#7 / 2018
7/13

Literaturverzeichnis

Angwin, J., Kirchner, L., Larson, J., & Mattu, S. (2016, Mai 23). Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And it’s Biased Against Blacks. Abgerufen 11. Dezember 2016, von https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

Brennan Center for Justice. (2017, Mai 19). Brennan Center for Justice v. New York Police Department. Abgerufen 4. Januar 2018, von https://www.brennancenter.org/legal-work/brennan-center-justice-v-new-york-police-department

Consumer Reports. (2015). How a Credit Score Affects Your Car Insurance. Abgerufen von http://www.consumerreports.org/cro/car-insurance/credit-scores-affect-auto-insurance-rates/index.htm#creditmap

Danziger, S., Levav, J., & Avnaim-Pesso, L. (2011). Extraneous factors in judicial decisions. Proceedings of the National Academy of Sciences, 108(17), 6889–6892. https://doi.org/10.1073/pnas.1018033108

Heaton, B. (2015). New York City Fights Fire with Data. Government Technology. Abgerufen von http://www.govtech.com/public-safety/New-York-City-Fights-Fire-with-Data.html

Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353 (6301), 790–794.

Johnson, E. T. (2016, Juli 14). Special Order S09-11 Strategic Subject List (SSL) Dashboard. Abgerufen von http://directives.chicagopolice.org/directives/data/a7a57b85-155e9f4b-50c15-5e9f-7742e3ac8b0ab2d3.html

Lischka, K., & Klingel, A. (2017). Wenn Maschinen Menschen bewerten (Impuls Algorithmenethik No. #1). Bertelsmann Stiftung. Abgerufen von https://www.bertelsmann-stiftung.de/de/publikationen/publikation/did/wenn-maschinen-menschen-bewerten/

New York City Independent Budget Office. (2016). A Look at New York City’s Public High School Choice Process. New York City Independent Budget Office. Abgerufen von http://www.ibo.nyc.ny.us/iboreports/preferences-and-outcomes-a-look-at-new-york-citys-public-high-school-choice-process.pdf

O’Neil, C. (2016). Weapons of math destruction: how big data increases inequality and threatens democracy (First edition). New York: Crown.

O’Neil, C. (2017, Mai 15). Don’t Grade Teachers With a Bad Algorithm. Bloomberg.com. Abgerufen von https://www.bloomberg.com/view/articles/2017-05-15/don-t-grade-teachers-with-a-bad-algorithm

Patel, P. (2016, August 18). Fighting Poverty With Satellite Images and Machine-Learning Wizardry. Abgerufen 2. Januar 2017, von http://spectrum.ieee.org/tech-talk/aerospace/satellites/fighting-poverty-with-satellite-data-and-machine-learning-wizardry

Saunders, J., Hunt, P., Hollywood, J. S., Criminol, J. E., & Org, J. (2016). Predictions put into practice: a quasi-experimental evaluation of Chicago’s predictive policing pilot. https://doi.org/10.1007/s11292-016-9272-0

Schneider, J.; Yemane, R.; Weinmann, M. (2014). Diskriminierung am Ausbildungsmarkt. Ausmaß, Ursachen und Handlungsempfehlungen. Abgerufen von:
 https://www.svr-migration.de/wp-content/uploads/2014/03/SVR-FB_Diskriminierung-am-Ausbildungsmarkt.pdf

Singer, N. (2015, Februar 20). Bringing Big Data to the Fight Against Benefits Fraud. The New York Times. Abgerufen von https://www.nytimes.com/2015/02/22/technology/bringing-big-data-to-the-fight-against-benefits-fraud.html

Tullis, T. (2014, Dezember 5). How Game Theory Helped Improve New York City’s High School Application Process. The New York Times. Abgerufen von https://www.nytimes.com/2014/12/07/nyregion/how-game-theory-helped-improve-new-york-city-high-school-application-process.html

Weber, L., & Dwoskin, E. (2014, September 30). Are Workplace Personality Tests Fair? Wall Street Journal. Abgerufen von http://www.wsj.com/articles/are-workplace-personality-tests-fair-1412044257

Weltbank. (2015, September 30). FAQs: Global Poverty Line Update [Text/HTML]. Abgerufen 3. Januar 2017, von http://www.worldbank.org/en/topic/poverty/brief/global-poverty-line-faq

Zweig, K. A. (2018). Wo Maschinen irren können - Fehlerquellen und Verantwortlichkeiten in Prozessen algorithmischer Entscheidungsfindung. Bertelsmann Stiftung. Abgerufen von https://doi.org/10.11586/2018006