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Unethically Me: Explaining Artificial Intelligence’s Results by Being Unethical

Published: 26 October 2020 Publication History

Abstract

The goal of this workshop is to examine what is needed to explain artificial intelligence (AI) for lay end-users. In a full day workshop we want to engage with an interdisciplinary group of researchers to find best practices in explaining and interpreting results from AI for those with only with limited knowledge of AI.

References

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Roberto Confalonieri, Tarek R Besold, Tillman Weyde, Kathleen Creel, Tania Lombrozo, Shane Mueller, and Patrick Shafto. 2017. What makes a good explanation? Cognitive dimensions of explaining intelligent machines. LombrozoJuly(2017). https://www.perspicuous-computing.science
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Catherine D’Ignazio and Lauren F Klein. 2020. Data feminism. MIT Press.
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Lilian Edwards and Michael Veale. 2018. Enslaving the Algorithm: From a ”right to an Explanation” to a ”right to Better Decisions”?. In IEEE Security and Privacy, Vol. 16. IEEE, 46–54. https://doi.org/10.1109/MSP.2018.2701152 arxiv:1803.07540
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Virginia Eubanks. 2018. Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
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Alexandra Kirsch. 2018. Explain to whom? Putting the user in the center of explainable AI. In CEUR Workshop Proceedings, Vol. 2071.
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Jessica Morley, Luciano Floridi, Libby Kinsey, and Anat Elhalal. 2019. From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices. Science and Engineering Ethics0123456789 (2019). https://doi.org/10.1007/s11948-019-00165-5 arxiv:1905.06876
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Alun Preece, Dan Harborne, Dave Braines, Richard Tomsett, and Supriyo Chakraborty. 2018. Stakeholders in Explainable AI. In AAAI FSS-18: Artificial Intelligence in Government and Public Sector. arxiv:1810.00184http://arxiv.org/abs/1810.00184
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Katharina Weitz, Dominik Schiller, Ruben Schlagowski, Tobias Huber, and Elisabeth André. 2019. ”Do You Trust Me?”: Increasing User-Trust by Integrating Virtual Agents in Explainable AI Interaction Design. In Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents. 7–9. https://doi.org/10.1145/3308532.3329441

Cited By

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  • (2023)“That’s important, but...”: How Computer Science Researchers Anticipate Unintended Consequences of Their Research InnovationsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581347(1-16)Online publication date: 19-Apr-2023

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        NordiCHI '20: Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society
        October 2020
        1177 pages
        ISBN:9781450375795
        DOI:10.1145/3419249
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 26 October 2020

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        Author Tags

        1. Explainability
        2. HCI Design

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        • Extended-abstract
        • Research
        • Refereed limited

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        NordiCHI '20
        NordiCHI '20: Shaping Experiences, Shaping Society
        October 25 - 29, 2020
        Tallinn, Estonia

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        NordiCHI '20 Paper Acceptance Rate 89 of 399 submissions, 22%;
        Overall Acceptance Rate 379 of 1,572 submissions, 24%

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        • (2023)“That’s important, but...”: How Computer Science Researchers Anticipate Unintended Consequences of Their Research InnovationsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581347(1-16)Online publication date: 19-Apr-2023

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