Week 2: Machine Learning, Training Data, and Bias

DSAN 5450: Data Ethics and Policy
Spring 2024, Georgetown University

Jeff Jacobs

jj1088@georgetown.edu

Wednesday, January 24, 2024

Overview: Slouching Towards Fairness

  • First half: Remaining high-level issues!
  • Second half: you’ll start to understand why I kept maniacally pointing to \(p \implies q\) on the board last lecture!
  • “Rules” for fairness are not “rules” at all! They’re statements of the form “If we accept ethical framework \(x\), then our algorithms ought to satisfy condition \(y\)”

\[ \underbrace{p(x)}_{\substack{\text{Accept ethical} \\ \text{framework }x}} \implies \underbrace{q(y)}_{\substack{\text{Algorithms should} \\ \text{satisfy condition }y}} \]

  • Last week: very broad intro to possible ethical frameworks (values for \(x\))
  • Today: very broad intro to possible fairness criteria (values for \(y\))
  • End of today: HW1: Nuts and Bolts for Evaluating Fairness

Ethical Issues in Data Science

  • Data Science for Who?
  • Operationalization
  • Fair Comparisons
  • Implementation

Data Science for Who(m)?

  • What are the processes by which data is measured, recorded, and distributed?

The Library of Missing Datasets. From D’Ignazio and Klein (2020)

Example: Measuring “Freedom” and “Human Rights”

  • Freedom House Ratings are the most common measure of “freedom” in a country, across social science literature; US State Dept. Country Reports on Human Rights Practices are the most common measure of “human rights” in a country, across social science literature
  • …So what’s the issue? (What is Jeff whining about this time?)

From Freedom House, Inc., Notes to Financial Statements (2023), p. 21

From state.gov/reports

Example: Measuring “Freedom” and “Human Rights”

Operationalization

  • Think of common claims made on basis of “data”:
    • Markets create economic prosperity
    • A glass of wine in the evening prevents cancer
    • Policing makes communities safer
  • How exactly are “prosperity”, “preventing cancer”, “policing”, “community safety” being measured?

Thumbnail from full video (Quarto crashes when I embed it directly 😑)

Thumbnail from full video (Quarto crashes when I embed it directly 😑)

Stiglitz, Sen, and Fitoussi (2010)

What Is Being Compared?

  • Are countries with 1 billion people comparable to countries with 10 million people?
  • Are countries which were colonized comparable to the colonizing countries?
  • When did the colonized countries gain independence?

Drèze and Sen (1991)

Implementation

From D’Ignazio and Klein (2020), Ch. 6 (see also)

From Lerman and Weaver (2014)

Fairness… 🧐

Figure 1: From Lily Hu, Direct Effects: How Should We Measure Racial Discrimination?, Phenomenal World, 25 September 2020
Figure 2: From Kasy and Abebe (2021)

…And INVERSE Fairness 🤯

From Machine Learning What Policymakers Value (Björkegren, Blumenstock, and Knight 2022)

Ethical Issues in Applying Data Science

Facial Recognition Algorithms

Facia.ai (2023)

Wellcome Collection (1890)

Ouz (2023)

Wang and Kosinski (2018)

Large Language Models

Figure 3: From Schiebinger et al. (2020)
Figure 4: From DeepLearning.AI’s Deep Learning course

Military and Police Applications of AI

Ayyub (2019)

McNeil (2022)

Machine Learning at 30,000 Feet

Three Component Parts of Machine Learning

  1. A cool algorithm 😎😍
  2. [Possibly benign but possibly biased] Training data ❓🧐
  3. Exploitation of below-minimum-wage human labor 😞🤐 (Dube et al. 2020, like and subscribe yall, get those ❤️s goin)

A Cool Algorithm 😎😍

Training Data With Acknowledged Bias

  • One potentially fruitful approach to fairness: since we can’t eliminate it, bring it out into the open and study it!
    • This can, at very least, help us brainstorm how we might “correct” for it (next slides!)

