PADTHAI-MM: A Principled Approach for Designing Trustable, Human-centered AI systems using the MAST Methodology
CoRR(2024)
摘要
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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