-
1
-
-
84954094374
-
-
104 California Law Review . Available at SSRN
-
Barocas S, Selbst AD. Big data's disparate impact. 104 California Law Review 671 (2016). Available at SSRN: https://ssrn. com/ abstract=2477899
-
(2016)
Big Data's Disparate Impact
, pp. 671
-
-
Barocas, S.1
Selbst, A.D.2
-
3
-
-
0010877010
-
Fair credit reporting act: A legislative overview
-
McNamara RM Jr. Fair credit reporting act: A legislative overview. J Pub L. 1973;22:67.
-
(1973)
J Pub L.
, vol.22
, pp. 67
-
-
McNamara, R.M.1
-
4
-
-
85019708205
-
-
Zalta EN (ed. ) The Stanford Encyclopedia of Philosophy accessed June 7
-
Altman A. Discrimination. In: Zalta EN (ed. ) The Stanford Encyclopedia of Philosophy. Available at: https://plato. stanford. edu/archives/ win2016/entries/discrimination (accessed June 7, 2017).
-
(2017)
Discrimination
-
-
Altman, A.1
-
6
-
-
58149417330
-
Estimating causal effects of treatments in randomized and nonrandomized studies
-
Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974;66:688.
-
(1974)
J Educ Psychol.
, vol.66
, pp. 688
-
-
Rubin, D.B.1
-
9
-
-
84900849170
-
A multidisciplinary survey on discrimination analysis
-
Romei A, Ruggieri S. A multidisciplinary survey on discrimination analysis. Knowl Eng Rev. 2014;29:582-638.
-
(2014)
Knowl Eng Rev.
, vol.29
, pp. 582-638
-
-
Romei, A.1
Ruggieri, S.2
-
10
-
-
79951740264
-
Discrimination aware decision tree learning
-
Kamiran F, Calders T, Pechenizkiy M. Discrimination aware decision tree learning. In: 2010 IEEE International Conference on Data Mining, IEEE, 2010, pp. 869-874.
-
(2010)
2010 IEEE International Conference on Data Mining, IEEE
, pp. 869-874
-
-
Kamiran, F.1
Calders, T.2
Pechenizkiy, M.3
-
11
-
-
77958063401
-
Three naive Bayes approaches for discrimination-free classification
-
Calders T, Verwer S. Three naive Bayes approaches for discrimination-free classification. Data Min Knowl Discov. 2010;21:277-292.
-
(2010)
Data Min Knowl Discov.
, vol.21
, pp. 277-292
-
-
Calders, T.1
Verwer, S.2
-
12
-
-
84897542525
-
Learning fair representations
-
Zemel RS, Wu Y, Swersky K, et al. Learning fair representations. ICML. 2013;28:325-333.
-
(2013)
ICML.
, vol.28
, pp. 325-333
-
-
Zemel, R.S.1
Wu, Y.2
Swersky, K.3
-
14
-
-
84877110194
-
Quantifying explainable discrimination and removing illegal discrimination in automated decision making
-
Kamiran F, Zliobaite I, Calders T. Quantifying explainable discrimination and removing illegal discrimination in automated decision making. Knowl Inf Syst. 2013;35:613-644.
-
(2013)
Knowl Inf Syst.
, vol.35
, pp. 613-644
-
-
Kamiran, F.1
Zliobaite, I.2
Calders, T.3
-
15
-
-
80052678955
-
K-NN as an implementation of situation testing for discrimination discovery and prevention
-
Luong BT, Ruggieri S, Turini F. k-NN as an implementation of situation testing for discrimination discovery and prevention. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2011, pp. 502-510.
-
(2011)
Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM
, pp. 502-510
-
-
Luong, B.T.1
Ruggieri, S.2
Turini, F.3
-
16
-
-
84894660134
-
Controlling attribute effect in linear regression
-
Calders T, Karim A, Kamiran F, et al. Controlling attribute effect in linear regression. In: Proceedings of the 13th International Conference on Data Mining, ICDM, 2013, pp. 71-80.
