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Explaining machine learning classifiers through diverse counterfactual explanations
Explaining machine learning classifiers through diverse counterfactual explanations
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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
Mitigating Unwanted Biases with Adversarial Learning
Mitigating Unwanted Biases with Adversarial Learning
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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

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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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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
Spread of Hate Speech in Online Social Media
Spread of Hate Speech in Online Social Media
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Spread of Hate Speech in Online Social Media

Mathew, Binny ; Dutt, Ritam ; Goyal, Pawan ; Mukherjee, Animesh

Proceedings of the 10th ACM Conference on Web Science, 2019, p.173-182

New York, NY, USA: ACM

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5
On the apparent conflict between individual and group fairness
On the apparent conflict between individual and group fairness
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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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6
Towards a critical race methodology in algorithmic fairness
Towards a critical race methodology in algorithmic fairness
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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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7
FACE: Feasible and Actionable Counterfactual Explanations
FACE: Feasible and Actionable Counterfactual Explanations
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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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8
The hidden assumptions behind counterfactual explanations and principal reasons
The hidden assumptions behind counterfactual explanations and principal reasons
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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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9
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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10
Roles for computing in social change
Roles for computing in social change
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Roles for computing in social change

Abebe, Rediet ; Barocas, Solon ; Kleinberg, Jon ; Levy, Karen ; Raghavan, Manish ; Robinson, David G.

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020, p.252-260

New York, NY, USA: ACM

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