research-article

Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI Research

Authors:

Sachin R. Pendse,

Nathaniel Young Lu,

Michael L. Birnbaum,

Gregory D. Abowd,

Munmun De ChoudhuryAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 8, Issue CSCW1

Article No.: 95, Pages 1 - 24

https://doi.org/10.1145/3637372

Published: 26 April 2024 Publication History

Abstract

In the mental health domain, patient engagement is key to designing human-centered technologies. CSCW and HCI researchers have delved into various facets of collaboration in AI research; however, previous research neglects the individuals who both produce the data and will be most impacted by the resulting technologies, such as patients. This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how we can improve human-centeredness in mental health AI research. We interviewed patient participants, AI researchers, and clinical researchers in a federally funded mental health AI research project. We used the concept of boundary objects to understand stakeholder collaboration. Our findings reveal that the social media data provided by patient participants functioned as boundary objects that facilitated stakeholder collaboration. Although the collaboration appeared to be successful, we argue that building consensus, or understanding each other's perspectives, can improve the human-centeredness of mental health AI research. Based on the findings, we provide suggestions for human-centered mental health AI research, working with data donors as domain experts, making invisible work visible, and privacy implications.

References

[1]

Rikke Aarhus and Stinne Aaløkke Ballegaard. 2010. Negotiating boundaries: Managing disease at home. Conference on Human Factors in Computing Systems - Proceedings, Vol. 2 (2010), 1223--1232. https://doi.org/10.1145/1753326.1753509

Digital Library

[2]

Marios Adamou, Grigoris Antoniou, Elissavet Greasidou, Vincenzo Lagani, Paulos Charonyktakis, and Ioannis Tsamardinos. 2018. Mining free-text medical notes for suicide risk assessment. In Proceedings of the 10th hellenic conference on artificial intelligence. 1--8.

Digital Library

[3]

Karla Badillo-Urquiola, Diva Smriti, Brenna McNally, Evan Golub, Elizabeth Bonsignore, and Pamela J Wisniewski. 2019. Stranger danger! social media app features co-designed with children to keep them safe online. In Proceedings of the 18th ACM International Conference on Interaction Design and Children. 394--406.

[4]

Shaowen Bardzell. 2010. Feminist HCI: Taking stock and outlining an agenda for design. Conference on Human Factors in Computing Systems - Proceedings, Vol. 2, 1301--1310. https://doi.org/10.1145/1753326.1753521

Digital Library

[5]

Eric PS Baumer. 2017. Toward human-centered algorithm design. Big Data & Society, Vol. 4, 2 (2017), 2053951717718854.

[6]

Eric PS Baumer. 2018. Socioeconomic Inequalities in the Non use of Facebook. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1--14.

Digital Library

[7]

Wiebe E Bijker. 1997. Of bicycles, bakelites, and bulbs: Toward a theory of sociotechnical change. MIT press.

[8]

Michael L. Birnbaum, Sindhu Kiranmai Ernala, Asra F. Rizvi, Elizabeth Arenare, Anna R. Van Meter, Munmun De Choudhury, and John M. Kane. 2019. Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook. npj Schizophrenia, Vol. 5 (12 2019), 17. Issue 1. https://doi.org/10.1038/s41537-019-0085--9

[9]

Geoffrey C Bowker and Susan Leigh Star. 2000. Sorting things out: Classification and its consequences. MIT press.

Digital Library

[10]

Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology, Vol. 3 (2006), 77--101. Issue 2. https://doi.org/10.1191/1478088706QP063OA

[11]

Virginia Braun and Victoria Clarke. 2016. (Mis)conceptualising themes, thematic analysis, and other problems with Fugard and Potts' (2015) sample-size tool for thematic analysis. International Journal of Social Research Methodology, Vol. 19 (11 2016), 739--743. Issue 6. https://doi.org/10.1080/13645579.2016.1195588

[12]

Anna Brown, Alexandra Chouldechova, Emily Putnam-Hornstein, Andrew Tobin, and Rhema Vaithianathan. 2019. Toward algorithmic accountability in public services: A qualitative study of affected community perspectives on algorithmic decision-making in child welfare services. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1--12.

