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<p>Implicit bias perpetuates social injustice, serving as a significant barrier to equality and fairness. Despite extensive efforts, most debiasing interventions have failed to produce lasting, robust, and generalizable changes. In this paper, we introduce a new framework—the Heterarchical Control Network model—that goes beyond the limitations of current embodied approaches, which often lack mechanistic details or remain closely tied to classical architecture. Our model conceptualizes implicit bias as emerging from the dynamic interactions of multiple semi-autonomous mechanisms, many of which are embodied and embedded. By providing a more detailed mechanistic account, it explains both the failure and the relative successes of existing interventions. This framework not only deepens our theoretical understanding of implicit bias but also offers practical implications for designing more effective, durable, and generalizable interventions.</p>

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Towards a Mechanistic Account of Embodied Implicit Bias: Heterarchical Control Network and Its Implications for Intervention

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月20日

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  • Michael Scott Sechman,
  • Linus Ta-Lun Huang

摘要

Implicit bias perpetuates social injustice, serving as a significant barrier to equality and fairness. Despite extensive efforts, most debiasing interventions have failed to produce lasting, robust, and generalizable changes. In this paper, we introduce a new framework—the Heterarchical Control Network model—that goes beyond the limitations of current embodied approaches, which often lack mechanistic details or remain closely tied to classical architecture. Our model conceptualizes implicit bias as emerging from the dynamic interactions of multiple semi-autonomous mechanisms, many of which are embodied and embedded. By providing a more detailed mechanistic account, it explains both the failure and the relative successes of existing interventions. This framework not only deepens our theoretical understanding of implicit bias but also offers practical implications for designing more effective, durable, and generalizable interventions.