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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2502.11459 |
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| _version_ | 1866912233717497856 |
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| author | Vargas-Solar, Genoveva |
| author_facet | Vargas-Solar, Genoveva |
| contents | This project addresses the challenges of responsible and fair resource allocation in data science (DS), focusing on DS queries evaluation. Current DS practices often overlook the broader socio-economic, environmental, and ethical implications, including data sovereignty, fairness, and inclusivity. By integrating a decolonial perspective, the project aims to establish innovative fairness metrics that respect cultural and contextual diversity, optimise computational and energy efficiency, and ensure equitable participation of underrepresented communities. The research includes developing algorithms to align resource allocation with fairness constraints, incorporating ethical and sustainability considerations, and fostering interdisciplinary collaborations to bridge technical advancements and societal impact gaps. This work aims to reshape into an equitable, transparent, and community-empowering practice challenging the technological power developed by the Big Tech. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11459 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Responsible and Fair Data Science: Resource Allocation for Inclusive and Sustainable Analytics Vargas-Solar, Genoveva Databases This project addresses the challenges of responsible and fair resource allocation in data science (DS), focusing on DS queries evaluation. Current DS practices often overlook the broader socio-economic, environmental, and ethical implications, including data sovereignty, fairness, and inclusivity. By integrating a decolonial perspective, the project aims to establish innovative fairness metrics that respect cultural and contextual diversity, optimise computational and energy efficiency, and ensure equitable participation of underrepresented communities. The research includes developing algorithms to align resource allocation with fairness constraints, incorporating ethical and sustainability considerations, and fostering interdisciplinary collaborations to bridge technical advancements and societal impact gaps. This work aims to reshape into an equitable, transparent, and community-empowering practice challenging the technological power developed by the Big Tech. |
| title | Towards Responsible and Fair Data Science: Resource Allocation for Inclusive and Sustainable Analytics |
| topic | Databases |
| url | https://arxiv.org/abs/2502.11459 |