Amy McGovern

AIJ Journal 2025 Journal Article

(Re)Conceptualizing trustworthy AI: A foundation for change

• Christopher D. Wirz
• Julie L. Demuth
• Ann Bostrom
• Mariana G. Cains
• Imme Ebert-Uphoff
• David John Gagne
• Andrea Schumacher
• Amy McGovern

Developers and academics have grown increasingly interested in developing “trustworthy” artificial intelligence (AI). However, this aim is difficult to achieve in practice, especially given trust and trustworthiness are complex, multifaceted concepts that cannot be completely guaranteed nor built entirely into an AI system. We have drawn on the breadth of trust-related literature across multiple disciplines and fields to synthesize knowledge pertaining to interpersonal trust, trust in automation, and risk and trust. Based on this review we have (re)conceptualized trustworthiness in practice as being both (a) perceptual, meaning that a user assesses whether, when, and to what extent AI model output is trustworthy, even if it has been developed in adherence to AI trustworthiness standards, and (b) context-dependent, meaning that a user's perceived trustworthiness and use of an AI model can vary based on the specifics of their situation (e.g., time-pressures for decision-making, high-stakes decisions). We provide our reconceptualization to nuance how trustworthiness is thought about, studied, and evaluated by the AI community in ways that are more aligned with past theoretical research.

IJCAI Conference 2007 Conference Paper

• William Dabney
• Amy McGovern

We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-based function approximation derived from McCallum's [1995] UTree algorithm. We have extended this approach to use a relational representation where relational observations are represented by attributed graphs [McGovern et al. , 2003]. We address the challenges introduced by a relational representation by using stochastic sampling to manage the search space [Srinivasan, 1999] and temporal sampling to manage autocorrelation [Jensen and Neville, 2002]. Relational UTree incorporates Iterative Tree Induction [Utgoff et al. , 1997] to allow it to adapt to changing environments. We empirically demonstrate that Relational UTree performs better than similar relational learning methods [Finney et al. , 2002; Driessens et al. , 2001] in a blocks world domain. We also demonstrate that Relational UTree can learn to play a sub-task of the game of Go called Tsume-Go [Ramon et al. , 2001].

ICML Conference 2003 Conference Paper

Identifying Predictive Structures in Relational Data Using Multiple Instance Learning

• Amy McGovern
• David D. Jensen

ICML Conference 2001 Conference Paper

Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density

• Amy McGovern
• Andrew G. Barto

NeurIPS Conference 1998 Conference Paper

Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts

• Amy McGovern
• J. Moss

In 1986, Tanner and Mead [1] implemented an interesting constraint sat(cid: 173) isfaction circuit for global motion sensing in a VLSI. We report here a new and improved a VLSI implementation that provides smooth optical flow as well as global motion in a two dimensional visual field. The com(cid: 173) putation of optical flow is an ill-posed problem, which expresses itself as the aperture problem. However, the optical flow can be estimated by the use of regularization methods, in which additional constraints are intro(cid: 173) duced in terms of a global energy functional that must be minimized. We show how the algorithmic constraints of Hom and Schunck [2] on com(cid: 173) puting smooth optical flow can be mapped onto the physical constraints of an equivalent electronic network.