Chapter 39: Further Reading

Foundational Texts

Barocas, S., Hardt, M., and Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities. MIT Press. The definitive textbook on fairness in ML, covering definitions, impossibility results, and mitigation strategies. Freely available at https://fairmlbook.org/.
O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown. An accessible account of how algorithms can perpetuate and amplify societal inequities.
Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. Examines the material, social, and environmental costs of AI systems.

Buolamwini, J. and Gebru, T. (2018). "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification." FAccT 2018. Demonstrated significant accuracy disparities in commercial facial recognition across gender and skin type.
Hardt, M., Price, E., and Srebro, N. (2016). "Equality of Opportunity in Supervised Learning." NeurIPS 2016. Introduced the equalized odds and equal opportunity fairness definitions.
Chouldechova, A. (2017). "Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments." Big Data, 5(2). Proved the impossibility of simultaneously satisfying calibration and equalized odds when base rates differ.
Kleinberg, J., Mullainathan, S., and Raghavan, M. (2016). "Inherent Trade-Offs in the Fair Determination of Risk Scores." ITCS 2017. Independent proof of incompatibility between fairness criteria.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). "Fairness Through Awareness." ITCS 2012. Introduced the individual fairness framework.

Zhang, B. H., Lemoine, B., and Mitchell, M. (2018). "Mitigating Unwanted Biases with Adversarial Learning." AIES 2018. Adversarial debiasing for fair classification.
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C. (2013). "Learning Fair Representations." ICML 2013. Learning representations that are invariant to protected attributes.
Agarwal, A., Beygelzimer, A., Dudik, M., Langford, J., and Wallach, H. (2018). "A Reductions Approach to Fair Classification." ICML 2018. Reducing fair classification to a sequence of standard classification problems.

Dwork, C. (2006). "Differential Privacy." ICALP 2006. The foundational paper on differential privacy.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L. (2016). "Deep Learning with Differential Privacy." CCS 2016. Introduced DP-SGD for training neural networks with differential privacy.
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T. B., Song, D., Erlingsson, U., Oprea, A., and Raffel, C. (2021). "Extracting Training Data from Large Language Models." USENIX Security 2021. Demonstrated that GPT-2 memorizes and can regurgitate training data.
Shokri, R., Stronati, M., Song, C., and Shmatikov, V. (2017). "Membership Inference Attacks Against Machine Learning Models." IEEE S&P 2017. The foundational paper on membership inference attacks.

European Parliament and Council (2024). Regulation (EU) 2024/1689 (AI Act). The full text of the EU AI Act. Available at https://eur-lex.europa.eu/.
NIST (2023). AI Risk Management Framework (AI RMF 1.0). Voluntary US framework for managing AI risks. Available at https://www.nist.gov/artificial-intelligence/executive-order-safe-secure-and-trustworthy-artificial-intelligence.
Smuha, N. A. (2021). "From a 'Race to AI' to a 'Race to AI Regulation': Regulatory Competition for Artificial Intelligence." Law, Innovation and Technology, 13(1). Comparative analysis of AI regulation across jurisdictions.

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., and Gebru, T. (2019). "Model Cards for Model Reporting." FAccT 2019. Proposed standardized documentation for trained ML models.
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daume III, H., and Crawford, K. (2021). "Datasheets for Datasets." Communications of the ACM, 64(12). Proposed standardized documentation for datasets.

Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mane, D. (2016). "Concrete Problems in AI Safety." arXiv:1606.06565. Outlines five practical safety problems: avoiding side effects, reward hacking, scalable oversight, safe exploration, and distributional shift.
Ngo, R., Chan, L., and Mindermann, S. (2024). "The Alignment Problem from a Deep Learning Perspective." ICLR 2024. A modern treatment of AI alignment challenges specific to deep learning systems.
Hendrycks, D., Carlini, N., Mazeika, M., et al. (2022). "Unsolved Problems in ML Safety." arXiv:2109.13916. A comprehensive catalog of open problems in ML safety.

Strubell, E., Ganesh, A., and McCallum, A. (2019). "Energy and Policy Considerations for Deep Learning in NLP." ACL 2019. Quantified the carbon footprint of training large NLP models and called for energy reporting.
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., and Dean, J. (2021). "Carbon Emissions and Large Neural Network Training." arXiv:2104.10350. Google's analysis of the carbon footprint of training large models.

Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., and Ortega-Garcia, J. (2020). "DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection." Information Fusion, 64, 131--148. Comprehensive survey of deepfake generation and detection techniques.

Fairlearn: https://fairlearn.org/. Microsoft's open-source toolkit for assessing and improving fairness of AI systems.
AI Fairness 360 (AIF360): https://aif360.mybluemix.net/. IBM's comprehensive fairness toolkit with 70+ metrics and 10+ mitigation algorithms.
Opacus: https://opacus.ai/. PyTorch library for training with differential privacy (DP-SGD).
What-If Tool: https://pair-code.github.io/what-if-tool/. Google's interactive tool for probing ML models for fairness.