413","title":"Equality of Opportunity in Supervised Learning","created":"2016-10-07","cited_by_count":2007},{"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":"2005.14050","title":"Language (Technology) is Power: A Critical Survey of \"Bias\" in NLP","created":"2020-05-28","cited_by_count":956},{"id":"1703.06856","title":"Counterfactual Fairness","created":"2017-03-20","cited_by_count":804},{"id":"1511.04508","title":"Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks","created":"2015-11-14","cited_by_count":678},{"id":"1610.07524","title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments","created":"2016-10-24","cited_by_count":672},{"id":"1801.07593","title":"Mitigating Unwanted Biases with Adversarial Learning","created":"2018-01-22","cited_by_count":663},{"id":"1905.07697","title":"Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations","created":"2019-05-19","cited_by_count":614},{"id":"1609.05807","title":"Inherent Trade-Offs in the Fair Determination of Risk Scores","created":"2016-09-19","cited_by_count":586},{"id":"1712.03141","title":"Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning","created":"2017-12-08","cited_by_count":414},{"id":"1711.05144","title":"Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness","created":"2017-11-14","cited_by_count":384},{"id":"2001.00973","title":"Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing","created":"2020-01-03","cited_by_count":384},{"id":"1703.09207","title":"Fairness in Criminal Justice Risk Assessments: The State of the Art","created":"2017-03-27","cited_by_count":315},{"id":"1802.07623","title":"Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives","created":"2018-02-21","cited_by_count":262},{"id":"2012.05345","title":"Data and its (dis)contents: A survey of dataset development and use in machine learning research","created":"2020-12-09","cited_by_count":260},{"id":"2001.02478","title":"Questioning the AI: Informing Design Practices for Explainable AI User Experiences","created":"2020-01-08","cited_by_count":253},{"id":"2002.05651","title":"Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning","created":"2020-01-31","cited_by_count":249},{"id":"2111.09679","title":"Enhanced Membership Inference Attacks against Machine Learning Models","created":"2021-11-18","cited_by_count":248},{"id":"1909.09223","title":"InterpretML: A Unified Framework for Machine Learning Interpretability","created":"2019-09-19","cited_by_count":246},{"id":"1905.12516","title":"Racial Bias in Hate Speech and Abusive Language Detection Datasets","created":"2019-05-29","cited_by_count":209},{"id":"1907.04135","title":"The What-If Tool: Interactive Probing of Machine Learning Models","created":"2019-07-09","cited_by_count":207},{"id":"1909.10594","title":"MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples","created":"2019-09-23","cited_by_count":197},{"id":"1901.10002","title":"A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle","created":"2019-01-28","cited_by_count":193},{"id":"1807.05852","title":"Machine Learning with Membership Privacy using Adversarial Regularization","created":"2018-07-16","cited_by_count":186},{"id":"1906.09208","title":"Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices","created":"2019-06-21","cited_by_count":174},{"id":"2106.15590","title":"The Values Encoded in Machine Learning Research","created":"2021-06-29","cited_by_count":172},{"id":"1805.11202","title":"FairGAN: Fairness-aware Generative Adversarial Networks","created":"2018-05-28","cited_by_count":164},{"id":"1811.05577","title":"Aequitas: A Bias and Fairness Audit Toolkit","created":"2018-11-14","cited_by_count":147},{"id":"2010.13561","title":"Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure","created":"2020-10-23","cited_by_count":138},{"id":"1906.06668","title":"Principles alone cannot guarantee ethical AI","created":"2019-06-16","cited_by_count":135},{"id":"1912.05100","title":"Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches","created":"2019-12-11","cited_by_count":101},{"id":"1805.03677","title":"The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards","created":"2018-05-09","cited_by_count":97},{"id":"1801.10408","title":"'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions","created":"2018-01-31","cited_by_count":91},{"id":"2002.00772","title":"Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study","created":"2020-02-03","cited_by_count":86},{"id":"2108.04308","title":"Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development","created":"2021-08-09","cited_by_count":85},{"id":"1602.03506","title":"Research