Towards Sustainability of AI

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Establish Standards in Data Collection and Preparation new ​

Overview ​

Sustainability DimensionGovernance
ML Development PhaseData Collection and Preparation
ML Development StakeholdersBusiness Stakeholder, ML Development, Auditing & Testing

Description ​

The DP, “Establish Standards in Data Collection and Preparation”, fosters clear internal guidelines for data access, generation, and collection (Dankwa-Mullan & Weeraratne, 2022). On the one hand, missing standards facilitate the exposure of sensitive data, leading to serious financial and reputational consequences. On the other hand, those standards enable the provision of clean, fair, and socially safe data (Gill et al., 2022). Hence, organizations must establish data collection and preparation standards such as data contracts, meta-data catalogs, definitions of fair data, data lineage, and data ownership on a governmental level (Cowls et al., 2023).

Sources ​

  • Dankwa-Mullan, I., & Weeraratne, D. (2022). Artificial Intelligence and Machine Learning Technologies in Cancer Care: Addressing Disparities, Bias, and Data Diversity. Cancer Discovery, 12(6), 1423–1427. https://doi.org/10.1158/2159-8290.CD-22-0373
  • Gill, N., Mathur, A., & Conde, M. V. (2022). A Brief Overview of AI Governance for Responsible Machine Learning Systems. Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022. https://doi.org/10.48550/arXiv.2211.13130
  • Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2023). The AI gambit: Leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI & SOCIETY, 38(1), 283–307. https://doi.org/10.1007/s00146-021-01294-x