Breaking Boxes: Countering Bias in AI and Human Thinking
Briefly

Human cognition inherently favors simplification, leading to categorization that can oversimplify reality and perpetuate stereotypes. This pattern of binary thinking can negatively impact relationships and societal dynamics. Labels create rigid frameworks that neglect important nuances, constraining our perceptions and interactions. Coupled with Bayesian reasoning, this cognitive shortcut often overlooks the present's complexities. To combat these biases, the article introduces the 4-step A-Frame as an effective method for fostering deeper understanding online and offline, ultimately promoting a richer appreciation for human diversity.
Human brains naturally gravitate toward categorization, which oversimplifies reality, fuels stereotypes, and creates binary thinking detrimental to relationships and societal dynamics.
The brain's reliance on labels aligns with Bayesian logic, interpreting new experiences through past probabilities but frequently missing critical nuances and deeper understanding.
Stereotypes are pervasive, and labels shape perceptions, often trapping us in simplistic views that deny the diversity and richness of human experience.
The 4-step A-Frame is a practical framework designed to combat bias, both online and offline, facilitating more nuanced understanding and interactions.
Read at Psychology Today
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