메뉴 건너뛰기




Volumn 2017-December, Issue , 2017, Pages 5681-5690

On fairness and calibration

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

EID: 85047008485     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (811)

References (39)
  • 3
    • 85047003482 scopus 로고    scopus 로고
    • 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
  • 5
    • 85019238255 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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
  • 14
    • 85083951030 scopus 로고    scopus 로고
    • 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
  • 17
    • 85018896328 scopus 로고    scopus 로고
    • 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
  • 21
    • 85018868407 scopus 로고    scopus 로고
    • Fairness in learning: Classic and contextual bandits
    • M. Joseph, M. Kearns, J. H. Morgenstern, and A. Roth. Fairness in learning: Classic and contextual bandits. In NIPS, 2016.
    • (2016) NIPS
    • Joseph, M.1    Kearns, M.2    Morgenstern, J.H.3    Roth, A.4
  • 23
    • 84857172480 scopus 로고    scopus 로고
    • 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
  • 30
    • 31844433358 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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
  • 35
    • 0003259364 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.