Digital Library

[13]

Caroline Bull, Hanan Aljasim, and Douglas Zytko. 2021. Designing Opportunistic Social Matching Systems for Women's Safety During Face-to-Face Social Encounters. In Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing. 23--26.

Digital Library

[14]

Carrie J. Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2021. Onboarding Materials as Cross-functional Boundary Objects for Developing AI Assistants. Conference on Human Factors in Computing Systems - Proceedings (5 2021). https://doi.org/10.1145/3411763.3443435

Digital Library

[15]

Sarah Carr. 2020. ?AI gone mental': engagement and ethics in data-driven technology for mental health. https://doi.org/10.1080/09638237.2020.1714011, Vol. 29 (3 2020), 125--130. Issue 2. https://doi.org/10.1080/09638237.2020.1714011

[16]

Stevie Chancellor, Eric P.S. Baumer, and Munmun De Choudhury. 2019a. Who is the ?human" in human-centered machine learning: The case of predicting mental health from social media. Proceedings of the ACM on Human-Computer Interaction, Vol. 3 (2019), 32. Issue CSCW. https://doi.org/10.1145/3359249

Digital Library

[17]

Stevie Chancellor, Michael L Birnbaum, Eric D Caine, Vincent MB Silenzio, and Munmun De Choudhury. 2019b. A taxonomy of ethical tensions in inferring mental health states from social media. In Proceedings of the conference on fairness, accountability, and transparency. 79--88.

Digital Library

[18]

Stevie Chancellor and Munmun De Choudhury. 2020. Methods in predictive techniques for mental health status on social media: a critical review. NPJ digital medicine, Vol. 3, 1 (2020), 1--11.

[19]

Stevie Chancellor, Zhiyuan Lin, Erica L Goodman, Stephanie Zerwas, and Munmun De Choudhury. 2016a. Quantifying and predicting mental illness severity in online pro-eating disorder communities. In Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing. 1171--1184.

Digital Library

[20]

Stevie Chancellor, Tanushree Mitra, and Munmun De Choudhury. 2016b. Recovery amid pro-anorexia: Analysis of recovery in social media. In Proceedings of the 2016 CHI conference on human factors in computing systems. 2111--2123.

Digital Library

[21]

Xuetong Chen, Martin D Sykora, Thomas W Jackson, and Suzanne Elayan. 2018. What about mood swings: Identifying depression on twitter with temporal measures of emotions. In Companion Proceedings of the The Web Conference 2018. 1653--1660.

Digital Library

[22]

Munmun De Choudhury and Michael Gamon. 2013. Predicting Depression via Social Media. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, Vol. 2, 128--137.

[23]

Chia-Fang Chung, Kristin Dew, Allison M Cole, Jasmine Zia, James A Fogarty, Julie A Kientz, and Sean A Munson. 2016. Boundary Negotiating Artifacts in Personal Informatics: Patient-Provider Collaboration with Patient-Generated Data. Proceedings of CSCW (2016), 768--784. https://doi.org/10.1145/2818048.2819926

Digital Library

[24]

Glen Coppersmith, Ryan Leary, Eric Whyne, and Tony Wood. 2015. Quantifying suicidal ideation via language usage on social media. In Joint statistics meetings proceedings, statistical computing section, JSM, Vol. 110.

[25]

Orianna DeMasi and Benjamin Recht. 2017. A step towards quantifying when an algorithm can and cannot predict an individual's wellbeing. In Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers. 763--771.

Digital Library

[26]

Sunipa Dev, Masoud Monajatipoor, Anaelia Ovalle, Arjun Subramonian, Jeff M Phillips, and Kai-Wei Chang. 2021. Harms of gender exclusivity and challenges in non-binary representation in language technologies. EMNLP (2021).

