Foundations of ethical algorithms

หน้านี้สำหรับรายวิชา Foundations of Ethical Algorithms

เนื้อหา

• Week 1: Introduction
  • เอกสารอ้างอิง
    • 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
    • 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
    • 2nd Wave of Algorithmic Accountability: Julia Powles and Helen Nissenbaum, https://onezero.medium.com/the-seductive-diversion-of-solving-bias-in-artificial-intelligence-890df5e5ef53   |   Frank Pasquale, The Second Wave of Algorithmic Accountability &nbps; |  

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). Association for Computing Machinery, New York, NY, USA, 7.   |   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)