Designing Contestability: Interaction Design, Machine Learning, and Mental Health
Open accessAbstract
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.
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Contestability ensures failures are avoided by allowing human controllers to intervene before machine decisions are put into force.
“Contestability ensures such failures are avoided by allowing human controllers to intervene before machine decisions are put into force.”
From Contestable AI by Design: Towards a Framework (2022) · cites this paper as Hirsch et al. (2017)
In “Facilitating Contestation” (page unavailable), the paper calls for mechanisms that let users challenge machine predictions and explains why errors can harm therapists and patients. It does not say contestability ensures failures are avoided or that human controllers intervene before machine-informed decisions take effect; “Design for Contestability” instead describes strategies to help mitigate future misuse. (LLM, conf=0.88)
Experimental — accuracy not yet measuredCorrections, appeals, and additional contextual information from human controllers and decision subjects can be used as feedback signals for the decision-making process as a whole.
“Corrections, appeals, and additional contextual information from human controllers and decision subjects can be used as feedback signals for the decision-making process as a whole (Hirsch et al., 2017;Vaccaro et al., 2020).”
From Contestable AI by Design: Towards a Framework (2022) · cites this paper as Hirsch et al., 2017
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Machine Learning-based Mental Health Analysis and Early Warning for College Student
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- Authors
- Hirsch, T., Merced, K., Narayanan, S., Imel, Z. E., & Atkins, D. C.
- Affiliations
- University of Washington (US)
- University of Utah (US)
- University of Southern California (US)
- Venue
- Proceedings of the 2017 Conference on Designing Interactive Systems
- Publisher
- ACM
- Published
- 2017-06-10
- Volume
- 2017
- Pages
- 95-99
- PMC
- PMC5590649
- OpenAlex
- W2623360248
- Cited by
- 110
- References
- 36
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 11 · 2023: 11 · 2022: 19 · 2021: 16 · 2020: 10 · 2019: 11 · 2018: 6 · 2017: 1
- FWCI
- 19.10
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Designing Interactive Systems Conference (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Health (AA023814, AA018673, DA034860)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- contestability, automated assessment, psychotherapy training, interaction design, high-stakes decision making
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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