Towards Sustainability of AI

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Conceptualize Definition of Fairness new ​

Overview ​

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

Description ​

As highlighted in the introduction to this subsection, a holistic definition of fairness covering each element is impossible (Kleinberg et al., 2017). Thus, the DP “Conceptualize Definition of Fairness” focuses on the selection or development of a conceptualized fairness definition for the problem at hand. Whenever possible, existing fairness definitions and metrics should be favored (Friedler et al., 2019). To achieve this, it may be helpful to engage in discussions with domain experts and business stakeholders regarding the application context to determine the essential features to incorporate in the following model training phase (Bellamy et al., 2019; Van Giffen et al., 2022).

Sources ​

  • Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent Trade-Offs in the Fair Determination of Risk Scores. 8th Innovations in Theoretical Computer Science Conference (ITCS 2017). https://doi.org/10.4230/LIPICS.ITCS.2017.43
  • Friedler, S. A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E. P., & Roth, D. (2019). A comparative study of fairness-enhancing interventions in machine learning. Proceedings of the Conference on Fairness, Accountability, and Transparency, 329–338. https://doi.org/10.1145/3287560.3287589
  • Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Mehta, S., Mojsilovic, A., Nagar, S., Ramamurthy, K. N., Richards, J., Saha, D., Sattigeri, P., Singh, M., Varshney, K. R., & Zhang, Y. (2019). Think Your Artificial Intelligence Software Is Fair? Think Again. IEEE Software, 36(4), 76–80. https://doi.org/10.1109/MS.2019.2908514
  • 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