-
3
-
-
85047003482
-
A primer on fairness in criminal justice risk assessments
-
R. Berk. A primer on fairness in criminal justice risk assessments. Criminology, 41(6): 6-9, 2016.
-
(2016)
Criminology
, vol.41
, Issue.6
, pp. 6-9
-
-
Berk, R.1
-
4
-
-
85029119134
-
-
R. Berk, H. Heidari, S. Jabbari, M. Kearns, and A. Roth. Fairness in criminal justice risk assessments: The state of the art. arXiv preprint arXiv: 1703.09207, 2017.
-
(2017)
Fairness in Criminal Justice Risk Assessments: The State of the Art
-
-
Berk, R.1
Heidari, H.2
Jabbari, S.3
Kearns, M.4
Roth, A.5
-
5
-
-
85019238255
-
Man is to computer programmer as woman is to homemaker? Debiasing word embeddings
-
T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In NIPS, pages 4349-4357, 2016.
-
(2016)
NIPS
, pp. 4349-4357
-
-
Bolukbasi, T.1
Chang, K.-W.2
Zou, J.Y.3
Saligrama, V.4
Kalai, A.T.5
-
6
-
-
85047015301
-
Three naive bayes approaches for discrimination-free classification
-
T. Calders and S. Verwer. Three naive bayes approaches for discrimination-free classification. KDD, 2012.
-
(2012)
KDD
-
-
Calders, T.1
Verwer, S.2
-
9
-
-
85029021286
-
Algorithmic decision making and the cost of fairness
-
S. Corbett-Davies, E. Pierson, A. Feller, S. Goel, and A. Huq. Algorithmic decision making and the cost of fairness. In KDD, pages 797-806, 2017.
-
(2017)
KDD
, pp. 797-806
-
-
Corbett-Davies, S.1
Pierson, E.2
Feller, A.3
Goel, S.4
Huq, A.5
-
10
-
-
84983749157
-
Assessing calibration of prognostic risk scores
-
C. S. Crowson, E. J. Atkinson, and T. M. Therneau. Assessing calibration of prognostic risk scores. Statistical Methods in Medical Research, 25(4): 1692-1706, 2016.
-
(2016)
Statistical Methods in Medical Research
, vol.25
, Issue.4
, pp. 1692-1706
-
-
Crowson, C.S.1
Atkinson, E.J.2
Therneau, T.M.3
-
13
-
-
84856446756
-
Fairness through awareness
-
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel. Fairness through awareness. In Innovations in Theoretical Computer Science, 2012.
-
(2012)
Innovations in Theoretical Computer Science
-
-
Dwork, C.1
Hardt, M.2
Pitassi, T.3
Reingold, O.4
Zemel, R.5
-
14
-
-
85083951030
-
Censoring representations with an adversary
-
H. Edwards and A. Storkey. Censoring representations with an adversary. In ICLR, 2016.
-
(2016)
ICLR
-
-
Edwards, H.1
Storkey, A.2
-
15
-
-
84954161181
-
Certifying and removing disparate impact
-
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian. Certifying and removing disparate impact. In KDD, pages 259-268, 2015.
-
(2015)
KDD
, pp. 259-268
-
-
Feldman, M.1
Friedler, S.A.2
Moeller, J.3
Scheidegger, C.4
Venkatasubramanian, S.5
-
16
-
-
85038576805
-
-
Technical report September
-
A. Flores, C. Lowenkamp, and K. Bechtel. False positives, false negatives, and false analyses: A rejoinder to "machine bias: There's software used across the country to predict future criminals. and it's biased against blacks.". Technical report, Crime & Justice Institute, September 2016. http://www.crj.org/cji/entry/false-positives-false-negatives-and-false-analyses-a-rejoinder.
-
(2016)
False Positives, False Negatives, and False Analyses: A Rejoinder to "machine Bias: There's Software Used Across the Country to Predict Future Criminals. and It's Biased Against Blacks."
