skip to main content
Idioma:
Primo Advanced Search
Primo Advanced Search Query Term
Primo Advanced Search Query Term
Primo Advanced Search Query Term
Primo Advanced Search prefilters
Busca Simples
Resultados 1 2 3 4 5 next page
Mostrar Somente
Refinado por: Base de dados/Biblioteca: ACM Digital Library remover tipo de recurso: Anais de Congresso remover
Result Number Material Type Add to My Shelf Action Record Details and Options
1
Explaining machine learning classifiers through diverse counterfactual explanations
Explaining machine learning classifiers through diverse counterfactual explanations
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

Explaining machine learning classifiers through diverse counterfactual explanations

Mothilal, Ramaravind K. ; Sharma, Amit ; Tan, Chenhao

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020, p.607-617

New York, NY, USA: ACM

Texto completo disponível

Citações Citado por
2
Mitigating Unwanted Biases with Adversarial Learning
Mitigating Unwanted Biases with Adversarial Learning
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

Mitigating Unwanted Biases with Adversarial Learning

Zhang, Brian Hu ; Lemoine, Blake ; Mitchell, Margaret

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018, p.335-340

New York, NY, USA: ACM

Texto completo disponível

Citações Citado por
3
The hidden assumptions behind counterfactual explanations and principal reasons
The hidden assumptions behind counterfactual explanations and principal reasons
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

The hidden assumptions behind counterfactual explanations and principal reasons

Barocas, Solon ; Selbst, Andrew D. ; Raghavan, Manish

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020, p.80-89

New York, NY, USA: ACM

Texto completo disponível

Citações Citado por
4
Introductory programming: a systematic literature review
Introductory programming: a systematic literature review
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

Introductory programming: a systematic literature review

Luxton-Reilly, Andrew ; Simon ; Albluwi, Ibrahim ; Becker, Brett A. ; Giannakos, Michail ; Kumar, Amruth N. ; Ott, Linda ; Paterson, James ; Scott, Michael James ; Sheard, Judy ; Szabo, Claudia

Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, 2018, p.55-106

New York, NY, USA: ACM

Texto completo disponível

Citações Citado por
5
FACE: Feasible and Actionable Counterfactual Explanations
FACE: Feasible and Actionable Counterfactual Explanations
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

FACE: Feasible and Actionable Counterfactual Explanations

Poyiadzi, Rafael ; Sokol, Kacper ; Santos-Rodriguez, Raul ; De Bie, Tijl ; Flach, Peter

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2020, p.344-350

New York, NY, USA: ACM

Texto completo disponível

Citações Citado por
6
Explainable machine learning in deployment
Explainable machine learning in deployment
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

Explainable machine learning in deployment

Bhatt, Umang ; Xiang, Alice ; Sharma, Shubham ; Weller, Adrian ; Taly, Ankur ; Jia, Yunhan ; Ghosh, Joydeep ; Puri, Ruchir ; Moura, José M. F. ; Eckersley, Peter

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020, p.648-657

New York, NY, USA: ACM

Texto completo disponível

Citações Citado por
7
Measuring and Mitigating Unintended Bias in Text Classification
Measuring and Mitigating Unintended Bias in Text Classification
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

Measuring and Mitigating Unintended Bias in Text Classification

Dixon, Lucas ; Li, John ; Sorensen, Jeffrey ; Thain, Nithum ; Vasserman, Lucy

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018, p.67-73

New York, NY, USA: ACM

Texto completo disponível

Citações Citado por
8
Mitigating bias in algorithmic hiring: evaluating claims and practices
Mitigating bias in algorithmic hiring: evaluating claims and practices
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

Mitigating bias in algorithmic hiring: evaluating claims and practices

Raghavan, Manish ; Barocas, Solon ; Kleinberg, Jon ; Levy, Karen

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020, p.469-481

New York, NY, USA: ACM

Texto completo disponível

Citações Citado por
9
Predicting academic performance: a systematic literature review
Predicting academic performance: a systematic literature review
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

Predicting academic performance: a systematic literature review

Hellas, Arto ; Ihantola, Petri ; Petersen, Andrew ; Ajanovski, Vangel V. ; Gutica, Mirela ; Hynninen, Timo ; Knutas, Antti ; Leinonen, Juho ; Messom, Chris ; Liao, Soohyun Nam

Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, 2018, p.175-199

New York, NY, USA: ACM

Texto completo disponível

Citações Citado por
10
Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy
Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy

Yang, Kaiyu ; Qinami, Klint ; Fei-Fei, Li ; Deng, Jia ; Russakovsky, Olga

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020, p.547-558

New York, NY, USA: ACM

Texto completo disponível

Citações Citado por
Resultados 1 2 3 4 5 next page

Personalize Seus Resultados

  1. Editar

Refine Search Results

Expandir Meus Resultados

  1.   

Mostrar Somente

  1. Revistas revisadas por pares (1)

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.