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Computer Science Department Colloquium Pursuing Transparency and Accountability in Data and Decision Processes
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Speaker: Amélie Marian

Bio

Amélie Marian is an Associate Professor in the Computer Science Department at Rutgers University. Her research interests are in Explainable Rankings, Accountability of Decision-making Systems, Personal Digital Traces, and Data Integration. Her recent public scholarship work on explaining the NYC School Admission lottery process to families, in collaboration with elected parent representatives, was instrumental in increasing transparency and accountability in the NYC high school application system and received an award from the NYC Citywide Council on High Schools recognizing leadership and service on behalf of New York City Public School Families in April 2024. Amélie received her Ph.D. in Computer Science from Columbia University in 2005. She is the recipient of a Microsoft Live Labs Award, three Google Research Awards, and an NSF CAREER award.

Location : CoRE 301

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Event Type: Computer Science Department Colloquium

Abstract: Algorithmic systems and data processes are being deployed to aid a wide range of high-impact decisions: from school applications or job interviews to gathering, storing, and analyzing personal data, or even performing critical tasks in the electoral process. These systems can have momentous consequences for the people they affect but their internal behaviors are often incompletely communicated to stakeholders, leaving them frustrated and distrusting of the outcomes of the decisions. Transparency and accountability are critical prerequisites for building trust in the results of decisions and guaranteeing fair and equitable outcomes.In this talk, I will present my work on making these opaque processes more transparent and accountable to the public in several real-world applications. In particular, I will discuss how ranking aggregation functions traditionally used in decision systems inadequately reflect the intention of the decision-makers and present work on providing transparent metrics to clarify the ranking process. In addition, ranking functions that are used in resource allocation systems often produce disparate results because of bias in the underlying data, I will show how compensatory bonus points can transparently address disparate outcomes in ranking applications. Furthermore, real-world deployments of algorithmic decision-making solutions often run into implementation constraints that impact the guarantees of the underlying algorithms, I will discuss practical challenges associated with algorithms used in the electoral process and their impacts on the trustworthiness of electoral outcomes. Organizations do not have strong incentives to explain and clarify their decision processes; however, stakeholders are not powerless and can strategically combine their efforts to push for more transparency. I will discuss the results and lessons learned from such an effort: a parent-led crowdsourcing campaign to increase transparency in the New York City school admission process. This work highlights the need for oversight and AI governance to improve the trust of stakeholders who have no choice but to interact with automated decision systems.

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Contact  Professor Ulrich Kremer

Join Zoom Meeting
https://rutgers.zoom.us/j/2014444359?pwd=WW9ybFNCNVFrUWlycHowSHdNZjhzUT09

Meeting ID: 201 444 4359
Password: 550978