arXiv subjects

Victor Frimpong

Publications and source records attributed to Victor Frimpong.

3 recordsLinked to original sources

arXiv2026

Empathy and the Human-Moment Gaps of AI Chatbots: Insights from Empathy Displacement Theory

Artificial intelligence (AI) chatbots are increasingly deployed in domains where empathy is essential, including healthcare, education, and customer service. However, their capacity to sustain authentic human moments remains structurally limited. This paper introduces two interlinked conceptual models to explain and address this limitation. First, the Human-Moment Gap Framework (HMGF) identifies three structural empathy deficits in AI-mediated interaction: affective surfaceism (emotional imitation without depth), memory fragmentation (lack of relational continuity), and moral framing mismatch (efficiency prioritised over dignity). Second, the paper develops the Empathy Displacement Theory (EDT), which explains how AI-simulated empathy can progressively substitute, distort, and displace genuine human empathy across individual, relational, and organisational contexts. HMGF serves as the causal foundation of EDT by demonstrating how technical and moral deficiencies in chatbot design may evolve into broader social and institutional consequences. The study is conceptual and exploratory, aiming to develop an integrative theoretical framework rather than provide empirical validation. Together, HMGF and EDT provide a unified framework for understanding AI-mediated empathy, generating testable propositions and implications for the responsible development and governance of empathetic AI systems. The paper concludes that the central challenge of empathetic AI is not whether machines can genuinely care, but how simulated care reshapes human emotional expectations, interpersonal behaviour, and institutional norms.

cs.HC↗

arXiv2026

The Governance Inversion Hypothesis: Why More AI Regulation May Produce Less Organisational Control

This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditions of increasing regulatory expansion and technological complexity, organisations may become more formally governed while simultaneously experiencing a decline in operational control over AI systems. Existing AI governance frameworks generally assume that stronger regulation improves accountability, oversight, and organisational control. This paper challenges that assumption by arguing that governance formalisation itself may contribute to the erosion of control in AI-intensive environments. Drawing on institutional theory, organisational governance research, accountability scholarship, and emerging AI governance literature, the paper develops a conceptual framework explaining how regulatory expansion may weaken operational authority through four interconnected mechanisms: authority fragmentation, symbolic governance expansion, externalisation of control, and authority paralysis. As governance systems become increasingly layered and procedurally dense, organisations may struggle to maintain coherent authority, technical visibility, escalation capability, and meaningful intervention power over opaque and externally mediated AI infrastructures. The paper extends institutional decoupling theory by introducing governance inversion as a structural condition in which governance expansion may actively undermine operational coherence rather than strengthen it. It concludes that the central risk in AI governance may not be the absence of governance structures, but the emergence of institutions that appear increasingly governed while progressively losing the capacity to govern effectively.

cs.CY↗

arXiv2026

AI Debris: Residual Risk and the Afterlife of Failed AI Systems

AI governance frameworks primarily focus on risks during the development and deployment phases, implicitly treating system withdrawal as a technical shutdown. This paper argues that decommissioned AI systems generate residual risk, termed AI debris, that persists after model removal and continues to shape institutional behaviour, accountability, and trust. AI debris is defined as the post-withdrawal socio-technical residue of AI systems, including workflow dependency, data contamination, capability displacement (deskilling), legitimacy erosion, and accountability breakdown. The paper develops a typology of debris domains and identifies mechanisms through which debris persists, including institutional memory, path dependency, blame avoidance, and feedback effects in organisational data. To operationalise the concept, the paper proposes an evaluator-ready AI Debris Decommissioning Protocol (AIDP), a stepwise checklist specifying auditable evidence for freezing decision footprints, incident review, remediation, contestability, and post-withdrawal accountability assignment. A brief vignette of Amazon's discontinued hiring tool illustrates how algorithmic decision categories and screening heuristics can persist after system rollback. The paper contributes a practical governance instrument for regulators, auditors, and organisations seeking to prevent paper compliance, strengthen AI lifecycle governance, and improve institutional resilience in high-stakes decision environments.

cs.CY↗