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With the recent release of AI interaction guidelines from Apple, Google, and Microsoft, there is clearly interest in understanding the best practices in human-AI interaction.
Practitioner knowledge and the problem of evidence based research policy and practice
M. Issitt and J. Spence · 2005
Earlier work this paper cites.
Visual analytics in deep learning: An interrogative survey for the next frontiers, 2018
F. Hohman, M. Kahng, R. Pienta, and D. H. Chau · 2018
Earlier work this paper cites.
Guidelines for human-ai interaction
S. Amershi, D. Weld, M. Vorvoreanu, A. Fourney, B. Nushi, P. Collisson, J. Suh, S. Iqbal, P. Bennett, K. Inkpen, J. Teevan, R. Kikin-Gil, and E. Horvitz · 2019
Earlier work this paper cites.
Everyday ethics for artificial intelligence
A. Cutler, M. Pribić, and L. Humphrey · 2019
Earlier work this paper cites.
Toward a design space for mitigating cognitive bias in vis
E. Wall, J. Stasko, and A. Endert · 2019
Cited alongside, same era.
Fairvis: Visual analytics for discovering intersectional bias in machine learning, 2019
Ángel Alexander Cabrera, W. Epperson, F. Hohman, M. Kahng, J. Morgenstern, and D. H. Chau · 2019
Cited alongside, same era.
The state of the art in enhancing trust in machine learning models with the use of visualizations
A. Chatzimparmpas, R. M. Martins, I. Jusufi, K. Kucher, F. Rossi, and A. Kerren · 2020
Cited alongside, same era.
Bluff: Interactively deciphering adversarial attacks on deep neural networks, 2020
N. Das, H. Park, Z. J. Wang, F. Hohman, R. Firstman, E. Rogers, and D. H. Chau · 2020
Cited alongside, same era.
Why authors don’t visualize uncertainty
J. Hullman · 2020
Closest in time.
A multidisciplinary survey and framework for design and evaluation of explainable ai systems, 2020
S. Mohseni, N. Zarei, and E. D. Ragan · 2020
Closest in time.
Should we trust (x)ai? design dimensions for structured experimental evaluations, 2020
F. Sperrle, M. El-Assady, G. Guo, D. H. Chau, A. Endert, and D. Keim · 2020
Closest in time.
The what-if tool: Interactive probing of machine learning models
J. Wexler, M. Pushkarna, T. Bolukbasi, M. Wattenberg, F. Viégas, and J. Wilson · 2020
Closest in time.
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