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In AI-assisted decision-making, effective hybrid (human-AI) teamwork is not solely dependent on AI performance alone, but also on its impact on human decision-making.
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Harvesting implicit group attitudes and beliefs from a demonstration web site
Nosek, B.; and Banaji, M. 2002 · 2002
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A perspective on judgment and choice
Kahneman, D. 2003 · 2003
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Reinforcing the glass ceiling: the consequences of hostile sexism for female managerial candidates
Masser, B.; and Abrams, D. 2004 · 2004
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The Construction of Preference
Lichtenstein, S.; and Slovic, P. 2006 · 2006
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Nudge: improving decisions about health, wealth, and happiness
Thaler, R.; and Sunstein, C. 2008 · 2008
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Interventions that affect gender bias in hiring: a systematic review
Isaac, C.; Lee, B.; and Carnes, M. 2009 · 2009
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Consumer decision making in knowledge-based recommendation
Mandl, M.; Felfernig, A.; Teppan, E.; and Schubert, M. 2011 · 2011
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Fairness through awareness
Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012 · 2012
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Combining human and machine intelligence in large-scale crowdsourcing
Kamar, E.; Hacker, S.; and Horvitz, E. 2012 · 2012
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Biases and implicit knowledge
Cunningham, T. 2013 · 2013
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Discrimination in online ad delivery
Sweeney, L. 2013 · 2013
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A meta-analysis of gender stereotypes and bias in experimental simulations of employment decision making
Koch, A.; D’Mello, S.; and Sackett, P. 2015 · 2015
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Big data’s disparate impact
Barocas, S.; and Selbst, A. 2016 · 2016
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Man is to computer programmer as woman is to homemaker? Debiasing word embeddings
Bolukbasi, T.; Chang, K.-W.; Zou, J.; Saligrama, V.; and Kalai, A. 2016 · 2016
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Human-centered machine learning
Gillies, M.; Fiebrink, R.; Tanaka, A.; Caramiaux, B.; Garcia, J.; Bevilacqua, F.; Heloir, A.; Nunnari, F.; Mackay, W.; and Amershi, S. 2016 · 2016
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Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
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Fairness in machine learning
Barocas, S.; Hardt, M.; and Narayanan, A. 2017 · 2017
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Group fairness under composition
Dwork, C.; and Ilvento, C. 2018 · 2018
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Explaining explanations: an overview of interpretability in machine learning
Gilpin, L.; Bau, D.; Yuan, B.; Bajwa, A.; Specter, M.; and Kagal, L. 2018 · 2018
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Lipstick on a pig: debiasing methods cover up systematic gender biases in word embeddings but do not remove them
Gonen, H.; and Goldberg, Y. 2019 · 2019
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The principles and limits of algorithm-in-the-loop decision making
Green, B.; and Chen, Y. 2019 · 2019
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Human-level performance in 3D multiplayer games with population-based reinforcement learning
Jadeberg, M.; Czarnecki, W.; Dunning, I.; Marris, L.; Lever, G.; Garcia Castaneda, A.; Beattie, C.; Rabinowitz, N.; Morcos, A.; Ruderman, A.; Sonnerat, N.; Green, T.; Deason, L.; Leibo, J.; Silver, D.; Hassabis, D.; Kavukcuoglu, K.; and Graepel, T. 2019 · 2019
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Discrimination in the age of algorithms
Kleinberg, J.; Ludwig, J.; Mullainathan, S.; and Sunstein, C. R. 2019 · 2019
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On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection
Lai, V.; and Tan, C. 2019 · 2019
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Human decisions and machine predictions
Kleinberg, J.; Lakkaraju, H.; Leskovec, J.; Ludwig, J.; and Mullainathan, S. 2018 · 2018
Cited alongside, same era.
Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery
Lundberg, S.; Nair, B.; Vavilala, M.; Horibe, M.; Eisses, M.; Adams, T.; Liston, D.; King-Wai Low, D.; Newman, S.-F.; Kim, J.; and Lee, S.-I. 2018 · 2018
Cited alongside, same era.
Amazon ditched AI recruiting tool that favored men for technical jobs
The Guardian, N. 2018 · 2018
Cited alongside, same era.
Beyond accuracy: the role of mental models in human-AI team performance
Bansal, G.; Nushi, B.; Kamar, E.; Lasecki, W.; Weld, D.; and Horvitz, E. 2019a · 2019
Cited alongside, same era.
Updates in human-AI teams: understanding and addressing the performance-compatibility tradeoff
Bansal, G.; Nushi, B.; Kamar, E.; Weld, D.; and Horvitz, E. 2019b · 2019
Cited alongside, same era.
Bias in bios: A case study of semantic representation bias in a high-stakes setting
De-Arteaga, M.; Romanov, A.; Wallach, H.; Chayes, J.; Borgs, C.; Chouldechova, A.; Geyik, S.; Kenthapadi, K.; and Kalai, A. 2019 · 2019
Cited alongside, same era.
What you see is what you get? The Impact of Representation Criteria on Human Bias in Hiring
Peng, A.; Nushi, B.; Kiciman, E.; Inkpen, K.; Suri, S.; and Kamar, E. 2019 · 2019
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What’s in a name? Reducing bias in bios without access to protected attributes
Romanov, A.; De-Arteaga, M.; Wallach, H.; Chayes, J.; Borgs, C.; Chouldechova, A.; Geyik, S.; Kenthapadi, K.; Rumshisky, A.; and Kalai, A. 2019 · 2019
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Understanding the effect of accuracy on trust in machine learning models
Yin, M.; Wortman Vaughan, J.; and Wallach, H. 2019 · 2019
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Mitigating bias in algorithmic hiring: Evaluating claims and practices
Raghavan, M.; Barocas, S.; Kleinberg, J.; and Levy, K. 2020 · 2020
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Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making
Zhang, Y.; Liao, V.; and Bellamy, R. 2020 · 2020
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Does the whole exceed its parts? the effect of ai explanations on complementary team performance
Bansal, G.; Wu, T.; Zhou, J.; Fok, R.; Nushi, B.; Kamar, E.; Ribeiro, M. T.; and Weld, D. 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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Manipulating and measuring model interpretability
Poursabzi-Sangdeh, F.; Goldstein, D.; Hofman, J.; Wortman Vaughn, J.; and Wallach, H. 2021 · 2021
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