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Explaining machine learning classifiers through diverse counterfactual explanations
Explaining machine learning classifiers through diverse counterfactual explanations
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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

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2
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing

Raji, Inioluwa Deborah ; Smart, Andrew ; White, Rebecca N. ; Mitchell, Margaret ; Gebru, Timnit ; Hutchinson, Ben ; Smith-Loud, Jamila ; Theron, Daniel ; Barnes, Parker

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020, p.33-44

New York, NY, USA: ACM

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3
Mitigating bias in algorithmic hiring: evaluating claims and practices
Mitigating bias in algorithmic hiring: evaluating claims and practices
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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

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4
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
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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

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5
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

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6
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

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7
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

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8
Towards a critical race methodology in algorithmic fairness
Towards a critical race methodology in algorithmic fairness
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

Towards a critical race methodology in algorithmic fairness

Hanna, Alex ; Denton, Emily ; Smart, Andrew ; Smith-Loud, Jamila

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020, p.501-512

New York, NY, USA: ACM

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9
On the apparent conflict between individual and group fairness
On the apparent conflict between individual and group fairness
Material Type:
Ata de Congresso 		Adicionar ao Meu Espaço

On the apparent conflict between individual and group fairness

Binns, Reuben

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020, p.514-524

New York, NY, USA: ACM

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10
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability
Material Type:
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What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability

Wieringa, Maranke

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020, p.1-18

New York, NY, USA: ACM

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