Priorities for Robust and Beneficial Artificial Intelligence","created":"2016-02-10","cited_by_count":74},{"id":"1912.08320","title":"Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?","created":"2019-12-17","cited_by_count":64},{"id":"2101.09824","title":"Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs","created":"2021-01-24","cited_by_count":64},{"id":"1902.04783","title":"Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning","created":"2019-02-13","cited_by_count":57},{"id":"1908.08313","title":"Auditing Radicalization Pathways on YouTube","created":"2019-08-22","cited_by_count":53},{"id":"2105.02980","title":"Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors","created":"2021-05-06","cited_by_count":50},{"id":"1912.04883","title":"Roles for Computing in Social Change","created":"2019-12-10","cited_by_count":49},{"id":"1511.06385","title":"A Unified Gradient Regularization Family for Adversarial Examples","created":"2015-11-19","cited_by_count":43},{"id":"2007.07399","title":"Bringing the People Back In: Contesting Benchmark Machine Learning Datasets","created":"2020-07-14","cited_by_count":43},{"id":"1602.07043","title":"Auditing Black-box Models for Indirect Influence","created":"2016-02-23","cited_by_count":41},{"id":"2001.09604","title":"Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences","created":"2020-01-27","cited_by_count":41},{"id":"1802.09548","title":"Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction","created":"2018-02-26","cited_by_count":35},{"id":"1901.07694","title":"Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment","created":"2019-01-23","cited_by_count":31},{"id":"2104.03483","title":"Question-Driven Design Process for Explainable AI User Experiences","created":"2021-04-08","cited_by_count":31},{"id":"1806.02711","title":"POTs: Protective Optimization Technologies","created":"2018-06-07","cited_by_count":28},{"id":"2005.13213","title":"Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis?","created":"2020-05-27","cited_by_count":27},{"id":"2105.02751","title":"On the Ethical Limits of Natural Language Processing on Legal Text","created":"2021-05-06","cited_by_count":26},{"id":"1912.01172","title":"Value-laden Disciplinary Shifts in Machine Learning","created":"2019-12-03","cited_by_count":25},{"id":"2006.05336","title":"On the Effectiveness of Regularization Against Membership Inference Attacks","created":"2020-06-09","cited_by_count":24},{"id":"2103.06172","title":"Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems","created":"2021-03-10","cited_by_count":21},{"id":"2006.09663","title":"Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context","created":"2020-06-17","cited_by_count":18},{"id":"2103.01774","title":"Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions","created":"2021-02-23","cited_by_count":18},{"id":"2101.03827","title":"Representativeness in Statistics, Politics, and Machine Learning","created":"2021-01-11","cited_by_count":17},{"id":"1906.04585","title":"Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning","created":"2019-06-09","cited_by_count":16},{"id":"1908.06166","title":"A Mulching Proposal","created":"2019-08-10","cited_by_count":14},{"id":"2005.04949","title":"Designing for Human Rights in AI","created":"2020-05-11","cited_by_count":13},{"id":"2101.03042","title":"Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective","created":"2021-01-08","cited_by_count":9},{"id":"2004.02028","title":"Measuring Social Biases of Crowd Workers using Counterfactual Queries","created":"2020-04-04","cited_by_count":6},{"id":"2108.05624","title":"Operationalizing Human Values in Software Engineering: A Survey","created":"2021-08-12","cited_by_count":6},{"id":"2103.03332","title":"An Agent-based Model to Evaluate Interventions on Online Dating Platforms to Decrease Racial Homogamy","created":"2021-03-04","cited_by_count":4},{"id":"1908.02619","title":"Experiential AI","created":"2019-08-06","cited_by_count":2},{"id":"2105.14066","title":"Are Privacy Dashboards Good for End Users? Evaluating User Perceptions and Reactions to Google's My Activity (Extended Version)","created":"2021-05-28","cited_by_count":2},{"id":"2107.11273","title":"Towards a Human Values Dashboard for Software Development: An Exploratory Study","created":"2021-07-23","cited_by_count":2},{"id":"2110.11424","title":"Analysis of memory consumption by neural networks based on hyperparameters","created":"2021-10-21","cited_by_count":1}],"next_cursor":null}