How AI developers can assure algorithmic fairness
Abstract Artificial intelligence (AI) has rapidly become one of the technologies used for competitive advantage. However, there are also growing concerns about bias in AI models as AI developers risk introducing bias both unintentionally and intentionally. This study, using a qualitative approach, i...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Springer
2023-07-01
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| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-023-00074-4 |
| _version_ | 1797774117166907392 |
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| author | Khensani Xivuri Hosanna Twinomurinzi |
| author_facet | Khensani Xivuri Hosanna Twinomurinzi |
| author_sort | Khensani Xivuri |
| collection | DOAJ |
| description | Abstract Artificial intelligence (AI) has rapidly become one of the technologies used for competitive advantage. However, there are also growing concerns about bias in AI models as AI developers risk introducing bias both unintentionally and intentionally. This study, using a qualitative approach, investigated how AI developers can contribute to the development of fair AI models. The key findings reveal that the risk of bias is mainly because of the lack of gender and social diversity in AI development teams, and haste from AI managers to deliver much-anticipated results. The integrity of AI developers is also critical as they may conceal bias from management and other AI stakeholders. The testing phase before model deployment risks bias because it is rarely representative of the diverse societal groups that may be affected. The study makes practical recommendations in four main areas: governance, social, technical, and training and development processes. Responsible organisations need to take deliberate actions to ensure that their AI developers adhere to fair processes when developing AI; AI developers must prioritise ethical considerations and consider the impact their models may have on society; partnerships between AI developers, AI stakeholders, and society that might be impacted by AI models should be established; and AI developers need to prioritise transparency and explainability in their models while ensuring adequate testing for bias and corrective measures before deployment. Emotional intelligence training should also be provided to the AI developers to help them engage in productive conversations with individuals outside the development team. |
| first_indexed | 2024-03-12T22:15:23Z |
| format | Article |
| id | doaj.art-e1b70366459847cd87e2a36ae64c6570 |
| institution | Directory Open Access Journal |
| issn | 2731-0809 |
| language | English |
| last_indexed | 2024-03-12T22:15:23Z |
| publishDate | 2023-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj.art-e1b70366459847cd87e2a36ae64c65702023-07-23T11:20:27ZengSpringerDiscover Artificial Intelligence2731-08092023-07-013112110.1007/s44163-023-00074-4How AI developers can assure algorithmic fairnessKhensani Xivuri0Hosanna Twinomurinzi1Centre for Applied Data Science, University of JohannesburgCentre for Applied Data Science, University of JohannesburgAbstract Artificial intelligence (AI) has rapidly become one of the technologies used for competitive advantage. However, there are also growing concerns about bias in AI models as AI developers risk introducing bias both unintentionally and intentionally. This study, using a qualitative approach, investigated how AI developers can contribute to the development of fair AI models. The key findings reveal that the risk of bias is mainly because of the lack of gender and social diversity in AI development teams, and haste from AI managers to deliver much-anticipated results. The integrity of AI developers is also critical as they may conceal bias from management and other AI stakeholders. The testing phase before model deployment risks bias because it is rarely representative of the diverse societal groups that may be affected. The study makes practical recommendations in four main areas: governance, social, technical, and training and development processes. Responsible organisations need to take deliberate actions to ensure that their AI developers adhere to fair processes when developing AI; AI developers must prioritise ethical considerations and consider the impact their models may have on society; partnerships between AI developers, AI stakeholders, and society that might be impacted by AI models should be established; and AI developers need to prioritise transparency and explainability in their models while ensuring adequate testing for bias and corrective measures before deployment. Emotional intelligence training should also be provided to the AI developers to help them engage in productive conversations with individuals outside the development team.https://doi.org/10.1007/s44163-023-00074-4Artificial intelligence (AI)FairnessAlgorithmsJurgen HabermasDomination-free development environmentProcess model |
| spellingShingle | Khensani Xivuri Hosanna Twinomurinzi How AI developers can assure algorithmic fairness Discover Artificial Intelligence Artificial intelligence (AI) Fairness Algorithms Jurgen Habermas Domination-free development environment Process model |
| title | How AI developers can assure algorithmic fairness |
| title_full | How AI developers can assure algorithmic fairness |
| title_fullStr | How AI developers can assure algorithmic fairness |
| title_full_unstemmed | How AI developers can assure algorithmic fairness |
| title_short | How AI developers can assure algorithmic fairness |
| title_sort | how ai developers can assure algorithmic fairness |
| topic | Artificial intelligence (AI) Fairness Algorithms Jurgen Habermas Domination-free development environment Process model |
| url | https://doi.org/10.1007/s44163-023-00074-4 |
| work_keys_str_mv | AT khensanixivuri howaideveloperscanassurealgorithmicfairness AT hosannatwinomurinzi howaideveloperscanassurealgorithmicfairness |