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

arXiv 2207.00644 ↗First posted 1 Jul 2022cs.CY cs.AIcited by 0

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Abstract (arXiv)

How might we use cognitive modeling to consider the ways in which antiblackness, and racism more broadly, impact the design and development of AI systems? We provide a discussion and an example towards an answer to this question. We use the ACT-R/Phi cognitive architecture and an existing knowledge graph system, ConceptNet, to consider this question not only from a cognitive and sociocultural perspective, but also from a physiological perspective. In addition to using a cognitive modeling as a means to explore how antiblackness may manifest in the design and development of AI systems (particularly from a software engineering perspective), we also introduce connections between antiblackness, the Human, and computational cognitive modeling. We argue that the typical eschewing of sociocultural processes and knowledge structures in cognitive architectures and cognitive modeling implicitly furthers a colorblind approach to cognitive modeling and hides sociocultural context that is always present in human behavior and affects cognitive processes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 46 references that are in the map. First posted on arXiv 1 Jul 2022.

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4 of 46 references.

References · 4

  1. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,067
  2. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  3. 2020Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructurecited by 138
  4. 2021AI and Blackness: Towards moving beyond bias and representationcited by 4

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