[27]

Paul Dourish. 2004. What we talk about when we talk about context. Personal and ubiquitous computing, Vol. 8, 1 (2004), 19--30.

[28]

Simon D'Alfonso. 2020. AI in mental health. Current Opinion in Psychology, Vol. 36 (2020), 112--117.

[29]

Sindhu Kiranmai Ernala, Michael L Birnbaum, Kristin A Candan, Asra F Rizvi, William A Sterling, John M Kane, and Munmun De Choudhury. 2019. Methodological gaps in predicting mental health states from social media: triangulating diagnostic signals. In Proceedings of the 2019 chi conference on human factors in computing systems. 1--16.

Digital Library

[30]

Sindhu Kiranmai Ernala, Tristan Labetoulle, Fred Bane, Michael L Birnbaum, Asra F Rizvi, John M Kane, and Munmun De Choudhury. 2018. Characterizing Audience Engagement and Assessing Its Impact on Social Media Disclosures of Mental Illnesses. International AAAI Conference on Web and Social Media. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/view/17884

[31]

Chaonan Feng, Huimin Gao, Xuefeng B Ling, Jun Ji, and Yantao Ma. 2018. Shorten bipolarity checklist for the differentiation of subtypes of bipolar disorder using machine learning. In Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology. 162--166.

Digital Library

[32]

William R Frey, Desmond U Patton, Michael B Gaskell, and Kyle A McGregor. 2020. Artificial intelligence and inclusion: Formerly gang-involved youth as domain experts for analyzing unstructured twitter data. Social Science Computer Review, Vol. 38, 1 (2020), 42--56.

Digital Library

[33]

Nicholas Furlo, Jacob Gleason, Karen Feun, and Douglas Zytko. 2021. Rethinking Dating Apps as Sexual Consent Apps: A New Use Case for AI-Mediated Communication. In Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing. 53--56.

Digital Library

[34]

Sandra Gabriele and Sonia Chiasson. 2020. Understanding fitness tracker users' security and privacy knowledge, attitudes and behaviours. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1--12.

Digital Library

[35]

Martin Gjoreski, Hristijan Gjoreski, Mitja Luvs trek, and Matjavz Gams. 2016. Continuous stress detection using a wrist device: in laboratory and real life. In proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing: Adjunct. 1185--1193.

Digital Library

[36]

Felipe González, Yihan Yu, Andrea Figueroa, Claudia López, and Cecilia Aragon. 2019. Global reactions to the cambridge analytica scandal: A cross-language social media study. In Companion Proceedings of the 2019 world wide web conference. 799--806.

Digital Library

[37]

Brian Granger, Chris Colbert, and Ian Rose. 2017. JupyterLab: The next generation jupyter frontend. JupyterCon 2017 (2017).

[38]

Katrin H"ansel, Inna Wanyin Lin, Michael Sobolev, Whitney Muscat, Sabrina Yum-Chan, Munmun De Choudhury, John M Kane, and Michael L Birnbaum. 2021. Utilizing instagram data to identify usage patterns associated with schizophrenia spectrum disorders. Frontiers in Psychiatry, Vol. 12 (2021).

[39]

Gillian R. Hayes. 2011. The relationship of action research to human-computer interaction. ACM Transactions on Computer-Human Interaction, Vol. 18 (8 2011), 15:1--15:20. Issue 3. https://doi.org/10.1145/1993060.1993065

Digital Library

[40]

Tad Hirsch, Kritzia Merced, Shrikanth Narayanan, Zac E Imel, and David C Atkins. 2017. Designing contestability: Interaction design, machine learning, and mental health. In Proceedings of the 2017 Conference on Designing Interactive Systems. 95--99.

Digital Library

[41]

Naja Holten Møller, Irina Shklovski, and Thomas T Hildebrandt. 2020. Shifting concepts of value: Designing algorithmic decision-support systems for public services. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society. 1--12.

