Erin K. Chiou

Myke C. Cohen, Nayoung Kim, Yang Ba et al.

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.

Felix Gröner, Erin K. Chiou

Because large language models (LLMs) can produce natural language that is sometimes indistinguishable from texts produced by people, some researchers are starting to consider replacing human participants with LLM simulations. In this study, we test the extent to which the findings of a simulation with an LLM prompted to act as a synthetic participant match those obtained from 30 human participants. In our experiments, we evaluated how well writing style preference inference algorithms adapted to a participant over repeated interactions, compared to a baseline. We discover hints of bias and a lack of depth in GPT-4o's text generation and judgement that prevent it from accurately simulating people's behavior. Our results also hint at human biases that highlight the importance of considering human factors in the evaluation of systems that depend on human-automation interaction. Rather than treating these discrepancies as evidence for or against the validity of LLM-simulated participants, we present this study as a case analysis of methodological and design challenges.

Yang Ba, Michelle V. Mancenido, Erin K. Chiou et al.

As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data, so as to improve analysis performance and reduce biases in subsequent machine learning tasks. Given the lack of ground truth in most cases of crowdsourcing, we refer to data quality as annotators' consistency and credibility. Unlike the simple scenarios where Kappa coefficient and intraclass correlation coefficient usually can apply, online crowdsourcing requires dealing with more complex situations. We introduce a systematic method for evaluating data quality and detecting spamming threats via variance decomposition, and we classify spammers into three categories based on their different behavioral patterns. A spammer index is proposed to assess entire data consistency, and two metrics are developed to measure crowd workers' credibility by utilizing the Markov chain and generalized random effects models. Furthermore, we showcase the practicality of our techniques and their advantages by applying them on a face verification task with both simulation and real-world data collected from two crowdsourcing platforms.