Jack Bandy
Clinical Assistant Professor
Department of Computer Science
Pronouns: He/Him/His
Contact
Building & Room:
CDRLC 3454
Address:
850 W. Taylor St., Chicago IL 60607
Office Phone:
Email:
CV Link:
Related Sites:
About
Jack Bandy joined UIC in Fall 2025. In his first year he taught five sections of CS 377: Ethical Issues in Computing. In Fall 2026, he is scheduled to teach two sections of CS 377 as well as one section of CS 418: Intro to Data Science.
Broadly, his scholarship focuses on human-computer interaction, ethical issues in computing, and computer-mediated communication (i.e. "algorithmic media").
Selected Publications
Bandy, Jack. Algorithmic Media and the Enshittified Town Square: How Big Tech Corrupts Democratic Communication. Palgrave Macmillan, 2026. https://link.springer.com/book/9783032194374
Hagar, Nick, and Jack Bandy. “Practical Datasets for Analyzing LLM Corpora Derived from Common Crawl.” Proceedings of the International AAAI Conference on Web and Social Media, vol. 19, pp. 2454–2464, 2025. https://doi.org/10.1609/icwsm.v19i1.35948
Chen, Jiahao, Jack Bandy, Dave Buckley, and Ruchi Bhatia. “AI Transparency in Practice: What Was Learnt from Third‑Party Audit of Recommender Systems at LinkedIn and Dailymotion.” Christchurch Call Initiative on Algorithmic Outcomes, report, Oct. 31, 2024.
Bandy, Jack, and Nicholas Diakopoulos. “Facebook’s News Feed Algorithm and the 2020 US Election.” Social Media+ Society, vol. 9, no. 3, 2023. https://doi.org/10.1177/20563051231196898
Bandy, Jack, and Tomo Lazovich. “Exposure to Marginally Abusive Content on Twitter.” Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, 2023. https://doi.org/10.1609/icwsm.v17i1.22123
Bandy, Jack. “Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits.” Proceedings of the ACM on Human-Computer Interaction, vol. 5, no. CSCW1, pp. 1–34, 2021. https://doi.org/10.1145/3449148
Bandy, Jack, and Nicholas Vincent. “Addressing ‘documentation debt’ in machine learning: A retrospective datasheet for bookcorpus.” Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2021.
Publication Aggregators
Education
Ph.D. in Computer Science & Communication Studies from Northwestern University (2023)
M.S. in Computer Science from University of Kentucky (2018)
B.S. in Computer Science from Wheaton College, IL (2016)
Research Currently in Progress
Most of my time and energy is currently channeled toward teaching. I also work on several research projects related to human-computer interaction and ethical issues in computing, outlined below. If you are interested in collaborating in some way, please reach out!
- Quality benchmarks for datasets
- Auditing heuristics in filtering pipelines
- Dataset transparency
- Consent and provenance in training data
- Connections between data filtering/preprocessing choices and model behavior
Risks and Harms from Large Language Models
- Frameworks for output evaluation (safety, accuracy, reliability, etc.)
- Homogenization due to LLM outputs (e.g. linguistic and rhetorical patterns from LLMs)
- Moral and ethical permissiveness of LLMs
- Misrepresentation in LLM outputs
- Persuasive effects of LLMs
- Analyzing content and dynamics in "algorithmic media" feeds (e.g. TikTok, YouTube, Instagram)
- Quantifying algorithmic suppression/amplification of sources, topics, genres, etc.
- Alternative feed designs, i.e. "algorithmic choice"
- Dashboards for feed transparency
Computer Science Education
- Embedding ethics exercises in other courses (e.g. data science, programming, etc.)
- Exercises for transparency, documentation, auditing in ML data and ML models
- Tracking/measuring growth in students' ethical reasoning
- Guiding students to connect real-world case studies with fictional scenarios