Digital Library

[42]

Mary Beth Kery, Marissa Radensky, Mahima Arya, Bonnie E John, and Brad A Myers. 2018. The story in the notebook: Exploratory data science using a literate programming tool. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1--11.

Digital Library

[43]

Seunghyun Kim, Afsaneh Razi, Gianluca Stringhini, Pamela J Wisniewski, and Munmun De Choudhury. 2021. A Human-Centered Systematic Literature Review of Cyberbullying Detection Algorithms. Proceedings of the ACM on Human-Computer Interaction, Vol. 5, CSCW2 (2021), 1--34.

Digital Library

[44]

Adam DI Kramer. 2010. An unobtrusive behavioral model of" gross national happiness". In Proceedings of the SIGCHI conference on human factors in computing systems. 287--290.

Digital Library

[45]

Ellen E Lee, John Torous, Munmun De Choudhury, Colin A Depp, Sarah A Graham, Ho-Cheol Kim, Martin P Paulus, John H Krystal, and Dilip V Jeste. 2021. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Vol. 6, 9 (2021), 856--864.

[46]

Min Kyung Lee, Daniel Kusbit, Anson Kahng, Ji Tae Kim, Xinran Yuan, Allissa Chan, Daniel See, Ritesh Noothigattu, Siheon Lee, Alexandros Psomas, et al. 2019. WeBuildAI: Participatory framework for algorithmic governance. Proceedings of the ACM on Human-Computer Interaction, Vol. 3, CSCW (2019), 1--35.

[47]

Wayne G Lutters and Mark S Ackerman. 2002. Achieving Safety: A Field Study of Boundary Objects in Aircraft Technical Support. Proceedings of the 2002 ACM conference on Computer supported cooperative work - CSCW '02 (2002). https://doi.org/10.1145/587078

Digital Library

[48]

Yaoli Mao, Dakuo Wang, Michael Muller, Kush R. Varshney, Ioana Baldini, Casey Dugan, and Aleksandra Mojsilovic. 2019. How data scientists work together with domain experts in scientific collaborations: To find the right answer or to ask the right qestion? Proceedings of the ACM on Human-Computer Interaction, Vol. 3 (12 2019). Issue GROUP. https://doi.org/10.1145/3361118

Digital Library

[49]

Margaret Mitchell, Kristy Hollingshead, and Glen Coppersmith. 2015. Quantifying the language of schizophrenia in social media. In Proceedings of the 2nd workshop on Computational linguistics and clinical psychology: From linguistic signal to clinical reality. 11--20.

[50]

Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model cards for model reporting. FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency (1 2019), 220--229. https://doi.org/10.1145/3287560.3287596

Digital Library

[51]

Annemarie Mol. 2002. The body multiple: Ontology in medical practice. Duke University Press.

[52]

Khatiya C Moon, Anna R Van Meter, Michael A Kirschenbaum, Asra Ali, John M Kane, and Michael L Birnbaum. 2021. Internet search activity of young people with mood disorders who are hospitalized for suicidal thoughts and behaviors: qualitative study of Google search activity. JMIR mental health, Vol. 8, 10 (2021), e28262.

[53]

Mehrab Bin Morshed, Koustuv Saha, Richard Li, Sidney K D'Mello, Munmun De Choudhury, Gregory D Abowd, and Thomas Plötz. 2019. Prediction of mood instability with passive sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 3, 3 (2019), 1--21.

Digital Library

[54]

Danielle Mowery, Hilary Smith, Tyler Cheney, Greg Stoddard, Glen Coppersmith, Craig Bryan, Mike Conway, et al. 2017. Understanding depressive symptoms and psychosocial stressors on Twitter: a corpus-based study. Journal of medical Internet research, Vol. 19, 2 (2017), e6895.

[55]

Michael Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q. Vera Liao, Casey Dugan, and Thomas Erickson. 2019. How data science workers work with data. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3290605.3300356

Digital Library

[56]

Michael Muller and Angelika Strohmayer. 2022. Data Silences in Data Preparation: Toward a Taxonomy. In ACM CHI Conference on Human Factors in Computing Systems.

