The Telephone AI Paradox: How Voice Agents Can Help Counter Unwanted Telemarketing Through Role-Based Automation, Transparency, and Governance

Abstract

Unwanted telemarketing calls are a persistent source of consumer frustration and a legally regulated issue in Germany. At first glance, the idea of addressing this problem with AI-based voice technology appears contradictory: why should an automated caller help restore trust in a communication channel that has been damaged by aggressive outbound practices? This design-oriented case and prototype study argues that the paradox can be resolved through a different design logic. Rather than using AI to intensify persuasion, we present a role-based voice-agent architecture that constrains conversational behavior through narrow task boundaries, explicit escalation rules, and auditable data handling. The paper reports a transfer project involving FH Aachen students, Capgemini, and Fairdient GmbH. Methodologically, the work is positioned as a design-oriented case study with a prototype artifact. The contribution is threefold: first, we describe a three-agent architecture for outbound screening, consent-aware explanation, and inbound service; second, we derive governance principles for legally and ethically sensitive telephony, including transparency, bounded knowledge, privacy-preserving deployment, and human fallback; and third, we propose an evaluation framework covering conversion, compliance, hallucination control, user trust, and cost per validated outcome. The prototype does not yet claim large-scale field effectiveness. Instead, it offers a structured and empirically testable design for trustworthy voice automation in a domain where misuse, opacity, and user distrust are especially pronounced.