PADTHAI-MM: A Principled Approach for Designing Trustable, Human-centered AI systems using the MAST Methodology

• URL: http://arxiv.org/abs/2401.13850v1
• Date: Wed, 24 Jan 2024 23:15:44 GMT
• Title: PADTHAI-MM: A Principled Approach for Designing Trustable, Human-centered AI systems using the MAST Methodology
• Authors: Nayoung Kim, Myke C. Cohen, 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), a checklist rating system, addresses this gap in designing and evaluating AI-enabled decision support systems. We propose the Principled Approach for Designing Trustable Human-centered AI systems using MAST methodology. We show that MAST-guided design can improve trust perceptions, and that MAST criteria can be linked to performance, process, and purpose information.
• Score: 5.38932801848643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing for AI trustworthiness is challenging, with a lack of practical guidance despite extensive literature on trust. The Multisource AI Scorecard Table (MAST), a checklist rating system, addresses this gap in designing and evaluating AI-enabled decision support systems. We propose the Principled Approach for Designing Trustable Human-centered AI systems using MAST Methodology (PADTHAI-MM), a nine-step framework what we demonstrate through the iterative design of a text analysis platform called the REporting Assistant for Defense and Intelligence Tasks (READIT). We designed two versions of READIT, high-MAST including AI context and explanations, and low-MAST resembling a "black box" type system. Participant feedback and state-of-the-art AI knowledge was integrated in the design process, leading to a redesigned prototype tested by participants in an intelligence reporting task. Results show that MAST-guided design can improve trust perceptions, and that MAST criteria can be linked to performance, process, and purpose information, providing a practical and theory-informed basis for AI system design.

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