-
-
Flores, A.1
Lowenkamp, C.2
Bechtel, K.3
-
17
-
-
85018896328
-
Satisfying real-world goals with dataset constraints
-
G. Goh, A. Cotter, M. Gupta, and M. P. Friedlander. Satisfying real-world goals with dataset constraints. In NIPS, pages 2415-2423. 2016.
-
(2016)
NIPS
, pp. 2415-2423
-
-
Goh, G.1
Cotter, A.2
Gupta, M.3
Friedlander, M.P.4
-
23
-
-
84857172480
-
Fairness-aware learning through regularization approach
-
T. Kamishima, S. Akaho, and J. Sakuma. Fairness-aware learning through regularization approach. In ICDM Workshops, 2011.
-
(2011)
ICDM Workshops
-
-
Kamishima, T.1
Akaho, S.2
Sakuma, J.3
-
25
-
-
85047020437
-
Avoiding discrimination through causal reasoning
-
N. Kilbertus, M. Rojas-Carulla, G. Parascandolo, M. Hardt, D. Janzing, and B. Schölkopf. Avoiding discrimination through causal reasoning. In NIPS, 2017.
-
(2017)
NIPS
-
-
Kilbertus, N.1
Rojas-Carulla, M.2
Parascandolo, G.3
Hardt, M.4
Janzing, D.5
Schölkopf, B.6
-
29
-
-
85083951172
-
The variational fair auto encoder
-
C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel. The variational fair auto encoder. In ICLR, 2016.
-
(2016)
ICLR
-
-
Louizos, C.1
Swersky, K.2
Li, Y.3
Welling, M.4
Zemel, R.5
-
30
-
-
31844433358
-
Predicting good probabilities with supervised learning
-
A. Niculescu-Mizil and R. Caruana. Predicting good probabilities with supervised learning. In ICML, 2005.
-
(2005)
ICML
-
-
Niculescu-Mizil, A.1
Caruana, R.2
-
31
-
-
0003243224
-
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
-
J. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers, 10(3): 61-74, 1999.
-
(1999)
Advances in Large Margin Classifiers
, vol.10
, Issue.3
, pp. 61-74
-
-
Platt, J.1
-
32
-
-
84900849170
-
A multidisciplinary survey on discrimination analysis
-
A. Romei and S. Ruggieri. A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review, 29(05): 582-638, 2014.
-
(2014)
The Knowledge Engineering Review
, vol.29
, Issue.5
, pp. 582-638
-
-
Romei, A.1
Ruggieri, S.2
-
34
-
-
85047020484
-
Learning non-discriminatory predictors
-
Amsterdam, Netherlands, 07-10 Jul PMLR
-
B. Woodworth, S. Gunasekar, M. I. Ohannessian, and N. Srebro. Learning non-discriminatory predictors. In Proceedings of the 2017 Conference on Learning Theory, Volume 65, pages 1920-1953, Amsterdam, Netherlands, 07-10 Jul 2017. PMLR.
-
(2017)
Proceedings of the 2017 Conference on Learning Theory
, vol.65
, pp. 1920-1953
-
-
Woodworth, B.1
Gunasekar, S.2
Ohannessian, M.I.3
Srebro, N.4
-
35
-
-
0003259364
-
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
-
B. Zadrozny and C. Elkan. Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. In ICML, pages 609-616, 2001.
-
(2001)
ICML
, pp. 609-616
-
-
Zadrozny, B.1
Elkan, C.2
-
37
-
-
85048347682
-
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
-
M. B. Zafar, I. Valera, M. G. Rodriguez, and K. P. Gummadi. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In World Wide Web Conference, 2017.
-
(2017)
World Wide Web Conference
-
-
Zafar, M.B.1
Valera, I.2
Rodriguez, M.G.3
Gummadi, K.P.4
-
38
-
-
84897542525
-
Learning fair representations
-
R. S. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork. Learning fair representations. In ICML, 2013.
-
(2013)
ICML
-
-
Zemel, R.S.1
Wu, Y.2
Swersky, K.3
Pitassi, T.4
Dwork, C.5
|