Beyond AI Optimization – The Epistemic and Normative Primacy of Metacognition in Data-Driven Lobbying

In advanced democracies, lobbying, as interests’ representation with public decision-makers, has evolved, becoming a multifaceted form of intermediation between social actors, institutions, and policymakers, increasingly shaped by digital technologies and artificial intelligence (AI). This chapter explores the enduring human value in lobbying by examining metacognition—the reflective capacity to monitor and regulate cognitive processes—as a critical resource, contrasting it with AI’s epistemic and operational standardizations. Drawing on Flavell’s (1979) seminal work, metacognition encompasses knowledge and regulation, essential for navigating the dynamic, politically sensitive contexts of lobbying. The study positions lobbying as a cognitive and relational practice, demanding strategic adaptation, bias mitigation, and continuous learning—capabilities that AI struggles to replicate due to its lack of epistemic awareness, intentionality, and contextual ethical judgment. By blending psychological self-awareness with sociological insights, metacognition fosters in lobbying adaptive strategies and bona fide relations between lobbyists and decision-makers, complementing AI’s computational strengths in legislative framework mapping. This AI-metacognition synergy, thus, redefines the general context of lobbying, affirming metacognition’s critical role in effective, ethically grounded interest representation within complex institutional landscapes.