-
(2013)
Proceedings of the 13th International Conference on Data Mining, ICDM
, pp. 71-80
-
-
Calders, T.1
Karim, A.2
Kamiran, F.3
-
18
-
-
85021144225
-
-
last accessed January 31
-
Wikipedia. Cross industry standard process for data mining. Available online at https://en. wikipedia. org/wiki/Cross-Industry-Standard- Process-for-Data-Mining (last accessed January 31, 2017).
-
(2017)
Cross Industry Standard Process for Data Mining
-
-
-
19
-
-
65449163899
-
Discrimination-aware data mining
-
Pedreshi D, Ruggieri S, Turini F. Discrimination-aware data mining. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM,2008, pp. 560-568.
-
(2008)
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM
, pp. 560-568
-
-
Pedreshi, D.1
Ruggieri, S.2
Turini, F.3
-
20
-
-
84866854564
-
Fairness-aware classifier with prejudice remover regularizer
-
Springer Berlin Heidelberg
-
Kamishima T, Akaho S, Asoh H, Sakuma J. Fairness-aware classifier with prejudice remover regularizer. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2012, pp. 35-50.
-
(2012)
Joint European Conference on Machine Learning and Knowledge Discovery in Databases
, pp. 35-50
-
-
Kamishima, T.1
Akaho, S.2
Asoh, H.3
Sakuma, J.4
-
21
-
-
84991834384
-
Evaluating and optimizing online advertising: Forget the click, but there are good proxies
-
Dalessandro B, Hook R, Perlich C, Provost F. Evaluating and optimizing online advertising: Forget the click, but there are good proxies. Big Data 2015;3:90-102.
-
(2015)
Big Data
, vol.3
, pp. 90-102
-
-
Dalessandro, B.1
Hook, R.2
Perlich, C.3
Provost, F.4
-
23
-
-
84867577175
-
The foundations of cost-sensitive learning
-
Lawrence Erlbaum Associates Ltd.
-
Elkan C. The foundations of cost-sensitive learning. In: International Joint Conference on Artificial Intelligence (Vol. 17, No. 1). Lawrence Erlbaum Associates Ltd. , 2001, pp. 973-978.
-
(2001)
International Joint Conference on Artificial Intelligence
, vol.17
, Issue.1
, pp. 973-978
-
-
Elkan, C.1
-
24
-
-
0035283313
-
Robust classification for imprecise environments
-
Provost F, Fawcett T. Robust classification for imprecise environments. Mach Learn. 2001;42:203-231.
-
(2001)
Mach Learn.
, vol.42
, pp. 203-231
-
-
Provost, F.1
Fawcett, T.2
-
25
-
-
68949137209
-
-
Madison, WI: University of Wisconsin. 52. 55-52. 66
-
Settles B. Active learning literature survey. Madison, WI: University of Wisconsin. 2010;52. 55-52. 66:11.
-
(2010)
Active Learning Literature Survey
, pp. 11
-
-
Settles, B.1
-
26
-
-
84867097035
-
Data preprocessing techniques for classification without discrimination
-
Kamiran F, Calders T. Data preprocessing techniques for classification without discrimination. Knowl Inf Syst. 2012;33:1-33.
-
(2012)
Knowl Inf Syst.
, vol.33
, pp. 1-33
-
-
Kamiran, F.1
Calders, T.2
-
28
-
-
33745886270
-
Classifier technology and the illusion of progress
-
Hand DJ. Classifier technology and the illusion of progress. Stat Sci. 2006;21:1-14.
-
(2006)
Stat Sci.
, vol.21
, pp. 1-14
-
-
Hand, D.J.1
-
29
-
-
85021138156
-
Why you're not getting value from your data science
-
December 7
-
Veeramachaneni K. Why you're not getting value from your data science. Harvard Business Review. December 7, 2016.
-
(2016)
Harvard Business Review
-
-
Veeramachaneni, K.1
-
30
-
-
85021138139
-
The problems with using personality tests for hiring
-
August 27
-
Marin W. The problems with using personality tests for hiring. Harvard Business Review. August 27, 2014.
-
(2014)
Harvard Business Review
-
-
Marin, W.1
|