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Readings.txt
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Readings.txt
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# August 30
1. Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon
Link: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.441.9822&rep=rep1&type=pdf
2. Engaging the ethics of data science in practice
Link: https://dl.acm.org/citation.cfm?doid=3154816.3144172
3. The parable of Google Flu: traps in big data analysis
Link: http://science.sciencemag.org/content/343/6176/1203?casa_token=KgrXwVt-gjAAAAAA%3AN-0zkik1A19VjMjyXD6gI8wvW-an1EkeWj9kxHKrrls1Us-z_fB9UfRrAroiM3HE8LFF-DBT7BgC_w
4. HCI Across Borders
Link: https://dl.acm.org/citation.cfm?id=3108901
# September 6
1. The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision making
Link: http://journals.sagepub.com/doi/pdf/10.1177/0162243915605575
2. Obama Administration White House Report. 2016. Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights
Link: https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/2016_0504_data_discrimination.pdf
3. WHAT IS COMPUTER ETHICS?*
Link: https://pdfs.semanticscholar.org/2b26/2968529c25ebc2647f58cbb50a46fffcce17.pdf
4. Deconstructing statistical questions
Link: https://www.jstor.org/stable/pdf/2983526.pdf?casa_token=jGGz3LRnirAAAAAA:HvIXojFXbpqZSYqYB4dH9B3278j_VkNWiamEYSyL8CPwP8FzORgYPo3bIoIxPxchXX7p8Tj8BwlBPaKTxMQA8eUBWYcM6O4w0A5oaYYiVMuAs4E1uN-I5Q
# September 13
1. Bias in computer systems
Link: https://vsdesign.org/publications/pdf/64_friedman.pdf
2. Big data and its exclusions
Link: https://heinonline.org/HOL/Page?handle=hein.journals/slro66&div=9&g_sent=1&casa_token=&collection=journals
3. Economic Models of (Algorithmic) Discrimination
Link: http://www.mlandthelaw.org/papers/goodman2.pdf
4. Big data's disparate impact
Link: http://www.californialawreview.org/wp-content/uploads/2016/06/2Barocas-Selbst.pdf
# September 20
1. Auditing algorithms: Research methods for detecting discrimination on internet platforms
Link: https://pdfs.semanticscholar.org/b722/7cbd34766655dea10d0437ab10df3a127396.pdf
2. Algorithmic accountability: Journalistic investigation of computational power structures
Link: https://www.tandfonline.com/doi/full/10.1080/21670811.2014.976411
3. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination
Link: https://www.jstor.org/stable/pdf/3592802.pdf
4. Automated experiments on ad privacy settings
Link: https://www.degruyter.com/downloadpdf/j/popets.2015.1.issue-1/popets-2015-0007/popets-2015-0007.pdf
# September 27
1. Fairness through awareness
Link: https://arxiv.org/pdf/1104.3913.pdf
2. Certifying and removing disparate impact
Link: https://arxiv.org/pdf/1412.3756.pdf
3. On the (im)possibility of fairness
Link: https://arxiv.org/pdf/1609.07236.pdf
4. Fairness in criminal justice risk assessments: the state of the art.
Link: https://arxiv.org/pdf/1703.09207.pdf
# October 4
1. The scored society: due process for automated predictions
Link: https://heinonline.org/HOL/Page?handle=hein.journals/washlr89&div=4&g_sent=1&casa_token=&collection=journals
2. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability
Link: http://journals.sagepub.com/doi/pdf/10.1177/1461444816676645
3. Transparent predictions
Link: http://www.datascienceassn.org/sites/default/files/Transparent%20Predictions.pdf
4. Privacy, due process and the computational turn: the philosophy of law meets the philosophy of technology (Chapter 1)
Link: https://books.google.com/books?hl=en&lr=&id=2c9v5-fzU9EC&oi=fnd&pg=PP1&dq=Privacy,+due+process+and+the+computational+turn:+the+philosophy+of+law+meets+the+philosophy+of+technology&ots=f4HVOoSbat&sig=-nEMY5hnuvrljwtl12FtSMapJyQ#v=onepage&q=Privacy%2C%20due%20process%20and%20the%20computational%20turn%3A%20the%20philosophy%20of%20law%20meets%20the%20philosophy%20of%20technology&f=false
# October 11
1. Is Artificial Intelligence Permanently Inscrutable?
Link: http://nautil.us/issue/40/Learning/is-artificial-intelligence-permanently-inscrutable
2. How the machine ‘thinks’: Understanding opacity in machine learning algorithms
Link: http://journals.sagepub.com/doi/pdf/10.1177/2053951715622512
3. The mythos of model interpretability
Link: https://arxiv.org/pdf/1606.03490.pdf
4. Towards a rigorous science of interpretable machine learning
Link: https://arxiv.org/pdf/1702.08608.pdf
# November 1
1. Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards.
Link: https://dl.acm.org/citation.cfm?id=2675285
2. Bias and reciprocity in online reviews: Evidence from field experiments on airbnb
Link: https://dl.acm.org/citation.cfm?id=2764528
3. The Roots of Bias on Uber
Link: https://arxiv.org/abs/1803.08579
4. Evaluating Amazon's Mechanical Turk as a tool for experimental behavioral research
Link: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0057410