research-article

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

Authors:

Kritzia Merced,

Shrikanth Narayanan,

David C. AtkinsAuthors Info & Claims

DIS '17: Proceedings of the 2017 Conference on Designing Interactive Systems

Pages 95 - 99

https://doi.org/10.1145/3064663.3064703

Published: 10 June 2017 Publication History

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.

References

[1]

Allen, J.E. et al. 1999. Mixed-initiative interaction. IEEE Intelligent Systems. 14, 5 (Sep. 1999), 14--23.

[2]

Amershi, S. et al. 2014. Power to the people: The role of humans in interactive machine learning. AI Magazine. 35, 4 (2014), 105--120.

Digital Library

[3]

Atkins, D.C. et al. 2014. Scaling up the evaluation of psychotherapy: evaluating motivational interviewing fidelity via statistical text classification. Implementation Science. 9, 1 (Dec. 2014).

[4]

Bødker, S. 1991. Through the interface: a human activity approach to user interface design. L. Erlbaum.

[5]

Can, D. et al. 2016. "It sounds like...": A natural language processing approach to detecting counselor reflections in motivational interviewing. Journal of Counseling Psychology. 63, 3 (2016), 343--350.

[6]

Catherine Havasi et al. 2010. Open mind common sense: crowd-sourcing for common sense. (2010).

[7]

Cook, J. and Hirsch, T. 2014. Monologger: visualizing engagement in doctor-patient conversation. (2014), 37--40.

Digital Library

[8]

Emerging Technology from the arXiv 2016. Neural Network Learns to Identify Criminals by Their Faces. MIT Technology Review.

[9]

Höök, K. et al. 1996. A glass box approach to adaptive hypermedia. User Modeling and UserAdapted Interaction. 6, 2--3 (Jul. 1996), 157--184.

[10]

Höök, K. 2000. Steps to take before intelligent user interfaces become real. Interacting with Computers. 12, 4 (Feb. 2000), 409--426.

[11]

Horvitz, E. 1999. Principles of mixed-initiative user interfaces. (1999), 159--166.

Digital Library

[12]

Huang, Y. et al. 2016. Combining contribution interactions to increase coverage in mobile participatory sensing systems. (2016), 365--376.

Digital Library

[13]

Irani, L.C. and Silberman, M.S. 2013. Turkopticon: interrupting worker invisibility in amazon mechanical turk. (2013), 611.

[14]

James Gibson et al. 2016. Developing an Automated Report Card for Addiction Counseling: The Counselor Observer Ratings Expert for MI (COREMI). (San Jose, CA, 2016).

[15]

Jameson, A. 2003. Adaptive interfaces and agents. The human-computer interaction handbook. L. Erlbaum Associates Inc. 305--330.

Digital Library

[16]

Kraut, R.E. et al. 2011. Building successful online communities: evidence-based social design. MIT Press.

Digital Library

[17]

Lambert, M.J. 2013. Bergin & Garfield's handbook of psychotherapy and behavior change. Wiley.

[18]

Law, E.L.M. and Von Ahn, L. 2011. Human computation. Morgan & Claypool Publishers.

Digital Library

[19]

Leahu, L. 2016. Ontological Surprises: A Relational Perspective on Machine Learning. (2016), 182--186.

Digital Library

[20]

Lindqvist, J. et al. 2011. I'm the mayor of my house: examining why people use foursquare - a social driven location sharing application. (2011), 2409.

Digital Library

[21]

Mahendran, A. and Vedaldi, A. 2015. Understanding deep image representations by inverting them. (Jun. 2015), 5188--5196.

[22]

Proctor, E.K. et al. 2009. Implementation Research in Mental Health Services: an Emerging Science with Conceptual, Methodological, and Training challenges. Administration and Policy in Mental Health and Mental Health Services Research. 36, 1 (Jan. 2009), 24--34.

[23]

Rollnick, S. and Miller, W.R. 1995. What is Motivational Interviewing? Behavioural and Cognitive Psychotherapy. 23, 04 (Oct. 1995), 325.

[24]

Scheid, T.L. 2003. Managed Care and the Rationalization of Mental Health Services. Journal of Health and Social Behavior. 44, 2 (Jun. 2003), 142.

