Using ACT-R to model racial biases in a semantic knowledge graph

ConceptNet is a semantic knowledge graph made with the intention of drawing conclusions between words and expressions. This semantic network intakes information from various databases, largely originated from text gathered from online websites, defines the relationship between words based on the context in which it was found being used and assigns a relational strength between each of the words. But due to the sources of these datasets and degree of human influence over the spaces that this data is collected from, biases have been detected in the relationship aspects of this network. Our work focuses specifically on the racial biases that have multiplied in this environment. By using this network as a declarative memory knowledge source in a cognitive architecture, we can dissect some of these relational values and gain further insight into how the conceptual space of Blackness is treated among these representations and what this means for cognitive processes and behavior. While we are aware of (canonical) ACT-R's capability of representing a semantic knowledge graph, our goal with this model is to create an extended declarative memory that would hold the knowledge that ConceptNet contains—which consists of well over 1 million nodes. We plan to use this extended ACT-R system to understand the socio-cognitive processes used by participants in a human-AI cooperation study by Atkins et al. (2021). The study reported by Atkins and colleagues explicitly explored how (likely implicit) racialization of AI agents might affect human cooperation with those agents during a task. Thus, a cognitive model for that task would need some representation of sociocultural knowledge, particularly knowledge to represent the conceptual space of systems of oppression that result in racial categorization and racialization.