Towards Sustainability of AI

Appearance

Convey ML-Model Understanding to End Users new ​

Overview ​

Sustainability DimensionSocial
ML Development PhaseDeployment and Monitoring
ML Development StakeholdersBusiness Stakeholder, ML Development

Description ​

“Convey ML-Model Understanding to End Users” accounts for the perception of complexity and risk in the ML model prediction due to their non-deterministic functionality (i.e., varying text outputs of large language models based on similar input prompts due to different random number generators) (Baier et al., 2019; Westenberger et al., 2022). Especially since ML models are often user-facing and hence have wide-ranging social implications, the results of the ML models need to be transparent or at least understandable for the end user (Singh et al., 2022; Van Giffen et al., 2022).

Sources ​

  • Baier, L., Jöhren, F., & Seebacher, S. (2019). Challenges in the Deployment and Operation of Machine Learning in Practice. Proceedings of the 27th European Conference on Information Systems (ECIS). https://doi.org/10.5445/IR/1000095028
  • Westenberger, J., Schuler, K., & Schlegel, D. (2022). Failure of AI projects: Understanding the critical factors. Procedia Computer Science, 196, 69–76. https://doi.org/10.1016/j.procs.2021.11.074
  • 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.