[57]

Michael J Muller and Sarah Kuhn. 1993. Participatory design. Commun. ACM, Vol. 36, 6 (1993), 24--28.

Digital Library

[58]

Andrew B. Neang, Will Sutherland, Michael W. Beach, and Charlotte P. Lee. 2021. Data Integration as Coordination: The Articulation of Data Work in an Ocean Science Collaboration. Proceedings of the ACM on Human-Computer Interaction, Vol. 4 (1 2021), 1--25. Issue CSCW3. https://doi.org/10.1145/3432955

Digital Library

[59]

Albert Park, Mike Conway, and Annie T Chen. 2018. Examining thematic similarity, difference, and membership in three online mental health communities from Reddit: a text mining and visualization approach. Computers in human behavior, Vol. 78 (2018), 98--112.

[60]

Samir Passi and Steven J. Jackson. 2018. Trust in data science: Collaboration, translation, and accountability in corporate data science projects. Proceedings of the ACM on Human-Computer Interaction, Vol. 2 (11 2018). Issue CSCW. https://doi.org/10.1145/3274405

Digital Library

[61]

Andreas F. Phelps and Madhu Reddy. 2009. The influence of boundary objects on group collaboration in construction project teams. GROUP'09 - Proceedings of the 2009 ACM SIGCHI International Conference on Supporting Group Work (2009), 125--128. https://doi.org/10.1145/1531674.1531693

Digital Library

[62]

David Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael Muller, and Felix Portnoy. 2021. How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study. Proceedings of the ACM on Human-Computer Interaction, Vol. 5 (4 2021). Issue CSCW1. https://doi.org/10.1145/3449205

Digital Library

[63]

Mashfiqui Rabbi, Shahid Ali, Tanzeem Choudhury, and Ethan Berke. 2011. Passive and in-situ assessment of mental and physical well-being using mobile sensors. In Proceedings of the 13th international conference on Ubiquitous computing. 385--394.

Digital Library

[64]

Inioluwa Deborah Raji, Timnit Gebru, Margaret Mitchell, Joy Buolamwini, Joonseok Lee, and Emily Denton. 2020. Saving face: Investigating the ethical concerns of facial recognition auditing. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 145--151.

Digital Library

[65]

Afsaneh Razi, Seunghyun Kim, Ashwaq Alsoubai, Gianluca Stringhini, Thamar Solorio, Munmun De Choudhury, and Pamela J Wisniewski. 2021. A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection. Proceedings of the ACM on Human-Computer Interaction, Vol. 5, CSCW2 (2021), 1--38.

Digital Library

[66]

Mark O Riedl. 2019. Human-centered artificial intelligence and machine learning. Human Behavior and Emerging Technologies, Vol. 1, 1 (2019), 33--36.

[67]

Adam Rule, Aurélien Tabard, and James D Hollan. 2018. Exploration and explanation in computational notebooks. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1--12.

Digital Library

[68]

Koustuv Saha and Munmun De Choudhury. 2017. Modeling stress with social media around incidents of gun violence on college campuses. Proceedings of the ACM on Human-Computer Interaction, Vol. 1, CSCW (2017), 1--27.

Digital Library

[69]

Asif Salekin, Jeremy W Eberle, Jeffrey J Glenn, Bethany A Teachman, and John A Stankovic. 2018. A weakly supervised learning framework for detecting social anxiety and depression. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, Vol. 2, 2 (2018), 1--26.

Digital Library

[70]

Benjamin Saunders, Julius Sim, Tom Kingstone, Shula Baker, Jackie Waterfield, Bernadette Bartlam, Heather Burroughs, and Clare Jinks. 2018. Saturation in qualitative research: exploring its conceptualization and operationalization. Quality & quantity, Vol. 52 (2018), 1893--1907.

