A Decision-driven Methodology for Designing Uncertainty-aware AI Self-Assessment
<|reference_start|>A Decision-driven Methodology for Designing Uncertainty-aware AI Self-Assessment: Artificial intelligence (AI) has revolutionized decision-making processes and systems throughout society and, in particular, has emerged as a significant technology in high-impact scenarios of national interest. Yet, despite AI's impressive predictive capabilities in controlled settings, it still suffers from a range of practical setbacks preventing its widespread use in various critical scenarios. In particular, it is generally unclear if a given AI system's predictions can be trusted by decision-makers in downstream applications. To address the need for more transparent, robust, and trustworthy AI systems, a suite of tools has been developed to quantify the uncertainty of AI predictions and, more generally, enable AI to "self-assess" the reliability of its predictions. In this manuscript, we categorize methods for AI self-assessment along several key dimensions and provide guidelines for selecting and designing the appropriate method for a practitioner's needs. In particular, we focus on uncertainty estimation techniques that consider the impact of self-assessment on the choices made by downstream decision-makers and on the resulting costs and benefits of decision outcomes. To demonstrate the utility of our methodology for self-assessment design, we illustrate its use for two realistic national-interest scenarios. This manuscript is a practical guide for machine learning engineers and AI system users to select the ideal self-assessment techniques for each problem.<|reference_end|>
@article{canal2024a,
    title={A Decision-driven Methodology for Designing Uncertainty-aware AI
  Self-Assessment},
    author={Gregory Canal, Vladimir Leung, Philip Sage, Eric Heim, I-Jeng Wang},
    journal={arXiv preprint arXiv:2408.01301},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01301},
    primaryClass={stat.ML cs.AI cs.LG}
}