Foundations of ethical algorithms

หน้านี้สำหรับรายวิชา Foundations of Ethical Algorithms

เนื้อหา

• Week 1: Introduction
  • เอกสารอ้างอิง
    • Privacy
      • L. Sweeney, Simple Demographics Often Identify People Uniquely. Carnegie Mellon University, Data Privacy Working Paper 3. Pittsburgh 2000.
      • Netflix Prize. Arvind Narayanan and Vitaly Shmatikov, How To Break Anonymity of the Netflix Prize Dataset   |   FAQ
      • GWAS privacy. Homer N, Szelinger S, Redman M, et al. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet. 2008;4(8):e1000167. Published 2008 Aug 29. doi:10.1371/journal.pgen.1000167
    • Fairness
      • CACM Review. Chouldechova and Roth, A Snapshot of the Frontiers of Fairness in Machine Learning, CACM, May 2020
      • Word embedding. Bolukbasi, Chang, Zou, Saligrama, Kalai. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings.
      • COMPAS. Machine Bias (ProPublica)   |   How We Analyzed the COMPAS Recidivism Algorithm (ProPublica) by Jeff Larson, Surya Mattu, Lauren Kirchner and Julia Angwin
      • Hiring bias. Miranda Bogen, All the Ways Hiring Algorithms Can Introduce Bias, HBR, May 2019
      • Bias in facial recognition. Steve Lohr. Facial Recognition Is Accurate, if You’re a White Guy, NYT
    • Interpretability
      • CACM Review. Du, Liu, Hu. Techniques for Interpretable Machine Learning. CACM, Jan 2020
    • 2nd Wave of Algorithmic Accountability
      • Julia Powles and Helen Nissenbaum, The Seductive Diversion of ‘Solving’ Bias in Artificial Intelligence
      • Frank Pasquale, The Second Wave of Algorithmic Accountability
      • Frank Pasquale. 2020. Machines Judging Humans: The Promise and Perils of Formalizing Evaluative Criteria. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES ’20)
      • Doctorow, Second wave Algorithmic Accountability: from "What should algorithms do?" to "Should we use an algorithm?", BoingBoing

อ้างอิง

รายวิชาจะอ้างอิงเนื้อหาจากหลายแหล่ง ดังนี้

• หนังสือ The Algorithmic Foundations of Differential Privacy โดย Cynthia Dwork และ Aaron Roth
• Science of Data Ethics - UPenn สอนโดย Michael Kearns และ Kristian Lum
• Ethics in Data Science - UTah สอนโดย Suresh Venkatasubramanian และ Katie Shelef
• Foundations of Fairness in Machine Learning - UW สอนโดย Jamie Morgenstern
• Explainable AI in Industry: Practical Challenges and Lessons Learned (ACM FAT* 2020 Tutorial)