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Artificial intelligence (AI) research is routinely criticized for its real and potential impacts on society, and we lack adequate institutional responses to this criticism and to the responsibility that it reflects.
The unanticipated consequences of purposive social action
Merton, R. K · 1936
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The Belmont Report: Ethical principles and guidelines for the protection of human subjects of research, 1979
Department of Health, Education, and Welfare · 1979
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Do artifacts have politics?
Winner, L · 1980
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The duality of technology: Rethinking the concept of technology in organizations
Orlikowski, W. J · 1992
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Managing ethics and legal compliance: What works and what hurts
Trevino, L. K., Weaver, G. R., Gibson, D. G., and Toffler, B. L · 1999
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Cosmetic compliance and the failure of negotiated governance
Krawiec, K. D · 2003
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“go away”: participant objections to being studied and the ethics of chatroom research
Hudson, J. M., and Bruckman, A · 2004
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Mock trials and role-playing in computer ethics courses
Canosa, R. L., and Lucas, J. M · 2008
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“But the data is already public”: On the ethics of research in Facebook
Zimmer, M · 2010
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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Big data’s end run around anonymity and consent
Barocas, S., and Nissenbaum, H · 2014
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Auditing algorithms: Research methods for detecting discrimination on internet platforms
Sandvig, C., Hamilton, K., Karahalios, K., and Langbort, C · 2014
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Privacy on the ground: driving corporate behavior in the United States and Europe
Bamberger, K. A., and Mulligan, D. K · 2015
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Unequal representation and gender stereotypes in image search results for occupations
Kay, M., Matuszek, C., and Munson, S. A · 2015
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Geek heresy: Rescuing social change from the cult of technology
Toyama, K · 2015
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Big data’s disparate impact
Barocas, S., and Selbst, A. D · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
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Taking a hit: Designing around rejection, mistrust, risk, and workers’ experiences in amazon mechanical turk
McInnis, B., Cosley, D., Nam, C., and Leshed, G · 2016
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Where are human subjects in big data research? the emerging ethics divide
Metcalf, J., and Crawford, K · 2016
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Weapons of math destruction: How big data increases inequality and threatens democracy
O’neil, C · 2016
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The ethics of computing: A survey of the computing-oriented literature
Stahl, B. C., Timmermans, J., and Mittelstadt, B. D · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A · 2017
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Algorithms in practice: Comparing web journalism and criminal justice
Christin, A · 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
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Quantifying search bias: Investigating sources of bias for political searches in social media
Kulshrestha, J., Eslami, M., Messias, J., Zafar, M. B., Ghosh, S., Gummadi, K. P., and Karahalios, K · 2017
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Counterfactual fairness
Kusner, M. J., Loftus, J., Russell, C., and Silva, R · 2017
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Algorithmic mediation in group decisions: Fairness perceptions of algorithmically mediated vs. discussion-based social division
Lee, M. K., and Baykal, S · 2017
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Technically wrong: Sexist apps, biased algorithms, and other threats of toxic tech
Wachter-Boettcher, S · 2017
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A reductions approach to fair classification
Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., and Wallach, H · 2018
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‘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
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J., and Gebru, T · 2018
Cited alongside, same era.
How to teach computer ethics through science fiction
Burton, E., Goldsmith, J., and Mattei, N · 2018
Cited alongside, same era.
Ai surveillance studies need ethics review
Calvo, R. A., and Peters, D · 2018
Cited alongside, same era.
Implementing machine learning in health care—addressing ethical challenges
Char, D. S., Shah, N. H., and Magnus, D · 2018
Cited alongside, same era.
Design justice: towards an intersectional feminist framework for design theory and practice
Costanza-Chock, S · 2018
Cited alongside, same era.
Addressing age-related bias in sentiment analysis
Díaz, M., Johnson, I., Lazar, A., Piper, A. M., and Gergle, D · 2018
Cited alongside, same era.
