A Decision-driven Methodology for Designing Uncertainty-aware AI Self-Assessment

• URL: http://arxiv.org/abs/2408.01301v1
• Date: Fri, 2 Aug 2024 14:43:45 GMT
• Title: A Decision-driven Methodology for Designing Uncertainty-aware AI Self-Assessment
• Authors: Gregory Canal, Vladimir Leung, Philip Sage, Eric Heim, I-Jeng Wang,
• Abstract summary: It is unclear if a given AI system's predictions can be trusted by decision-makers in downstream applications. This manuscript is a practical guide for machine learning engineers and AI system users to select the ideal self-assessment techniques.
• Score: 8.482630532500057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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