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.
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.
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).