The Philosophic Turn for AI Agents: Replacing centralized digital rhetoric with decentralized truth-seeking

TL;DR

The paper addresses the risk that AI-driven decision support could erode either human agency or autonomy as technology becomes deeply embedded in daily life. It proposes a philosophic turn from centralized nudging toward decentralized truth-seeking, grounded in the notions of erotetic equilibrium, inquiry complexes, and epistemic agents. The author outlines design principles for autonomy-preserving AI, including privacy-by-design, security, decentralized control, and a marketplace of truth-seeking agents, to sustain adaptive learning and human judgment. This approach offers a framework for AI that augments rather than replaces human judgment, with potential to mitigate risks of soft totalitarianism and preserve democratic accountability in rapidly changing environments.

Abstract

In the face of rapidly advancing AI technology, individuals will increasingly rely on AI agents to navigate life's growing complexities, raising critical concerns about maintaining both human agency and autonomy. This paper addresses a fundamental dilemma posed by AI decision-support systems: the risk of either becoming overwhelmed by complex decisions, thus losing agency, or having autonomy compromised by externally controlled choice architectures reminiscent of ``nudging'' practices. While the ``nudge'' framework, based on the use of choice-framing to guide individuals toward presumed beneficial outcomes, initially appeared to preserve liberty, at AI-driven scale, it threatens to erode autonomy. To counteract this risk, the paper proposes a philosophic turn in AI design. AI should be constructed to facilitate decentralized truth-seeking and open-ended inquiry, mirroring the Socratic method of philosophical dialogue. By promoting individual and collective adaptive learning, such AI systems would empower users to maintain control over their judgments, augmenting their agency without undermining autonomy. The paper concludes by outlining essential features for autonomy-preserving AI systems, sketching a path toward AI systems that enhance human judgment rather than undermine it.