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

Gregory Canal, Vladimir Leung, Philip Sage, Eric Heim, I-Jeng Wang

TL;DR

This work addresses the challenge of making AI predictions trustworthy in high-stakes decision contexts by proposing a decision-driven methodology for uncertainty-aware AI self-assessment (SeA). It develops a structured framework that links AI predictions, self-assessment outputs, and downstream costs through a decision policy, and provides a practical workflow to select and tune SeA techniques for specific applications. The authors categorize SeA methods into decision-agnostic and decision-aware families, supply notional examples in disaster relief and autonomous UAV operations, and discuss challenges such as unknown decision costs and limited calibration data. The framework offers concrete guidance for ML engineers and AI system users to align uncertainty outputs with downstream decision needs, improving reliability, interpretability, and safety in national-interest use cases.

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.

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

TL;DR

This work addresses the challenge of making AI predictions trustworthy in high-stakes decision contexts by proposing a decision-driven methodology for uncertainty-aware AI self-assessment (SeA). It develops a structured framework that links AI predictions, self-assessment outputs, and downstream costs through a decision policy, and provides a practical workflow to select and tune SeA techniques for specific applications. The authors categorize SeA methods into decision-agnostic and decision-aware families, supply notional examples in disaster relief and autonomous UAV operations, and discuss challenges such as unknown decision costs and limited calibration data. The framework offers concrete guidance for ML engineers and AI system users to align uncertainty outputs with downstream decision needs, improving reliability, interpretability, and safety in national-interest use cases.

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.
Paper Structure (31 sections, 4 figures, 4 tables)

This paper contains 31 sections, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Mathematical framework
  3. AI predictive model
  4. AI self-assessment
  5. Downstream decision-making
  6. Self-assessment attributes and design guidelines
  7. AI task
  8. Uncertainty representation
  9. Generic metrics
  10. Estimation mechanisms
  11. Design parameters
  12. Downstream decisions
  13. Guided self-assessment design
  14. Overview of self-assessment techniques
  15. Decision-agnostic self-assessment
  16. ...and 16 more sections

Figures (4)

  • Figure 1: Mathematical framework for decision-driven AI self-assessment.
  • Figure 2: Methodology for a practitioner to downselect and optimize candidate self-assessment techniques for their application at hand. For candidate self-assessment techniques, refer to \ref{['sec:overview']} along with \ref{['table:decision_agnostic', 'table:decision_aware']}. For illustrative examples utilizing this methodology, refer to \ref{['sec:notional_examples']}.
  • Figure 3: (a) In settings similar to the DARPA Triage Challenge, autonomous agents are leveraged to assess medical triaging at scale. (b) In our notational use case, we envision an object-detection based triaging system to classify casualties based on predicted severity level. Images adapted and modified from https://triagechallenge.darpa.mil/index.
  • Figure 4: Stylization of UAV vehicle detection and following.