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What might the integration of cognitive architectures and generative models mean for sociocultural representations within both systems? Beyond just integration, we see this question as paramount to understanding the potential wider impact of integrations between these two types of computational systems. Generative models, though an imperfect representation of the world and various con-texts, nonetheless may be useful as general world knowledge with careful considerations of sociocultural representations provided therein, including the represented sociocultural systems or, as we explain, genres of the Human. Thus, such an integration gives an opportunity to develop cognitive models that represent from the physiological/biological time scale to the social timescale and that more accurately represent the effects of ongoing sociocultural systems and structures on behavior. In addition, integrating these systems should prove useful to audit and test many generative models under more realistic cognitive uses and conditions. That is, we can ask what it means that people will likely be using knowledge from such models as knowledge for their own behavior and actions. We further discuss these perspectives and focus these perspectives using ongoing and potential work with (primarily) the ACT-R cognitive architecture. We also discuss issues with using generative models as a system for integration.

 

How has recent AI Ethics literature addressed topics such as fairness and justice in the context of continued social and structural power asymmetries? We trace both the historical roots and current landmark work that have been shaping the field and categorize these works under three broad umbrellas: (i) those grounded in Western canonical philosophy, (ii) mathematical and statistical methods, and (iii) those emerging from critical data/algorithm/information studies. We also survey the field and explore emerging trends by examining the rapidly growing body of literature that falls under the broad umbrella of AI Ethics. To that end, we read and annotated peer-reviewed papers published over the past four years in two premier conferences: FAccT and AIES. We organize the literature based on an annotation scheme we developed according to three main dimensions: whether the paper deals with concrete applications, use-cases, and/or people’s lived experience; to what extent it addresses harmed, threatened, or otherwise marginalized groups; and if so, whether it explicitly names such groups. We note that although the goals of the majority of FAccT and AIES papers were often commendable, their consideration of the negative impacts of AI on traditionally marginalized groups remained shallow. Taken together, our conceptual analysis and the data from annotated papers indicate that the field would benefit from an increased focus on ethical analysis grounded in concrete use-cases, people’s experiences, and applications as well as from approaches that are sensitive to structural and historical power asymmetries.