[71]

Devansh Saxena, Karla Badillo-Urquiola, Pamela J Wisniewski, and Shion Guha. 2021. A framework of high-stakes algorithmic decision-making for the public sector developed through a case study of child-welfare. Proceedings of the ACM on Human-Computer Interaction, Vol. 5, CSCW2 (2021), 1--41.

Digital Library

[72]

Devansh Saxena and Shion Guha. 2020. Conducting participatory design to improve algorithms in public services: Lessons and challenges. In Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing. 383--388.

Digital Library

[73]

Devansh Saxena, Seh Young Moon, Dahlia Shehata, and Shion Guha. 2022. Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis. In CHI Conference on Human Factors in Computing Systems. 1--22.

Digital Library

[74]

Morgan Klaus Scheuerman, Alex Hanna, and Emily Denton. 2021. Do datasets have politics? Disciplinary values in computer vision dataset development. Proceedings of the ACM on Human-Computer Interaction, Vol. 5, CSCW2 (2021), 1--37.

Digital Library

[75]

Christin Seifert, Stefanie Scherzinger, and Lena Wiese. 2019. Towards Generating Consumer Labels for Machine Learning Models. 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI), 173--179.

[76]

Judy Hanwen Shen and Frank Rudzicz. 2017. Detecting anxiety through reddit. In Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology-From Linguistic Signal to Clinical Reality. 58--65.

[77]

C Estelle Smith, Bowen Yu, Anjali Srivastava, Aaron Halfaker, Loren Terveen, and Haiyi Zhu. 2020. Keeping community in the loop: Understanding wikipedia stakeholder values for machine learning-based systems. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1--14.

Digital Library

[78]

Stephen Snow, Awais Hameed Khan, Stephen Viller, Ben Matthews, Scott Heiner, James Pierce, Ewa Luger, Richard Gomer, and Dorota Filipczuk. 2020. Speculative Designs for Emergent Personal Data Trails: Signs, Signals and Signifiers. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. 1--8.

Digital Library

[79]

Bernd Carsten Stahl and David Wright. 2018. Ethics and privacy in AI and big data: Implementing responsible research and innovation. IEEE Security & Privacy, Vol. 16, 3 (2018), 26--33.

[80]

Susan Leigh Star. 2010. This is Not a Boundary Object: Reflections on the Origin of a Concept:. http://dx.doi.org/10.1177/0162243910377624, Vol. 35 (8 2010), 601--617. Issue 5. https://doi.org/10.1177/0162243910377624

[81]

Susan Leigh Star and James R. Griesemer. 1989. Institutional Ecology, ?Translations' and Boundary Objects: Amateurs and Professionals in Berkeley's Museum of Vertebrate Zoology, 1907--39. Social Studies of Science, Vol. 19 (1989), 387--420. Issue 3. https://doi.org/10.1177/030631289019003001

[82]

Anja Thieme, Danielle Belgrave, and Gavin Doherty. 2020. Machine Learning in Mental Health. ACM Transactions on Computer-Human Interaction (TOCHI), Vol. 27 (8 2020), 34. Issue 5. https://doi.org/10.1145/3398069

Digital Library

[83]

Truyen Tran, Dinh Phung, Wei Luo, Richard Harvey, Michael Berk, and Svetha Venkatesh. 2013. An integrated framework for suicide risk prediction. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 1410--1418.

Digital Library

[84]

Michael Veale, Max Van Kleek, and Reuben Binns. 2018. Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 chi conference on human factors in computing systems. 1--14.

Digital Library

[85]

Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019. Designing theory-driven user-centric explainable AI. Conference on Human Factors in Computing Systems - Proceedings (5 2019). https://doi.org/10.1145/3290605.3300831

Digital Library

[86]

Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T Campbell. 2014. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. 3--14.

Digital Library

[87]

Dong Whi Yoo, Aditi Bhatnagar, Sindhu Kiranmai Ernala, Asra Ali, Michael L Birnbaum, Gregory D Abowd, and Munmun De Choudhury. 2023. Discussing Social Media During Psychotherapy Consultations: Patient Narratives and Privacy Implications. Proceedings of the ACM on Human-Computer Interaction, Vol. 7, CSCW1 (2023), 1--24.