[25]

Stumpf, S. et al. 2007. Toward harnessing user feedback for machine learning. (2007), 82.

Digital Library

[26]

Suchman, L.A. 2007. Human-machine reconfigurations: plans and situated actions. Cambridge University Press.

Digital Library

[27]

Sweeney, L. 2013. Discrimination in Online Ad Delivery. Queue. 11, 3 (Mar. 2013), 10.

Digital Library

[28]

Taylor, A. 2015. After interaction. interactions. 22, 5 (Aug. 2015), 48--53.

Digital Library

[29]

Tracey, T.J.G. et al. 2014. Expertise in psychotherapy: an elusive goal? The American Psychologist. 69, 3 (Apr. 2014), 218--229.

[30]

Verbeek, P.-P. 2015. COVER STORY Beyond interaction: a short introduction to mediation theory. interactions. 22, 3 (Apr. 2015), 26--31.

Digital Library

[31]

Whiteford, H.A. et al. 2013. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. The Lancet. 382, 9904 (Nov. 2013), 1575-- 1586.

[32]

Yang, Q. et al. 2016. Investigating the Heart Pump Implant Decision Process: Opportunities for Decision Support Tools to Help. (2016), 4477--4488.

Digital Library

[33]

Yang, Q. et al. 2016. Planning Adaptive Mobile Experiences When Wireframing. (2016), 565--576.

Digital Library

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Hoeppner SSamek M(2024)Procedural Fairness as Stepping Stone for Successful Implementation of Algorithmic Decision-Making in Public Administration: Review and OutlookAUC IURIDICA10.14712/23366478.2024.2470:2(85-99)Online publication date: 23-May-2024
https://doi.org/10.14712/23366478.2024.24
Tsiakas KMurray-Rust D(2024)Unpacking Human-AI interactions: From Interaction Primitives to a Design SpaceACM Transactions on Interactive Intelligent Systems10.1145/366452214:3(1-51)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/10.1145/3664522
Yoo DWoo HPendse SLu NBirnbaum MAbowd GDe Choudhury M(2024)Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI ResearchProceedings of the ACM on Human-Computer Interaction10.1145/36373728:CSCW1(1-24)Online publication date: 26-Apr-2024
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Index Terms

Designing Contestability: Interaction Design, Machine Learning, and Mental Health
1. Human-centered computing

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cover image ACM Conferences

DIS '17: Proceedings of the 2017 Conference on Designing Interactive Systems

June 2017

1444 pages

ISBN:9781450349222

DOI:10.1145/3064663

General Chair:
Oli Mival
Edinburgh Napier University, UK
,
Program Chairs:
Michael Smyth
Edinburgh Napier University, UK
,
Peter Dalsgaard
Aarhus University, Denmark

Copyright © 2017 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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New York, NY, United States

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Published: 10 June 2017

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National Institute of Health

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DIS '17

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SIGCHI

DIS '17: Designing Interactive Systems Conference 2017

June 10 - 14, 2017

Edinburgh, United Kingdom

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DIS '17 Paper Acceptance Rate 107 of 487 submissions, 22%;

Overall Acceptance Rate 1,158 of 4,684 submissions, 25%

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https://doi.org/10.14712/23366478.2024.24
Tsiakas KMurray-Rust D(2024)Unpacking Human-AI interactions: From Interaction Primitives to a Design SpaceACM Transactions on Interactive Intelligent Systems10.1145/366452214:3(1-51)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/10.1145/3664522
Yoo DWoo HPendse SLu NBirnbaum MAbowd GDe Choudhury M(2024)Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI ResearchProceedings of the ACM on Human-Computer Interaction10.1145/36373728:CSCW1(1-24)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637372
Pyle CZhang BHaimson OAndalibi N(2024)"I'm Constantly in This Dilemma": How Migrant Technology Professionals Perceive Social Media Recommendation AlgorithmsProceedings of the ACM on Human-Computer Interaction10.1145/36373428:CSCW1(1-33)Online publication date: 26-Apr-2024
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Karusala NUpadhyay SVeeraraghavan RGajos K(2024)Understanding Contestability on the Margins: Implications for the Design of Algorithmic Decision-making in Public ServicesProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641898(1-16)Online publication date: 11-May-2024
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