Towards Sustainability of AI

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Assess Social Implications new ​

Overview ​

Sustainability DimensionSocial
ML Development PhaseML Demand Specification
ML Development StakeholdersBusiness Stakeholder, Domain Expert

Description ​

ML models face a multitude of social concerns (i.e., fairness) while simultaneously providing the potential to deliver positive social impact (Ayling & Chapman, 2022; Tomašev et al., 2020). Therefore, “Assess Social Implications” describes the examination of social objectives and potential social risks concerning the ML model and its system boundaries. On the one hand, this addresses contingent consequences that may lead to a socially unfair outcome (Blackman, 2020; Van Giffen et al., 2022). Moreover, an open exchange about the competing objectives of all stakeholder groups helps to find an overall compromise between fairness, accuracy, transparency, accountability, explainability, privacy, and security (Singh et al., 2022). On the other hand, the potential for improving the overall social good should also be integrated into the decision process (Tomašev et al., 2020).

Sources ​

  • Ayling, J., & Chapman, A. (2022). Putting AI ethics to work: Are the tools fit for purpose? AI and Ethics, 2(3), 405–429. https://doi.org/10.1007/s43681-021-00084-x
  • Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. V. D., Mugisha, F., Abila, G., Arai, H., Almiraat, H., Proskurnia, J., Snyder, K., Otake-Matsuura, M., Othman, M., Glasmachers, T., Wever, W. D., … Clopath, C. (2020). AI for social good: Unlocking the opportunity for positive impact. Nature Communications, 11(1), 2468. https://doi.org/10.1038/s41467-020-15871-z
  • Blackman, R. (2020, October 15). A Practical Guide to Building Ethical AI. Harvard Business Review. https://hbr.org/2020/10/a-practical-guide-to-building-ethical-ai
  • Van Giffen, B., Herhausen, D., & Fahse, T. (2022). Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods. Journal of Business Research, 144, 93–106. https://doi.org/10.1016/j.jbusres.2022.01.076
  • Singh, V., Singh, A., & Joshi, K. (2022). Fair CRISP-DM: Embedding Fairness in Machine Learning (ML) Development Life Cycle. Proceedings of the 55th Hawaii International Conference on System Sciences.