Generative AI image models may inadvertently generate problematic representations of people. Past research has noted that millions of users engage daily across the world with these models and that the models, including through problematic representations of people, have the potential to compound and accelerate real-world discrimination and other harms (Bianchi et al, 2023). In this paper, we focus on addressing the generation of problematic associations between demographic groups and semantic concepts that may reflect and reinforce negative narratives embedded in social data. Building on sociological literature (Blumer, 1958) and mapping representations to model behaviors, we have developed a taxonomy to study problematic associations in image generation models. We explore the effectiveness of fine tuning at the model level as a method to address these associations, identifying a potential reduction in visual quality as a limitation of traditional fine tuning. We also propose a new methodology with twice-human-in-the-loop (T-HITL) that promises improvements in both reducing problematic associations and also maintaining visual quality. We demonstrate the effectiveness of T-HITL by providing evidence of three problematic associations addressed by T-HITL at the model level. Our contributions to scholarship are two-fold. By defining problematic associations in the context of machine learning models and generative AI, we introduce a conceptual and technical taxonomy for addressing some of these associations. Finally, we provide a method, T-HITL, that addresses these associations and simultaneously maintains visual quality of image model generations. This mitigation need not be a tradeoff, but rather an enhancement.

As biological data about genes and their interactions proliferates, scientists have the opportunity to identify sets of proteins whose interactions make them worthy of further investigation. This paper reports on a knowledge discovery technique to support that work. Foretell is an algorithm originally designed to support search for solutions to constraint satisfaction problems. Recent adaptations enable Foretell to detect sets of genes that interact heavily with one another. We provide empirical results, and describe ongoing work on biological meaning and knowledge infusion from the user.

This paper advocates the use of a portfolio of representations for problem solving in complex domains. It describes an approach that decouples efficient storage mechanisms called descriptives from the decision-making procedures that employ them. An architecture that takes this approach can learn which representations are appropriate for a given problem class. Examples of search with a portfolio of representations are drawn from a broad set of domains.

The Association for the Advancement of Artificial Intelligence held its 1993 Fall Symposium Series on October 22-24 in Raleigh, North Carolina. This article contains summaries of the six symposia that were conducted: Automated Deduction in Nonstandard Logics; Games: Planning and Learning; Human-Computer Collaboration: Reconciling Theory, Synthesizing Practice; Instantiating Intelligent Agents; and Machine Learning and Computer Vision: What, Why, and How?

The Association for the Advancement of Artificial Intelligence held its 1993 Fall Symposium Series on October 22-24 in Raleigh, North Carolina. This article contains summaries of the six symposia that were conducted: Automated Deduction in Nonstandard Logics; Games: Planning and Learning; Human-Computer Collaboration: Reconciling Theory, Synthesizing Practice; Instantiating Intelligent Agents; and Machine Learning and Computer Vision: What, Why, and How?