[88]

Dong Whi Yoo, Michael L Birnbaum, Anna R Van Meter, Asra F Ali, Elizabeth Arenare, Gregory D Abowd, and Munmun De Choudhury. 2020. Designing a Clinician-Facing Tool for Using Insights From Patients' Social Media Activity: Iterative Co-Design Approach. JMIR Mental Health, Vol. 7 (2020), e16969. Issue 8. https://doi.org/10.2196/16969

[89]

Dong Whi Yoo, Sindhu Kiranmai Ernala, Bahador Saket, Domino Weir, Elizabeth Arenare, Asra F Ali, Anna R Van Meter, Michael L Birnbaum, Gregory D Abowd, and Munmun De Choudhury. 2021. Clinician perspectives on using computational mental health insights from patients' social media activities: design and qualitative evaluation of a prototype. JMIR mental health, Vol. 8, 11 (2021), e25455.

[90]

Camellia Zakaria, Rajesh Balan, and Youngki Lee. 2019. StressMon: scalable detection of perceived stress and depression using passive sensing of changes in work Routines and group interactions. Proceedings of the ACM on Human-Computer Interaction, Vol. 3, CSCW (2019), 1--29.

Digital Library

[91]

Amy X Zhang, Michael Muller, and Dakuo Wang. 2020. How do data science workers collaborate? roles, workflows, and tools. Proceedings of the ACM on Human-Computer Interaction, Vol. 4, CSCW1 (2020), 1--23.

Digital Library

Cited By

Kaushik PJain EGill KUpadhyay DDevliyal S(2024)Optimizing Mental Health Prediction by Fine-Tuning Decision Classifier Parameters for Enhanced Accuracy2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)10.1109/ICSCSS60660.2024.10625480(935-939)Online publication date: 10-Jul-2024
https://doi.org/10.1109/ICSCSS60660.2024.10625480

Index Terms

Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI Research
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing
  2. Human computer interaction (HCI)
    1. Empirical studies in HCI

Recommendations

Designing Patient-Centered Personal Health Records (PHRs): Health Care Professionals' Perspective on Patient-Generated Data

Currently, patients not only want access to various medical records their health care providers keep about them, but they also are willing to become active participants in managing their own health information and the health information of the ones they ...
Artificial intelligence in perinatal mental health research: A scoping review
Abstract
The intersection of Artificial Intelligence (AI) and perinatal mental health research presents promising avenues, yet uncovers significant challenges for innovation. This review explicitly focuses on this multidisciplinary field and undertakes a ...
Highlights
- Utilized PRISMA guidelines to review existing AI applications in perinatal mental health from 2013 to 2023.
- Identified the prevalent use of supervised machine learning techniques for feature selection and predicting relevant illnesses.
Provider Perspectives on Integrating Sensor-Captured Patient-Generated Data in Mental Health Care

The increasing ubiquity of health sensing technology holds promise to enable patients and health care providers to make more informed decisions based on continuously-captured data. The use of sensor-captured patient-generated data (sPGD) has been gaining ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 8, Issue CSCW1

CSCW

April 2024

6294 pages

EISSN:2573-0142

DOI:10.1145/3661497

Editor:
Jeff Nichols
Apple Inc., United States

Issue’s Table of Contents

Copyright © 2024 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2024

Published in PACMHCI Volume 8, Issue CSCW1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NIH (National Institutes of Health)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
324
Total Downloads

Downloads (Last 12 months)324
Downloads (Last 6 weeks)73

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kaushik PJain EGill KUpadhyay DDevliyal S(2024)Optimizing Mental Health Prediction by Fine-Tuning Decision Classifier Parameters for Enhanced Accuracy2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)10.1109/ICSCSS60660.2024.10625480(935-939)Online publication date: 10-Jul-2024
https://doi.org/10.1109/ICSCSS60660.2024.10625480

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents