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Hirsch T, Merced K, Narayanan S, et al. Designing Contestability: Interaction Design, Machine Learning, and Mental Health. DIS (Des Interact Syst Conf). 2017;2017:95-99doi: 10.1145/3064663.3064703.
Hirsch, T., Merced, K., Narayanan, S., Imel, Z. E., & Atkins, D. C. (2017). Designing Contestability: Interaction Design, Machine Learning, and Mental Health. DIS. Designing Interactive Systems (Conference), 201795-99. https://doi.org/10.1145/3064663.3064703
Hirsch, Tad, et al. "Designing Contestability: Interaction Design, Machine Learning, and Mental Health." DIS. Designing Interactive Systems (Conference) vol. 2017 (2017): 95-99. doi: https://doi.org/10.1145/3064663.3064703
Hirsch T, Merced K, Narayanan S, Imel ZE, Atkins DC. Designing Contestability: Interaction Design, Machine Learning, and Mental Health. DIS (Des Interact Syst Conf). 2017 Jun;2017:95-99. doi: 10.1145/3064663.3064703. PMID: 28890949; PMCID: PMC5590649.
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DIS (Des Interact Syst Conf). 2017 Jun;2017:95-99. doi: 10.1145/3064663.3064703.

Designing Contestability: Interaction Design, Machine Learning, and Mental Health.

DIS. Designing Interactive Systems (Conference)

Tad Hirsch, Kritzia Merced, Shrikanth Narayanan, Zac E Imel, David C Atkins

Affiliations

  1. University of Washington, Seattle, USA, [email protected].
  2. University of Utah, Salt Lake City, USA, [email protected].
  3. University of Southern California. Los Angeles, USA, [email protected].
  4. University of Utah, Salt Lake City, USA, [email protected].
  5. University of Washington, Seattle, USA, [email protected].

PMID: 28890949 PMCID: PMC5590649 DOI: 10.1145/3064663.3064703

Abstract

We describe the design of an automated assessment and training tool for psychotherapists to illustrate challenges with creating interactive machine learning (ML) systems, particularly in contexts where human life, livelihood, and wellbeing are at stake. We explore how existing theories of interaction design and machine learning apply to the psychotherapy context, and identify "contestability" as a new principle for designing systems that evaluate human behavior. Finally, we offer several strategies for making ML systems more accountable to human actors.

Keywords: Applications and Expert Systems; J.4 Applications; H.5.m. Information interfaces and presentation (e.g., HCI); Machine learning; Miscellaneous; I.2.1. Artificial Intelligence; Psychology; Design; Social and Behavioral Sciences; interaction design; mental health; psychotherapy

References

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  6. Lancet. 2013 Nov 9;382(9904):1575-86 - PubMed
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Publication Types

Grant support