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In a world of daily emerging scientific inquisition and discovery, the prolific launch of machine learning across industries comes to little surprise for those familiar with the potential of ML.
A framework for understanding unintended consequences of machine learning
Suresh, H., and Guttag, J. V. (2019) · 1901
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One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques
Arya, V., Bellamy, R. K. E., Chen, P.-Y., Dhurandhar, A., Hind, M., Hoffman, S. C., Houde, S., Liao, Q. V., Luss, R., Mojsilovic, A., Mourad, S., Pedemonte, P., Raghavendra, R., Richards, J. T., Sattigeri, P., Shanmugam, K., Singh, M., Varshney, K. R., Wei, D., and Zhang, Y. (2019) · 1909
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On Simpson’s Paradox and the Sure-Thing Principle
Blyth, C. R. (1972) · 1972
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A blot on the profession
Lowry, S., and MacPherson, G. (1988) · 1988
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Professional codes: Why, how, and with what impact?
Frankel, M. S. (1989) · 1989
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Thinking Like an Engineer: The Place of a Code of Ethics in the Practice of a Profession
Davis, M. (1991) · 1991
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Bias in Computer Systems
Friedman, B., and Nissenbaum, H. (1996) · 1996
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Sorting Things Out: Classification and Its Consequences
Bowker, G. C., and Star, S. L. (1999) · 1999
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Does automation bias decision-making?
Skitka, L. J., Mosier, K. L., and Burdick, M. (1999) · 1999
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Value sensitive design: Theory and methods
Friedman, B., Kahn, P., and Borning, A. (2002) · 2002
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Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age
Harcourt, B. (2007) · 2007
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Values and Pragmatic Action: The Challenges of Introducing Ethical Intelligence in Technical Design Communities
Manders-Huits, N., and Zimmer, M. (2009) · 2009
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Evaluating Learning Algorithms: A Classification Perspective
Japkowicz, N., and Shah, M. (2011) · 2011
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The effectiveness of personalized marketing in online banking: A comparison between search and experience offerings
Sunikka, A., Bragge, J., and Kallio, H. (2011) · 2011
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Johnson, B., Bartola, J., Angell, R., Keith, K. A., Witty, S., Giguere, S., and Brun, Y. (2020) · 2012
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Machine Learning that Matters
Wagstaff, K. L. (2012) · 2012
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Why unbiased computational processes can lead to discriminative decision procedures
Calders, T., and Žliobaitė, I. (2013) · 2013
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The Hidden Biases in Big Data
Crawford, K. (2013) · 2013
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The Pitfalls of Preduction
Ridgeway, G. (2013) · 2013
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Seeing the Sort: The Aesthetic and Industrial Defense of “The Algorithm”
Sandvig, C. (2014) · 2014
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Machine learning: Trends, perspectives, and prospects.
Jordan, M. I., and Mitchell, T. M. (2015) · 2015
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On the relation between accuracy and fairness in binary classification
Zliobaite, I. (2015) · 2015
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Machine Bias
Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (2016) · 2016
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A Confidence-Based Approach for Balancing Fairness and Accuracy
Fish, B., Kun, J., and Lelkes, Á. D. (2016) · 2016
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To predict and serve?
Lum, K., and Isaac, W. (2016) · 2016
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“Why Should I Trust You?” Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016) · 2016
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AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., Nagar, S., Ramamurthy, K. N., Richards, J., Saha, D., Sattigeri, P., Singh, M., Varshney, K. R., and Zhang, Y. (2018) · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · 2017
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How algorithmic popularity bias hinders or promotes quality
Ciampaglia, G. L., Nematzadeh, A., Menczer, F., and Flammini, A. (2017) · 2017
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Algorithmic Decision Making and the Cost of Fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., and Huq, A. (2017) · 2017
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The Trouble with Bias.
Crawford, K. (2017) · 2017
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Avoiding Discrimination through Causal Reasoning
Kilbertus, N., Rojas-Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., and Schölkopf, B. (2017) · 2017
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Inherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., and Raghavan, M. (2017) · 2017
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Statistical Bias Types Explained (with examples).
Mester, T. (2017) · 2017
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The Problem of Infra-marginality in Outcome Tests for Discrimination
Simoiu, C., Corbett-Davies, S., and Goel, S. (2017) · 2017
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Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data
Veale, M., and Binns, R. (2017) · 2017
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Bias on the web
Baeza-Yates, R. (2018) · 2018
Cited alongside, same era.
Big Data’s Disparate Impact
Barocas, S., and Selbst, A. D. (2018) · 2018
Cited alongside, same era.
Algorithmic prediction in policing: assumptions, evaluation, and accountability
Bennett Moses, L., and Chan, J. (2018) · 2018
Cited alongside, same era.
Fairness in Machine Learning: Lessons from Political Philosophy
Binns, R. (2018) · 2018
Cited alongside, same era.
’It’s Reducing a Human Being to a Percentage’; Perceptions of Justice in Algorithmic Decisions
Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., and Shadbolt, N. (2018) · 2018
Cited alongside, same era.
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification *
Buolamwini, J., and Gebru, T. (2018) · 2018
Cited alongside, same era.
Chatbots as a lever to redefine customer experience in banking
Moysan, Y., and Zeitoun, J. (2019) · 2019
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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., and Kıcıman, E. (2019) · 2019
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Landscape Summary: Bias in Algorithmic Decision-Making What is bias in algorithmic decision-making, how can we identify it, and how can we mitigate it?
Rovatsos, M., Mittelstadt, B., and Koene, A. (2019) · 2019
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Fairness and abstraction in sociotechnical systems
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., and Vertesi, J. (2019) · 2019
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Data Is the New What? Popular Metaphors & Professional Ethics in Emerging Data Culture
Stark, L., and Hoffmann, A. L. (2019) · 2019
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Novel robotic systems and future directions.