From Gendered Innovations in Science, Health & Medicine, Engineering, and Environment

Word Embeddings

Bolukbasi et al. (2016)

  • Notice how the \(x\)-axis has been selected by the researcher specifically to draw out (one) gendered dimension of language!
    • \(\overrightarrow{\texttt{she}}\) mapped to \(\langle -1,0\rangle\), \(\overrightarrow{\texttt{he}}\) mapped to \(\langle 1,0 \rangle\), others projected onto this dimension

Removing vs. Studying Biases

From Kozlowski, Taddy, and Evans (2019)

WordBias: An Interactive Tool for Discovering Intersectional Biases Encoded in Word Embeddings

References

Ayyub, Rami. 2019. “App Aims to Help Palestinian Drivers Find Their Way Around Checkpoints.” The Times of Israel, August. https://www.timesofisrael.com/app-aims-to-help-palestinian-drivers-find-their-way-around-checkpoints/.
Björkegren, Daniel, Joshua E. Blumenstock, and Samsun Knight. 2022. “(Machine) Learning What Policies Value.” arXiv. https://doi.org/10.48550/arXiv.2206.00727.
Bolukbasi, Tolga, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016. “Man Is to Computer Programmer as Woman Is to Homemaker? Debiasing Word Embeddings.” In Advances in Neural Information Processing Systems. Vol. 29. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html.
D’Ignazio, Catherine, and Lauren F. Klein. 2020. Data Feminism. MIT Press.
Drèze, Jean, and Amartya Sen. 1991. “China and India.” In Hunger and Public Action, 0. Oxford University Press. https://doi.org/10.1093/0198283652.003.0011.
Dube, Arindrajit, Jeff Jacobs, Suresh Naidu, and Siddharth Suri. 2020. “Monopsony in Online Labor Markets.” American Economic Review: Insights 2 (1): 33–46. https://doi.org/10.1257/aeri.20180150.
Facia.ai. 2023. “Facial Recognition Helps Vendors in Healthcare.” Facia.ai. https://facia.ai/blog/facial-recognition-healthcare/.
Kasy, Maximilian, and Rediet Abebe. 2021. “Fairness, Equality, and Power in Algorithmic Decision-Making.” In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 576–86. FAccT ’21. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3442188.3445919.
Kozlowski, Austin C., Matt Taddy, and James A. Evans. 2019. “The Geometry of Culture: Analyzing the Meanings of Class Through Word Embeddings.” American Sociological Review 84 (5): 905–49. https://doi.org/10.1177/0003122419877135.
Lerman, Amy E., and Vesla M. Weaver. 2014. Arresting Citizenship: The Democratic Consequences of American Crime Control. University of Chicago Press.
McNeil, Sam. 2022. “Israel Deploys Remote-Controlled Robotic Guns in West Bank.” AP News, November. https://apnews.com/article/technology-business-israel-robotics-west-bank-cfc889a120cbf59356f5044eb43d5b88.
Ouz. 2023. “Google Pixel 8 Face Unlock Vulnerability Discovered, Allowing Others to Unlock Devices.” Gizmochina. https://www.gizmochina.com/2023/10/16/google-pixel-8-face-unlock/.
Schiebinger, Londa, Ineke Klinga, Hee Young Paik, Inés Sánchez de Madariaga, Martina Schraudner, and Marcia Stefanick. 2020. “Machine Translation: Gendered Innovations.” http://genderedinnovations.stanford.edu/case-studies/nlp.html#tabs-2.
Stiglitz, Joseph E., Amartya Sen, and Jean-Paul Fitoussi. 2010. Mismeasuring Our Lives: Why GDP Doesn’t Add Up. The New Press.
Wang, Yilun, and Michal Kosinski. 2018. “Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation from Facial Images.” Journal of Personality and Social Psychology 114 (2): 246–57. https://doi.org/10.1037/pspa0000098.
Wellcome Collection. 1890. “Composite Photographs: "The Jewish Type".” https://wellcomecollection.org/works/ngq29vyw.

DSAN 5450 Week 2: Machine Learning, Training Data, and Bias