From Literature to Practice: Exploring Fairness Testing Tools for the Software Industry Adoption
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912014266269696 |
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| author | Nguyen, Thanh de Lima, Luiz Fernando Badassarre, Maria Teresa Santos, Ronnie de Souza |
| author_facet | Nguyen, Thanh de Lima, Luiz Fernando Badassarre, Maria Teresa Santos, Ronnie de Souza |
| contents | In today's world, we need to ensure that AI systems are fair and unbiased. Our study looked at tools designed to test the fairness of software to see if they are practical and easy for software developers to use. We found that while some tools are cost-effective and compatible with various programming environments, many are hard to use and lack detailed instructions. They also tend to focus on specific types of data, which limits their usefulness in real-world situations. Overall, current fairness testing tools need significant improvements to better support software developers in creating fair and equitable technology. We suggest that new tools should be user-friendly, well-documented, and flexible enough to handle different kinds of data, helping developers identify and fix biases early in the development process. This will lead to more trustworthy and fair software for everyone. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02433 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Literature to Practice: Exploring Fairness Testing Tools for the Software Industry Adoption Nguyen, Thanh de Lima, Luiz Fernando Badassarre, Maria Teresa Santos, Ronnie de Souza Software Engineering In today's world, we need to ensure that AI systems are fair and unbiased. Our study looked at tools designed to test the fairness of software to see if they are practical and easy for software developers to use. We found that while some tools are cost-effective and compatible with various programming environments, many are hard to use and lack detailed instructions. They also tend to focus on specific types of data, which limits their usefulness in real-world situations. Overall, current fairness testing tools need significant improvements to better support software developers in creating fair and equitable technology. We suggest that new tools should be user-friendly, well-documented, and flexible enough to handle different kinds of data, helping developers identify and fix biases early in the development process. This will lead to more trustworthy and fair software for everyone. |
| title | From Literature to Practice: Exploring Fairness Testing Tools for the Software Industry Adoption |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2409.02433 |