Chang, K., Raheem, A., and Rha, K. (2018) · 2018
Cited alongside, same era.
An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation.
Choi, D., and Lee, K. (2018) · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
Corbett-Davies, S., and Goel, S. (2018) · 2018
Cited alongside, same era.
Assessing and addressing algorithmic bias in practice
Cramer, H., Garcia-Gathright, J., Springer, A., and Reddy, S. (2018) · 2018
Cited alongside, same era.
Assessing and Addressing Algorithmic Bias - But Before We Get There
Garcia-Gathright, J., Springer, A., and Cramer, H. (2018) · 2018
Cited alongside, same era.
Datasheets for Datasets
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., and Crawford, K. (2018) · 2018
Cited alongside, same era.
Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Ví, F., and Wilson, J. (2019) · 2019
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On the Legal Compatibility of Fairness Definitions
Xiang, A., and Raji, I. D. (2019) · 2019
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Fairlearn: A toolkit for assessing and improving fairness in AI *
Bird, S., Dudík, M., Edgar, R., Horn, B., Lutz, R., Milan, V., Sameki, M., Wallach, H., and Walker, K. (2020) · 2020
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Data Ethics Framework
Bradley, T., Ambrose, K., Bernstein, M., DeLoatch, I., Dreisigmeyer, D., Gonzales, J., Grubb, C., Haralampus, L., Hawes, M., Johnson, B., Kopp, B., Krebs, J., Marsico, J., Morgan, D., Osatuke, K., and Vidrine, E. (2020) · 2020
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Algorithmic Fairness from a Non-ideal Perspective
Fazelpour, S., and Lipton, Z. C. (2020) · 2020
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Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?
Geiger, R. S., Yu, K., Yang, Y., Dai, M., Qiu, J., Tang, R., and Huang, J. (2019) · 2020
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Transparency and reproducibility in artificial intelligence
Haibe-Kains, B., Adam, G. A., Hosny, A., Khodakarami, F., Shraddha, T., Kusko, R., Sansone, S. A., Tong, W., Wolfinger, R. D., Mason, C. E., Jones, W., Dopazo, J., Furlanello, C., Waldron, L., Wang, B., McIntosh, C., Goldenberg, A., Kundaje, A., Greene, C. S., Broderick, T., Hoffman, M. M., Leek, J. T., Korthauer, K., Huber, W., Brazma, A., Pineau, J., Tibshirani, R., Hastie, T., Ioannidis, J. P., Quackenbush, J., and Aerts, H. J. (2020) · 2020
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Bias in Machine Learning – What is it Good for?
Hellström, T., Dignum, V., and Bensch, S. (2020) · 2020
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What’s sex got to do with machine learning?
Hu, L., and Kohler-Hausmann, I. (2020) · 2020
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Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., and Wortman Vaughan, J. (2020) · 2020
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”How do I fool you?”: Manipulating User Trust via Misleading Black Box Explanations
Lakkaraju, H., and Bastani, O. (2019) · 2020
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Designing Tools for Semi-Automated Detection of Machine Learning Biases: An Interview Study
Law, P.-M., Malik, S., Du, F., and Sinha, M. (2020a) · 2020
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Legal and Ethics Checklist for AI Systems
Lifshitz, L., and McMaster, C. (2020) · 2020
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Applications of artificial intelligence to electronic health record data in ophthalmology
Lin, W. C., Chen, J. S., Chiang, M. F., and Hribar, M. R. (2020) · 2020
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Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI
Madaio, M. A., Stark, L., Wortman Vaughan, J., and Wallach, H. (2020) · 2020
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An intelligent real-time scheduler for out-patient clinics: A multi-agent system model
Munavalli, J. R., Rao, S. V., Srinivasan, A., and van Merode, G. G. (2020) · 2020
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Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices.
Rakova, B., Yang, J., Cramer, H., and Chowdhury, R. (2020) · 2020
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Public Accountability : Understanding Sentiments towards Artificial Intelligence across Dispositional Identities
Richardson, B., Prioleau, D., Alikhademi, K., and Gilbert, J. E. (2020) · 2020
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Designing Fair AI
Robert, L. P., Pierce, C., Marquis, E., Kim, S., and Alahmad, R. (2020) · 2020
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ML-fairness-gym: A Tool for Exploring Long-Term Impacts of Machine Learning Systems
Srinivasan, H. (2020) · 2020
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LiFT: A Scalable Framework for Measuring Fairness in ML Applications
Vasudevan, S., and Kenthapadi, K. (2020) · 2020
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Fairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., and Roth, A. (2021) · 2021
Closest in time.
Algorithmic injustice: a relational ethics approach
Birhane, A. (2021) · 2021
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Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
Cheng, L., Varshney, K. R., and Liu, H. (2021) · 2021
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The (im)possibility of fairness
Friedler, S. A., Scheidegger, C. E., and Venkatasubramanian, S. (2021) · 2021
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Measurement and fairness
Jacobs, A. Z., and Wallach, H. (2021) · 2021
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Fairness, equality, and power in algorithmic decision-making
Kasy, M., and Abebe, R. (2021) · 2021
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A survey on bias and fairness in machine learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021) · 2021
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Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits; Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits
Richardson, B., Garcia-Gathright Spotify, J., Way, S. F., Jennifer Thom, S., Henriette Cramer, S., Garcia-Gathright, J., Thom, J., and Cramer, H. (2021) · 2021
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The Landscape and Gaps in Open Source Fairness Toolkits
Seng, M., Lee, A., and Singh, J. (2021) · 2021
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