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Using a cognitive architecture to consider antiBlackness in design and development of AI systems

Created by
  • Haebom

Author

Christopher L. Dancy

Outline

This paper examines the impact of racism, specifically anti-Blackness, on the design and development of AI systems through cognitive modeling. Leveraging the ACT-R/Φ cognitive architecture and the ConceptNet knowledge graph system, we analyze the problem from cognitive, sociocultural, and physiological perspectives. Beyond using cognitive modeling as a means to explore anti-Blackness in AI system design and development from a software engineering perspective, we present connections among anti-Blackness, human beings, and computational cognitive modeling. We argue that the exclusion of sociocultural processes and knowledge structures from existing cognitive architectures and models implicitly promotes a color-blind approach, obscuring the sociocultural contexts that pervasively influence human behavior and cognitive processes.

Takeaways, Limitations

Takeaways:
A novel approach to analyzing the potential impact of racism in AI systems through cognitive modeling is presented.
Emphasize the importance of cognitive modeling that takes sociocultural context into account.
Advancing a deeper understanding of the impact of anti-blackness on the design and development of AI systems.
Demonstrates the connection between cognitive modeling and social justice issues.
Limitations:
Further research is needed to determine the generalizability of the presented examples.
Lack of discussion of the possible impact of ACT-R/Φ and ConceptNet's Limitations on the results.
Lack of specific solutions to racial discrimination revealed through cognitive modeling.
The possibility that the complexity of the sociocultural context may not be fully captured.
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