Many high-stake decisions follow an expert-in-loop structure in that a human operator receives recommendations from an algorithm but is the ultimate decision maker. Hence, the algorithm’s recommendation may differ from the actual decision implemented in practice. However, most algorithmic recommendations … Continued
Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stakes applications where humans are the ultimate decision-makers. In this work, we propose two novel architectures of self-interpretable image classifiers that first explain, and then predict (as … Continued
Deep generative models have the potential to fundamentally change the way we create high-fidelity digital content but are often hard to control. Prompting a generative model is a promising recent development that in principle enables end-users to creatively leverage zero-shot … Continued
https://psyarxiv.com/gh2ck/ Research using online research participants is here to stay. Understanding who these subjects are, and specifically what kind of biases and selection mechanisms they bring to our research studies will be important for the future of human-subjects research as … Continued
A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of … Continued
As machine learning (ML) models are increasingly being deployed in high-stakes applications, policymakers have suggested tighter data protection regulations (e.g., GDPR, CCPA). One key principle is the “right to be forgotten” which gives users the right to have their data … Continued
Fostering public AI literacy (i.e. a high-level understanding of artificial intelligence (AI) that allows individuals to critically and effectively use AI technologies) is increasingly important as AI is integrated into individuals’ everyday lives and as concerns about AI grow. This … Continued
https://arxiv.org/pdf/2103.11972.pdf There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that aims to reduce the opaqueness of AI-based decision-making systems, allowing humans to scrunitize and trust them. Prior work in this context has focused on the … Continued
https://psyarxiv.com/6wh9m/ How does the difficulty of a task affect people’s enjoyment and engagement? Intrinsic motivation and flow theories posit a ‘goldilocks’ optimum where task difficulty matches performer skill, yet current work is confounded by questionable measurement practices and lacks scalable … Continued
In this paper, we develop a network of Bayesian agents that collectively model a team’s mental states from the team’s observed communication. We make two contributions. First, we show that our agent could generate interventions that improve the collective intelligence … Continued