Karrie Karahalios

About

Karrie G. Karahalios is a professor in the Department of Computer Science at the University of Illinois, with additional affiliations as an affiliate professor in the Unit for Criticism & Interpretive Theory, Electrical and Computer Engineering, and the School of Library and Information Science at the same university. She is also the co-director and founder of the Center for People and Infrastructures at the Coordinated Science Laboratory. Her educational background includes a Ph.D. in Media Arts and Sciences from the Massachusetts Institute of Technology, obtained in 2004. Karahalios's research focuses on understanding social and ethical aspects of computing, including investigating bias in social media and web search, analyzing emotional arousal in children with neurodevelopmental impairments, and diagnosing ethical harm in software algorithms. Her work often explores the societal impacts of technology, algorithmic bias, and the visualization of complex data to facilitate better human-computer interaction. She has contributed to the academic community through numerous publications in journals and conference proceedings, emphasizing her role as a leading researcher in her field.

Research topics

• Computer Science
• Sociology
• Engineering
• Internet privacy
• Accounting
• Business
• Visual arts
• Medical education
• Algorithm
• World Wide Web

Selected publications

• Auditing Algorithms: Understanding Algorithmic Systems from the Outside In

  Foundations and Trends® in Human–Computer Interaction · 2021 · 110 citations

  Algorithms are ubiquitous and critical sources of information online, increasingly acting as gatekeepers for users accessing or sharing information about virtually any topic, including their personal lives and those of friends and family, news and politics, entertainment, and even information about health and well-being. As a result, algorithmically-curated content is drawing increased attention and scrutiny from users, the media, and lawmakers alike. However, studying such content poses conside…

  PublisherDOI

• "At the End of the Day Facebook Does What ItWants"

  Proceedings of the ACM on Human-Computer Interaction · 2020-10-14 · 74 citations

  articleSenior author

  Interest has grown in designing algorithmic decision making systems for contestability. In this work, we study how users experience contesting unfavorable social media content moderation decisions. A large-scale online experiment tests whether different forms of appeals can improve users' experiences of automated decision making. We study the impact on users' perceptions of the Fairness, Accountability, and Trustworthiness of algorithmic decisions, as well as their feelings of Control (FACT). Su…

  PublisherDOI

• Random, Messy, Funny, Raw: Finstas as Intimate Reconfigurations of Social Media

  2020 · 67 citations

  Among many young people, the creation of a finsta-a portmanteau of "fake" and "Instagram" which describes secondary Instagram accounts-provides an outlet to share emotional, low-quality, or indecorous content with their close friends. To study why people create and maintain finstas, we conducted a qualitative study through interviews with finsta users and content analysis of video bloggers exposing their finsta on YouTube. We found that one way that young people deal with mounting social pressur…

  DOI

• Auditing Race and Gender Discrimination in Online Housing Markets

  Proceedings of the International AAAI Conference on Web and Social Media · 2020 · 59 citations

  Senior authorCorresponding

  While researchers have developed rigorous practices for offline housing audits to enforce the US Fair Housing Act, the online world lacks similar practices. In this work we lay out principles for developing and performing online fairness audits. We demonstrate a controlled sock-puppet audit technique for building online profiles associated with a specific demographic profile or intersection of profiles, and describe the requirements to train and verify profiles of other demographics. We also pre…

  PublisherOA PDFDOI

• Attitudes Surrounding an Imperfect AI Autograder

  2021 · 55 citations

  Senior authorCorresponding

  Deployment of AI assessment tools in education is widespread, but work on students’ interactions and attitudes towards imperfect autograders is comparatively lacking. This paper presents students’ perceptions surrounding a ∼ 90% accurate automated short-answer grader that determined homework and exam credit in a college-level computer science course. Using surveys and interviews, we investigated students’ knowledge about the autograder and their attitudes.

  DOI

Recent grants

• CHS: Medium: Collaborative Research: Beyond the Black Box: Understanding and Designing for User Expectations of Algorithmic Media

  NSF · $846k · 2016–2021

• CAREER: Enhancing Vocal Communication using Graphical Social Cues

  NSF · $500k · 2007–2013

Frequent coauthors

• Aditya Parameswaran

  28 shared

• Motahhare Eslami

  Carnegie Mellon University

  25 shared

• Christian Sandvig

  19 shared

• Ha‐Kyung Kong

  Rochester Institute of Technology

  18 shared

• Joshua Hailpern

  Hewlett-Packard (United States)

  17 shared

• Éric Gilbert

  17 shared

• Tony Bergstrom

  University of Illinois Urbana-Champaign

  15 shared

• Tarique Siddiqui

  Microsoft (United States)

  13 shared

Education

• PhD

  Massachusetts Institute of Technology

  2004

Awards & honors

• Celebration of Excellence 2023
• Celebration of Excellence 2022
• Celebration of Excellence 2021
• Celebration of Excellence 2024
• Celebration of Excellence 2025

Similar researchers at University of Illinois Urbana-Champaign

• Jiawei Hanh-146
• Kaiyu Guanh-86
• ChengXiang Zhaih-83