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Details

  • Class: DSC 290
  • Instructor: R. Stuart Geiger
  • Time: 5-6:50pm on Tuesdays
  • Place: SOLIS 109
  • Units: 2

Description

This seminar is for students interested in empirically investigating the outputs of real-world algorithmic systems of all kinds, particularly those where the code and/or training data are not publicly available. The first few weeks of the class will include more readings and lectures, when we cover the history of auditing and legal/ethical issues it raises. This includes studying classic audits of non-algorithmic decision systems (e.g. equal opportunity hiring investigations) to contemporary issues around the Computer Fraud and Abuse Act and the IRB. We will learn various approaches to investigate such systems, including auditing via training datasets, code, user reports, API scraping, sockpuppet accounts, and headless browsers. We will read and discuss various algorithmic audits by researchers and regulators, which will be a mix of selected readings and readings students choose. The second half of the class will be more discussion- and activity-based, as we perform audits on several real-world models whose developers have encouraged public auditing (e.g. Wikipedia’s content moderation classifiers). Students will work towards a final project, where they will conduct their own audits and develop strategies for how systems can be designed for auditability.

Prerequsites

There are no official prerequsites to register and students from all departments are welcome to enroll. The class will generally assume knowledge of:

  • Introductory Statistics: The math and statistics of basic auditing are not as complex as those used in developing machine learning algorithms, but does involve statistics at the undergraduate level: correlations, hypothesis testing, linear regressions, and analysis of variance (ANOVA) (e.g. what is taught in COGS 14B). You don't need to know how to mathematically derive these tests, just enough to be a well-informed user of them. However, some metrics of fairness, bias, causal inference, and related concepts do involve more advanced math and statistics, and students who want to work with such metrics will be able to do so.
  • Introductory programming for data collection and analysis: A working knowledge of a scripting language like python or R is highly recommended (e.g. what is taught in CSE 8A or this coursera class). Some audit methods involve automated data collection. We will learn how to query APIs and run headless browsers using standard libraries. Jupyter Notebooks will be used for literate programming. This class will be registered for UCSD datahub, which provides a web/cloud-based Jupyter environment in python and R.
  • All students must take and pass the UCSD/CITI IRB Human Subject Protection Training online course (Social and Behavioral Basic Course), by the end of week 2 of the class. This takes about 2-3 hours total and can be taken at any time, even during the summer. Register at citiprogram.org (no SSO available) and affiliate with UCSD. See more info at this video of me registering for the proper course. If you have passed the course in the past 3 years, your certificate is still valid; if not, you must take the shorter refresher course.

Please get in touch if you have any doubts or concerns about the prerequsites.

Potential readings

Note that the final reading list and schedule has not yet been finalized. Please reach out to Stuart Geiger if you have any suggestions or ideas. And thanks to auditingalgorithms.science for many of these!

What is an audit?

Classic auditing in non-algorithmic systems

Algorithmic auditing frameworks and introductions

The legal and ethical issues of conducting audits (and violating Terms of Service to do so)

The UMN Linux kernel security audit

On legal challenges to algorithms used by government agencies

  • Citron, Danielle Keats, and Frank A. Pasquale. 2014. "The Scored Society: Due Process for Automated Predictions." Washington Law Review 89. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2376209
  • Citron, Danielle Keats, Technological Due Process. Washington University Law Review, Vol. 85, pp. 1249-1313, 2007. READ § I.A., II.B.2, III.C.2. http://ssrn.com/abstract=1012360
  • Stuart, G., "Databases, Felons, and Voting: Errors and Bias in the Florida Felons Exclusion List in the 2000 Presidential Elections" (September 2002). KSG Working Paper Series RWP 02-041. Read pp. 22-40. http://ssrn.com/abstract=336540

Metrics of fairness, bias, and related concepts

(A big TBD here!)

Cases of algorithmic audits

Online advertising and pricing

Facial, biometric, speech recognition

  • Buolamwini, J., & Gebru, T. (2018, January). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77-91). PMLR. http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf
  • Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J.R., Jurafsky, D. and Goel, S., 2020. "Racial disparities in automated speech recognition." Proceedings of the National Academy of Sciences, 117(14), pp.7684-7689. https://www.pnas.org/content/117/14/7684
  • Tatman, R. (2017, April). Gender and dialect bias in YouTube’s automatic captions. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing (pp. 53-59). https://www.aclweb.org/anthology/W17-1606.pdf
  • Tatman, R., & Kasten, C. (2017, August). Effects of Talker Dialect, Gender & Race on Accuracy of Bing Speech and YouTube Automatic Captions. In Interspeech (pp. 934-938). https://www.isca-speech.org/archive/Interspeech_2017/pdfs/1746.PDF
  • Raji, I. D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., & Denton, E. (2020, February). Saving face: Investigating the ethical concerns of facial recognition auditing. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 145-151). https://dl.acm.org/doi/pdf/10.1145/3375627.3375820

Recommender systems and search engine rankings

  • Marc Faddoul, Guillaume Chaslot, and Hany Farid. 2020. A longitudinal analysis of YouTube’s promotion of conspiracy videos. arxiv:2003.03318 [cs.CY] https://arxiv.org/abs/2003.0331

  • Ulloa, R., Makhortykh, M., & Urman, A. (2021). "Algorithm Auditing at a Large-Scale: Insights from Search Engine Audits." arXiv preprint arXiv:2106.05831. https://arxiv.org/abs/2106.05831

  • Noble, S. U. (2018). Algorithms of oppression. New York University Press.

Social media and user-generated content (mostly NLP / sentiment analysis)

  • M. Eslami, K. Vaccaro, K. Karahalios, and K. Hamilton. “Be careful; things can be worse than they appear”: Understanding Biased Algorithms and Users’ Behavior around Them in Rating Platforms (ICWSM 2017). http://social.cs.uiuc.edu/papers/ICWSM17-PrePrint.pdf
  • King, G., Pan, J., & Roberts, M. E. (2014). Reverse-engineering censorship in China: Randomized experimentation and participant observation. Science, 345(6199). https://science.sciencemag.org/content/345/6199/1251722.abstract
  • Blodgett, S. L., & O'Connor, B. (2017). Racial disparity in natural language processing: A case study of social media african-american english. arXiv preprint arXiv:1707.00061. https://arxiv.org/pdf/1707.00061.pdf
  • Kiritchenko, S., & Mohammad, S. M. (2018). Examining gender and race bias in two hundred sentiment analysis systems. arXiv preprint arXiv:1805.04508. https://arxiv.org/abs/1805.04508
  • Rios, A. (2020, April). FuzzE: Fuzzy fairness evaluation of offensive language classifiers on African-American English. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 881-889). https://doi.org/10.1609/aaai.v34i01.5434
  • Davidson, T., Bhattacharya, D., & Weber, I. (2019). Racial bias in hate speech and abusive language detection datasets. arXiv preprint arXiv:1905.12516. https://arxiv.org/pdf/1905.12516

Hiring, admissions, and other social sorting

The ProPublica vs. COMPAS/Northpointe debate on COMPAS criminal risk scores

Other

  • Stuart, G., "Databases, Felons, and Voting: Errors and Bias in the Florida Felons Exclusion List in the 2000 Presidential Elections" (September 2002). KSG Working Paper Series RWP 02-041. Read pp. 22-40. http://ssrn.com/abstract=336540

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