Patanjali's five afflictions (kleshas) provide diagnostic categories for recognizing how AI systems amplify confusion, attachment, aversion, and suffering.
Patanjali identifies five kleshas (afflictions) that distort perception: avidya (ignorance), asmita (ego), raga (attachment), dvesha (aversion), and abhinivesha (fear of dissolution). These aren't moral failures but structural distortions of consciousness that every mind experiences. Applied to AI knowledge systems, klesa mapping becomes a diagnostic tool revealing systemic problems: algorithms driven by avidya (trained on incomplete data), platforms amplifying asmita (rewarding confident experts regardless of accuracy), recommendation engines creating raga (addiction) and dvesha (polarization), and user anxieties about knowledge obsolescence triggering abhinivesha. Wisdom platforms would explicitly identify where their own systems perpetuate kleshas and design interventions. This means transparent uncertainty in AI outputs, cognitive bias education for users, architectural choices that resist algorithmic polarization, and epistemological frameworks acknowledging knowledge impermanence. By mapping kleshas in both human and machine systems, AI knowledge platforms can move toward genuine education rather than affliction multiplication.
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