ผลต่างระหว่างรุ่นของ "Foundations of ethical algorithms"

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* Week 1: Introduction
 
* Week 1: Introduction
 
** เอกสารอ้างอิง
 
** เอกสารอ้างอิง
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*** [https://dataprivacylab.org/projects/identifiability/paper1.pdf L. Sweeney, Simple Demographics Often Identify People Uniquely. Carnegie Mellon University, Data Privacy Working Paper 3. Pittsburgh 2000.]
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*** Privacy
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*** Netflix Prize. [https://www.cs.cornell.edu/~shmat/shmat_oak08netflix.pdf Arvind Narayanan and Vitaly Shmatikov, How To Break Anonymity of the Netflix Prize Dataset]   |   [https://www.cs.cornell.edu/~shmat/netflix-faq.html FAQ]
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**** [https://dataprivacylab.org/projects/identifiability/paper1.pdf L. Sweeney, Simple Demographics Often Identify People Uniquely. Carnegie Mellon University, Data Privacy Working Paper 3. Pittsburgh 2000.]
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*** GWAS privacy. [https://pubmed.ncbi.nlm.nih.gov/18769715/ 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]
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**** Netflix Prize. [https://www.cs.cornell.edu/~shmat/shmat_oak08netflix.pdf Arvind Narayanan and Vitaly Shmatikov, How To Break Anonymity of the Netflix Prize Dataset]   |   [https://www.cs.cornell.edu/~shmat/netflix-faq.html FAQ]
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*** Word embedding. [https://arxiv.org/abs/1607.06520 Bolukbasi, Chang, Zou, Saligrama, Kalai. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings.]
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**** GWAS privacy. [https://pubmed.ncbi.nlm.nih.gov/18769715/ 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]
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*** COMPAS. [https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Machine Bias (ProPublica)]   |   [https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm How We Analyzed the COMPAS Recidivism Algorithm (ProPublica) by Jeff Larson, Surya Mattu, Lauren Kirchner and Julia Angwin]
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*** Fairness
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*** 2nd Wave of Algorithmic Accountability: [https://onezero.medium.com/the-seductive-diversion-of-solving-bias-in-artificial-intelligence-890df5e5ef53 Julia Powles and Helen Nissenbaum, https://onezero.medium.com/the-seductive-diversion-of-solving-bias-in-artificial-intelligence-890df5e5ef53]   |   [https://lpeproject.org/blog/the-second-wave-of-algorithmic-accountability/ Frank Pasquale, The Second Wave of Algorithmic Accountability]   |   [https://dl.acm.org/doi/abs/10.1145/3375627.3375839 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.]    |   [https://boingboing.net/2019/12/04/fundamental-critique.html Doctorow, Second wave Algorithmic Accountability: from "What should algorithms do?" to "Should we use an algorithm?", BoingBoing]
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**** CACM Review. [https://cacm.acm.org/magazines/2020/5/244336-a-snapshot-of-the-frontiers-of-fairness-in-machine-learning/fulltext Chouldechova and Roth, A Snapshot of the Frontiers of Fairness in Machine Learning, CACM, May 2020]
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**** Word embedding. [https://arxiv.org/abs/1607.06520 Bolukbasi, Chang, Zou, Saligrama, Kalai. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings.]
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**** COMPAS. [https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Machine Bias (ProPublica)]   |   [https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm How We Analyzed the COMPAS Recidivism Algorithm (ProPublica) by Jeff Larson, Surya Mattu, Lauren Kirchner and Julia Angwin]
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**** Hiring bias. [https://hbr.org/2019/05/all-the-ways-hiring-algorithms-can-introduce-bias Miranda Bogen, All the Ways Hiring Algorithms Can Introduce Bias, HBR, May 2019]
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**** Bias in facial recognition. [https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html  Steve Lohr. Facial Recognition Is Accurate, if You’re a White Guy, NYT]
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*** Interpretability
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**** CACM Review. [https://cacm.acm.org/magazines/2020/1/241703-techniques-for-interpretable-machine-learning/fulltext Du, Liu, Hu. Techniques for Interpretable Machine Learning. CACM, Jan 2020]
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*** 2nd Wave of Algorithmic Accountability
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**** [https://onezero.medium.com/the-seductive-diversion-of-solving-bias-in-artificial-intelligence-890df5e5ef53 Julia Powles and Helen Nissenbaum, The Seductive Diversion of ‘Solving’ Bias in Artificial Intelligence]
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**** [https://lpeproject.org/blog/the-second-wave-of-algorithmic-accountability/ Frank Pasquale, The Second Wave of Algorithmic Accountability]  
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**** [https://dl.acm.org/doi/abs/10.1145/3375627.3375839 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)]   
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**** [https://boingboing.net/2019/12/04/fundamental-critique.html Doctorow, Second wave Algorithmic Accountability: from "What should algorithms do?" to "Should we use an algorithm?", BoingBoing]
  
 
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