Mitigating Bias: Model Pruning for Enhanced Model Fairness and Efficiency Article Swipe
Harsh Kasyap
,
Ugur Ilker Atmaca
,
Michela Iezzi
,
Toby Walsh
,
Carsten Maple
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3233/faia240589
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3233/faia240589
Machine learning models have been instrumental in making decisions across domains, like mortgage lending and risk assessment in finance. However, these models have been found susceptible to biases, causing unfair decisions for a specific group of individuals. Such bias is generally based on some protected (or sensitive) attributes, such as age, sex, or race, and is still prevalent due to historical context or algorithmic bias. There have been several efforts to ensure equal opportunities for each individual/group, based on creditworthiness, rather than any social bias. Several pre-, in- and post-processing bias mitigation techniques have been proposed. However, these techniques perform data transformation or design new constraint/cost functions, which are task-specific, to achieve a fair prediction. Such techniques even require further access to the complete training/testing data. This paper proposes a novel post-processing bias mitigation technique that employs a model interpretation strategy to find the responsible model weights causing the bias. Pruning only a few model weights exhibits group fairness in model predictions while maintaining competitive accuracy levels, thus aligning with the goals of fairness and efficiency in decision-making. The proposed scheme requires access to only a few data samples representing the protected attributes, without exposing the complete training data. Through extensive experiments with multiple census datasets/methods, we demonstrate the efficacy of our approach, achieving up to a significant 50% reduction in bias while preserving the overall accuracy.
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- en
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- https://doi.org/10.3233/faia240589
- https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA240589
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- DOI
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https://doi.org/10.3233/faia240589Digital Object Identifier
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Mitigating Bias: Model Pruning for Enhanced Model Fairness and EfficiencyWork title
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book-chapterOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-10-16Full publication date if available
- Authors
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Harsh Kasyap, Ugur Ilker Atmaca, Michela Iezzi, Toby Walsh, Carsten MapleList of authors in order
- Landing page
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https://doi.org/10.3233/faia240589Publisher landing page
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https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA240589Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA240589Direct OA link when available
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Pruning, Computer science, Econometrics, Mathematics, Biology, HorticultureTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.scheme | 180 |
| abstract_inverted_index.social | 83 |
| abstract_inverted_index.unfair | 29 |
| abstract_inverted_index.Machine | 0 |
| abstract_inverted_index.Pruning | 150 |
| abstract_inverted_index.Several | 85 |
| abstract_inverted_index.Through | 199 |
| abstract_inverted_index.achieve | 111 |
| abstract_inverted_index.biases, | 27 |
| abstract_inverted_index.causing | 28, 147 |
| abstract_inverted_index.context | 61 |
| abstract_inverted_index.efforts | 69 |
| abstract_inverted_index.employs | 136 |
| abstract_inverted_index.further | 119 |
| abstract_inverted_index.lending | 13 |
| abstract_inverted_index.levels, | 166 |
| abstract_inverted_index.overall | 225 |
| abstract_inverted_index.perform | 99 |
| abstract_inverted_index.require | 118 |
| abstract_inverted_index.samples | 188 |
| abstract_inverted_index.several | 68 |
| abstract_inverted_index.weights | 146, 155 |
| abstract_inverted_index.without | 193 |
| abstract_inverted_index.However, | 19, 96 |
| abstract_inverted_index.accuracy | 165 |
| abstract_inverted_index.aligning | 168 |
| abstract_inverted_index.complete | 123, 196 |
| abstract_inverted_index.domains, | 10 |
| abstract_inverted_index.efficacy | 209 |
| abstract_inverted_index.exhibits | 156 |
| abstract_inverted_index.exposing | 194 |
| abstract_inverted_index.fairness | 158, 173 |
| abstract_inverted_index.finance. | 18 |
| abstract_inverted_index.learning | 1 |
| abstract_inverted_index.mortgage | 12 |
| abstract_inverted_index.multiple | 203 |
| abstract_inverted_index.proposed | 179 |
| abstract_inverted_index.proposes | 128 |
| abstract_inverted_index.requires | 181 |
| abstract_inverted_index.specific | 33 |
| abstract_inverted_index.strategy | 140 |
| abstract_inverted_index.training | 197 |
| abstract_inverted_index.accuracy. | 226 |
| abstract_inverted_index.achieving | 213 |
| abstract_inverted_index.approach, | 212 |
| abstract_inverted_index.decisions | 8, 30 |
| abstract_inverted_index.extensive | 200 |
| abstract_inverted_index.generally | 40 |
| abstract_inverted_index.prevalent | 57 |
| abstract_inverted_index.proposed. | 95 |
| abstract_inverted_index.protected | 44, 191 |
| abstract_inverted_index.reduction | 219 |
| abstract_inverted_index.technique | 134 |
| abstract_inverted_index.assessment | 16 |
| abstract_inverted_index.efficiency | 175 |
| abstract_inverted_index.functions, | 106 |
| abstract_inverted_index.historical | 60 |
| abstract_inverted_index.mitigation | 91, 133 |
| abstract_inverted_index.preserving | 223 |
| abstract_inverted_index.sensitive) | 46 |
| abstract_inverted_index.techniques | 92, 98, 116 |
| abstract_inverted_index.algorithmic | 63 |
| abstract_inverted_index.attributes, | 47, 192 |
| abstract_inverted_index.competitive | 164 |
| abstract_inverted_index.demonstrate | 207 |
| abstract_inverted_index.experiments | 201 |
| abstract_inverted_index.maintaining | 163 |
| abstract_inverted_index.prediction. | 114 |
| abstract_inverted_index.predictions | 161 |
| abstract_inverted_index.responsible | 144 |
| abstract_inverted_index.significant | 217 |
| abstract_inverted_index.susceptible | 25 |
| abstract_inverted_index.individuals. | 36 |
| abstract_inverted_index.instrumental | 5 |
| abstract_inverted_index.representing | 189 |
| abstract_inverted_index.opportunities | 73 |
| abstract_inverted_index.interpretation | 139 |
| abstract_inverted_index.task-specific, | 109 |
| abstract_inverted_index.transformation | 101 |
| abstract_inverted_index.constraint/cost | 105 |
| abstract_inverted_index.post-processing | 89, 131 |
| abstract_inverted_index.decision-making. | 177 |
| abstract_inverted_index.training/testing | 124 |
| abstract_inverted_index.creditworthiness, | 79 |
| abstract_inverted_index.datasets/methods, | 205 |
| abstract_inverted_index.individual/group, | 76 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.50673129 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |