Human-Centered Algorithm Design

Algorithm design traditionally hinges on optimizing quantitative performance metrics: precision, recall, area under the ROC curve, comparison against a human on the same task, etc. However, algorithms from diverse areas within computer science are being increasingly incorporated into interactive systems. The application domains for these algorithmic systems – commerce, criminal justice, transportation, advertising, cybersecurity, and others – introduce novel, complex design considerations that go beyond such metrics as precision, recall, or false positive rates.

One way of addressing these issues is to incorporate human users in the design process sooner and more often. While HCI has a variety of user-centered design methods, they are intended for designing interfaces rather than underlying algorithms. Currently, we lack well-developed methods of incorporating lay persons, both users and others, into the process of designing algorithmically-based interactive systems. This work aims to develop such methods, working closely with a variety of groups and organizations on application domains including data journalism, legal analysis, and mental health support.

This work is supported in part by an NSF CAREER grant and an NSF CHS grant (collaborative with Munmun De Choudhury).


Dilruba Showkat and Eric P. S. Baumer. 2022. “It’s Like the Value System in the Loop”: Domain Experts’ Values Expectations for NLP Automation. In Proceedings of the ACM Conference on Designing Interactive Systems (DIS), 100–122.

Dilruba Showkat and Eric P. S. Baumer. 2021. Where Do Stories Come From? Examining the Exploration Process in Investigative Data Journalism. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2: 390:1-390:31.

Anjali Devakumar, Jay Modh, Bahador Saket, Eric P. S. Baumer, and Munmun De Choudhury. 2021. A Review on Strategies for Data Collection, Reflection, and Communication in Eating Disorder Apps. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), 1–19.

Lauren C. Taylor, Kelsie Belan, Munmun De Choudhury, and Eric P. S. Baumer. 2021. Misfires, Missed Data, Misaligned Treatment: Disconnects in Collaborative Treatment of Eating Disorders. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1: 31:1-31:28.

Eric P. S. Baumer, Mark Blythe, and Theresa J. Tanenbaum. (2020). Evaluating Design Fiction: The Right Tool for the Job. in ACM Conference on Designing Interactive Systems (DIS). (Eindhoven, the Netherlands; held virtually). [24.0% acceptance rate; Honorable Mention Award]

Eric P. S. Baumer, Drew Siedel, Lena McDonnell, Jiayun Zhong, Patricia Sittikul, and Micki McGee. (2020). Topicalizer: Reframing Core Concepts in Machine Learning Visualization by Co-designing for Interpretivist Scholarship. Human-Computer Interaction, Special Issue on Unifying Human Computer Interaction and Artificial Intelligence 35(5-6): 452-480.

Stevie Chancellor, Eric P. S. Baumer, and Munmun De Choudhury. (2019). Who is the “Human” in Human-Centered Machine Learning: The Case of Predicting Mental Health from Social Media. Proceedings of the ACM Human-Computer Interaction 2, CSCW. [Honorable Mention Award]

Eric P. S. Baumer and Micki McGee. (2019). Speaking on Behalf of: Representation, Authority, and Delegation in Computational Text Analysis. in Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES). (Honolulu, HI).

Eric P. S. Baumer. (2017). Toward Human-Centered Algorithm Design. Big Data & Society, 4(2).