What do AI/ML practitioners think about AI/ML bias?
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909251719397376 |
|---|---|
| author | Pant, Aastha Hoda, Rashina Turhan, Burak Tantithamthavorn, Chakkrit |
| author_facet | Pant, Aastha Hoda, Rashina Turhan, Burak Tantithamthavorn, Chakkrit |
| contents | AI leaders and companies have much to offer to AI/ML practitioners to support them in addressing and mitigating biases in the AI/ML systems they develop. AI/ML practitioners need to receive the necessary resources and support from experts to develop unbiased AI/ML systems. However, our studies have revealed a discrepancy between practitioners' understanding of 'AI/ML bias' and the definitions of tech companies and researchers. This indicates a misalignment that needs addressing. Efforts should be made to match practitioners' understanding of AI/ML bias with the definitions developed by tech companies and researchers. These efforts could yield a significant return on investment by aiding AI/ML practitioners in developing unbiased AI/ML systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08895 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What do AI/ML practitioners think about AI/ML bias? Pant, Aastha Hoda, Rashina Turhan, Burak Tantithamthavorn, Chakkrit Software Engineering AI leaders and companies have much to offer to AI/ML practitioners to support them in addressing and mitigating biases in the AI/ML systems they develop. AI/ML practitioners need to receive the necessary resources and support from experts to develop unbiased AI/ML systems. However, our studies have revealed a discrepancy between practitioners' understanding of 'AI/ML bias' and the definitions of tech companies and researchers. This indicates a misalignment that needs addressing. Efforts should be made to match practitioners' understanding of AI/ML bias with the definitions developed by tech companies and researchers. These efforts could yield a significant return on investment by aiding AI/ML practitioners in developing unbiased AI/ML systems. |
| title | What do AI/ML practitioners think about AI/ML bias? |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2407.08895 |