Kara Schechtman will present her General Exam "Causal Evaluation of Algorithmic Decision Support’s Organizational Role" on Thursday, May 14, 2026 at 10:30 AM in Sherrerd 306.

Committee Members: Lydia Liu (advisor), Aleksandra Korolova, Arvind Narayanan

Abstract:

In high stakes organizational decision-making settings such as education, healthcare, and hiring, algorithms are increasingly used to support human decision-makers or even automate certain decisions entirely. Building a full scientific understanding of algorithmic decision support’s impacts requires grappling with the complexity of the organizations in which they operate, and the organizational goals we aim to achieve. In this talk, I present on recent and ongoing work which aim to contribute to this growing understanding.

First, I present recent work on the role of human expertise in algorithmic intervention targeting. Algorithmic decision support (ADS) is widely used to help human-decision makers target interventions, yet little is known about how human decision makers integrate algorithmic suggestions with contextual knowledge unavailable to algorithmic tools in settings where their actions influence outcomes. We develop a causal framework to test whether human decision-makers use contextual information beyond algorithmic inputs in order to improve outcomes. Applying the framework to rich longitudinal data from an algorithm-assisted college advising program, we estimate advisors relied on “non-algorithmic context” that 2 out of 3 recorded interventions plausibly relied on non-algorithmic context, corroborating this finding by systematic qualitative analysis of advisor meeting notes. These results suggest that human discretion remains a dominant driver of educational interventions even in algorithmic-assisted settings, and highlight the importance of scaling up human capacity to contextualize and act on algorithmic recommendations when expanding ADS.

Second, I outline the methodological commitments that inform my work — using a causal approach to evaluate organizational ADS, while drawing on social theory and qualitative research on street-level bureaucrats’ use of ADS to inform problem selection for empirical research. I explain how these methodologies coalesce in prior and ongoing work on the role of ADS in college advising, the role of correspondence studies in language model discrimination auditing, and the role of data collection in medical and public health decision making, as well as my goals for future work in these areas.

Reading List:

https://docs.google.com/document/d/1ZnR1Mmiep0u_pQ22eAMykzH4Evpep5tzmR5t-rOBw-s/edit?usp=sharing

Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.