Integrating ethics within machine learning courses
Saltz, J., Skirpan, M., Fiesler, C., Gorelick, M., Yeh, T., Heckman, R., Dewar, N., and Beard, N · 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
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Do no harm: a roadmap for responsible machine learning for health care
Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., et al · 2019
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Defending against neural fake news
Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., and Choi, Y · 2019
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Like a researcher stating broader impact for the very first time
Abuhamad, G., and Rheault, C · 2020
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Automating inequality: How high-tech tools profile, police, and punish the poor
Eubanks, V · 2018
Cited alongside, same era.
Word embeddings quantify 100 years of gender and ethnic stereotypes
Garg, N., Schiebinger, L., Jurafsky, D., and Zou, J · 2018
Cited alongside, same era.
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., and Crawford, K · 2018
Cited alongside, same era.
The ethics of computer science: this researcher has a controversial proposal
Gibney, E · 2018
Cited alongside, same era.
Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management
Lee, M. K · 2018
Cited alongside, same era.
Algorithms of oppression: How search engines reinforce racism
Noble, S. U · 2018
Cited alongside, same era.
Belfield, H · 2020
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From ethics washing to ethics bashing: a view on tech ethics from within moral philosophy
Bietti, E · 2020
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An agenda for disinformation research
Bliss, N., Bradley, E., Garland, J., Menczer, F., Ruston, S. W., Starbird, K., and Wiggins, C · 2020
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Language (technology) is power: A critical survey of “bias” in nlp
Blodgett, S. L., Barocas, S., Daumé III, H., and Wallach, H · 2020
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Overcoming failures of imagination in ai infused system development and deployment, 2020
Boyarskaya, M., Olteanu, A., and Crawford, K · 2020
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Algorithmic fairness from a non-ideal perspective
Fazelpour, S., and Lipton, Z. C · 2020
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What do we teach when we teach tech ethics? a syllabi analysis
Fiesler, C., Garrett, N., and Beard, N · 2020
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The next generation of cyber-enabled information warfare
Hartmann, K., and Giles, K · 2020
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Multi-layered explanations from algorithmic impact assessments in the gdpr
Kaminski, M. E., and Malgieri, G · 2020
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It is time for more critical cs education
Ko, A. J., Oleson, A., Ryan, N., Register, Y., Xie, B., Tari, M., Davidson, M., Druga, S., and Loksa, D · 2020
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Defining ai in policy versus practice
Krafft, P., Young, M., Katell, M., Huang, K., and Bugingo, G · 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
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Diversity and inclusion metrics in subset selection
Mitchell, M., Baker, D., Moorosi, N., Denton, E., Hutchinson, B., Hanna, A., Gebru, T., and Morgenstern, J · 2020
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Closing the ai accountability gap: defining an end-to-end framework for internal algorithmic auditing
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., and Barnes, P · 2020
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Rakova, B., Yang, J., Cramer, H., and Chowdhury, R · 2020
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Learning machine learning with personal data helps stakeholders ground advocacy arguments in model mechanics
Register, Y., and Ko, A. J · 2020
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Human-centered artificial intelligence: Reliable, safe & trustworthy
Shneiderman, B · 2020
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To live in their utopia: Why algorithmic systems create absurd outcomes
Alkhatib, A · 2021
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It’s compaslicated: The messy relationship between rai datasets and algorithmic fairness benchmarks
Bao, M., Zhou, A., Zottola, S., Brubach, B., Desmarais, S., Horowitz, A., Lum, K., and Venkatasubramanian, S · 2021
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Opening research commissioning to civic participation: Creating a community panel to review the social impact of hci research proposals
G Johnson, I., and Crivellaro, C · 2021
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Unpacking the expressed consequences of ai research in broader impact statements
Nanayakkara, P., Hullman, J., and Diakopoulos, N · 2021
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Modeling assumptions clash with the real world: Transparency, equity, and community challenges for student assignment algorithms
Robertson, S., Nguyen, T., and Salehi, N · 2021
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Timelines: A world-building activity for values advocacy
Wong, R. Y., and Nguyen, T · 2021
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Beyond kant and bentham: How ethical theories are being used in artificial moral agents
Zoshak, J., and Dew, K · 2021
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