PADTHAI-MM: Principles-based Approach for Designing Trustworthy, Human-centered AI using MAST Methodology

• URL: http://arxiv.org/abs/2401.13850v2
• Date: Wed, 22 Jan 2025 20:52:56 GMT
• Title: PADTHAI-MM: Principles-based Approach for Designing Trustworthy, Human-centered AI using MAST Methodology
• Authors: Myke C. Cohen, Nayoung Kim, Yang Ba, Anna Pan, Shawaiz Bhatti, Pouria Salehi, James Sung, Erik Blasch, Michelle V. Mancenido, Erin K. Chiou,
• Abstract summary: The Multisource AI Scorecard Table (MAST) was designed to bridge the gap by offering a systematic, tradecraft-centered approach to evaluating AI-enabled decision support systems. We introduce an iterative design framework called textitPrinciples-based Approach for Designing Trustworthy, Human-centered AI. We demonstrate this framework in our development of the Reporting Assistant for Defense and Intelligence Tasks (READIT)
• Score: 5.215782336985273
• License:
• Abstract: Despite an extensive body of literature on trust in technology, designing trustworthy AI systems for high-stakes decision domains remains a significant challenge, further compounded by the lack of actionable design and evaluation tools. The Multisource AI Scorecard Table (MAST) was designed to bridge this gap by offering a systematic, tradecraft-centered approach to evaluating AI-enabled decision support systems. Expanding on MAST, we introduce an iterative design framework called \textit{Principles-based Approach for Designing Trustworthy, Human-centered AI using MAST Methodology} (PADTHAI-MM). We demonstrate this framework in our development of the Reporting Assistant for Defense and Intelligence Tasks (READIT), a research platform that leverages data visualizations and natural language processing-based text analysis, emulating an AI-enabled system supporting intelligence reporting work. To empirically assess the efficacy of MAST on trust in AI, we developed two distinct iterations of READIT for comparison: a High-MAST version, which incorporates AI contextual information and explanations, and a Low-MAST version, akin to a ``black box'' system. This iterative design process, guided by stakeholder feedback and contemporary AI architectures, culminated in a prototype that was evaluated through its use in an intelligence reporting task. We further discuss the potential benefits of employing the MAST-inspired design framework to address context-specific needs. We also explore the relationship between stakeholder evaluators' MAST ratings and three categories of information known to impact trust: \textit{process}, \textit{purpose}, and \textit{performance}. Overall, our study supports the practical benefits and theoretical validity for PADTHAI-MM as a viable method for designing trustable, context-specific AI systems.

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