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%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/

%% Created for Laine, Riku P at 2019-08-02 09:38:23 +0300 


%% Saved with string encoding Unicode (UTF-8) 



@inproceedings{tolan2019why,
	Acmid = {3326705},
	Address = {New York, NY, USA},
	Author = {Tolan, Song\"{u}l and Miron, Marius and G\'{o}mez, Emilia and Castillo, Carlos},
	Booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law},
	Date-Added = {2019-08-02 06:37:23 +0000},
	Date-Modified = {2019-08-02 06:38:23 +0000},
	Doi = {10.1145/3322640.3326705},
	Isbn = {978-1-4503-6754-7},
	Keywords = {algorithmic bias, algorithmic fairness, criminal recidivism, machine learning, risk assessment},
	Location = {Montreal, QC, Canada},
	Numpages = {10},
	Pages = {83--92},
	Publisher = {ACM},
	Series = {ICAIL '19},
	Title = {Why Machine Learning May Lead to Unfairness: Evidence from Risk Assessment for Juvenile Justice in Catalonia},
	Url = {http://doi.acm.org/10.1145/3322640.3326705},
	Year = {2019},
	Bdsk-Url-1 = {http://doi.acm.org/10.1145/3322640.3326705},
	Bdsk-Url-2 = {http://dx.doi.org/10.1145/3322640.3326705}}

@article{pearl2010introduction,
	Author = {Pearl, Judea},
	Date-Added = {2019-08-02 06:37:23 +0000},
	Date-Modified = {2019-08-02 06:37:23 +0000},
	Journal = {The international journal of biostatistics},
	Number = {2},
	Publisher = {De Gruyter},
	Title = {An introduction to causal inference},
	Volume = {6},
	Year = {2010}}

@inproceedings{lakkaraju2017selective,
	Acmid = {3098066},
	Address = {New York, NY, USA},
	Author = {Lakkaraju, Himabindu and Kleinberg, Jon and Leskovec, Jure and Ludwig, Jens and Mullainathan, Sendhil},
	Booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	Date-Added = {2019-08-02 06:37:23 +0000},
	Date-Modified = {2019-08-02 06:37:23 +0000},
	Doi = {10.1145/3097983.3098066},
	Isbn = {978-1-4503-4887-4},
	Keywords = {evaluating machine learning algorithms, selective labels, unmeasured confounders, unobservables},
	Location = {Halifax, NS, Canada},
	Numpages = {10},
	Pages = {275--284},
	Publisher = {ACM},
	Series = {KDD '17},
	Title = {The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables},
	Url = {http://doi.acm.org/10.1145/3097983.3098066},
	Year = {2017},
	Bdsk-Url-1 = {http://doi.acm.org/10.1145/3097983.3098066},
	Bdsk-Url-2 = {http://dx.doi.org/10.1145/3097983.3098066}}

@article{jung2018algorithmic,
	Author = {Jung, Jongbin and Shroff, Ravi and Feller, Avi and Goel, Sharad},
	Date-Added = {2019-08-02 06:37:23 +0000},
	Date-Modified = {2019-08-02 06:37:23 +0000},
	Journal = {arXiv preprint arXiv:1805.01868},
	Title = {Algorithmic decision making in the presence of unmeasured confounding},
	Year = {2018}}

@article{dearteaga2018learning,
	Author = {De-Arteaga, Maria and Dubrawski, Artur and Chouldechova, Alexandra},
	Date-Added = {2019-08-02 06:37:23 +0000},
	Date-Modified = {2019-08-02 06:37:23 +0000},
	Journal = {arXiv preprint arXiv:1807.00905},
	Title = {Learning under selective labels in the presence of expert consistency},
	Year = {2018}}