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Explainable AI (XAI) research has been booming, but the question "$\textbf{To whom}$ are we making AI explainable?" is yet to gain sufficient attention.
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Shortliffe, E. H · 1974
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Explanation in second generation expert systems
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Evidence on discrimination in mortgage lending
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User modeling in human–computer interaction
Fischer, G · 2001
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Credit scoring and disparate impact, 2001
Fortowsky, E., LaCour-Little, M., and Mortgage, W. F. H · 2001
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Determinants, detection and amelioration of adverse impact in personnel selection procedures: Issues, evidence and lessons learned
Hough, L. M., Oswald, F. L., and Ployhart, R. E · 2001
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Generic user modeling systems
Kobsa, A · 2001
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Sample size required for adverse impact analysis
Morris, S. B · 2001
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A causal-model theory of conceptual representation and categorization
Rehder, B · 2003
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The inmates are running the asylum: Why high-tech products drive us crazy and how to restore the sanity
Cooper, A., et al · 2004
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Credit scoring and the availability of small business credit in low-and moderate-income areas
Frame, W. S., and Woosley, L · 2004
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Combining predictors to achieve optimal trade-offs between selection quality and adverse impact
De Corte, W., Lievens, F., and Sackett, P. R · 2007
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How it works: a field study of non-technical users interacting with an intelligent system
Tullio, J., Dey, A. K., Chalecki, J., and Fogarty, J · 2007
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The diversity–validity dilemma: Strategies for reducing racioethnic and sex subgroup differences and adverse impact in selection
Ployhart, R. E., and Holtz, B. C · 2008
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Explanation and categorization: How “why?” informs “what?”
Lombrozo, T · 2009
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Does credit scoring produce a disparate impact?
Explainable ai for designers: A human-centered perspective on mixed-initiative co-creation
Zhu, J., Liapis, A., Risi, S., Bidarra, R., and Youngblood, G. M · 2018
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Towards a characterization of explainable systems
Bohlender, D., and Köhl, M. A · 2019
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Friend, collaborator, student, manager: How design of an ai-driven game level editor affects creators
Guzdial, M., Liao, N., Chen, J., Chen, S.-Y., Shah, S., Shah, V., Reno, J., Smith, G., and Riedl, M. O · 2019
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Causability and explainability of artificial intelligence in medicine
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., and Müller, H · 2019
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Don’t let ai trigger a fair-lending violation, August 2019
Kosoff, J., and Wilbrandt, E · 2019
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Avery, R. B., Brevoort, K. P., and Canner, G · 2012
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Too much, too little, or just right? ways explanations impact end users’ mental models
Kulesza, T., Stumpf, S., Burnett, M., Yang, S., Kwan, I., and Wong, W.-K · 2013
Cited alongside, same era.
Broad agency announcementexplainable artificial intelligence (xai), 2016
DARPA · 2016
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Towards a rigorous science of interpretable machine learning
Doshi-Velez, F., and Kim, B · 2017
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What do we need to build explainable ai systems for the medical domain?
Holzinger, A., Biemann, C., Pattichis, C. S., and Kell, D. B · 2017
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Miller, T., Howe, P., and Sonenberg, L · 2017
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Computer says no: why making ais fair, accountable and transparent is crucial, 2017
Sample, I · 2017
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How to become a financial examiner, 2019
Labor Statistics, B · 2019
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Fusing level and ruleset features for multimodal learning of gameplay outcomes
Liapis, A., Karavolos, D., Makantasis, K., Sfikas, K., and Yannakakis, G. N · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Miller, T · 2019
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Explaining explanations in ai
Mittelstadt, B., Russell, C., and Wachter, S · 2019
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Detailed guide for compliance officers in california, 2019
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Summary report for: 13-2061.00 - financial examiners: Skills, 2019
Occupational Information, N · 2019
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Letter to regulators, 2019
U.S Senator Warren, E · 2019
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Actionable recourse in linear classification
Ustun, B., Spangher, A., and Liu, Y · 2019
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Regulators must issue ai guidance or fdic will: Mcwilliams, 2019
Witkowski, R · 2019
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Explainable machine learning in deployment
Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M., and Eckersley, P · 2020
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Effects test, 2020
CFPB · 2020
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Employment opportunities at eeoc, 2020
EEOC · 2020
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Explainability fact sheets: a framework for systematic assessment of explainable approaches
Sokol, K., and Flach, P · 2020
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