Cognitive vs. Algorithmic Evolution: Bias, Adaptation, and Conflict in Human-AI Systems: An Economic Introspection

Abstract: This paper explores the evolutionary asymmetries between human and AI systems, highlighting the need for policymakers and practitioners to mitigate associated risks. To ensure transparent, explainable, and fair AI systems, policymakers should prioritize robust testing and validation protocols, bias detection and mitigation, and adaptable institutions that accommodate the changing nature of work. Furthermore, AI systems should be designed to promote digital inclusion, address marginalized communities' needs, and ensure equitable benefits. Future research avenues include exploring cultural implications, developing ethical AI metrics, and examining hybrid human-AI systems' resilience. Ultimately, addressing evolutionary asymmetries is crucial for fostering beneficial human-AI collaboration.