Trust me, I’m wrong: The perils of AI hallucinations, a silent killer
Sourav Kumar Samanta & Anirban Chakraborty
What the paper says
Artificial intelligence is a disruptive technology, revolutionizing industries and fostering human capability. Irrespective of its efficiency, it is paired with a critical vulnerability, notably AI hallucinations. This case study dissects this phenomenon, where AI produces seemingly reasonable yet fabricated outputs. Hallucinations manifest in forms ranging from logical inconsistencies to persona deviations, with its detrimental consequences. They inflict severe reputational, financial, and ethical damage on both organizations and consumers. To address these risks, the case proposes a comprehensive two-pronged strategy. On the supply side, a technical framework of guardrails validates outputs and integrates trusted data sources, preventing hallucinations at its origin. Simultaneously, the demand side requires platform accountability and user education. Strategies include proactive prebunking, retroactive debunking, and initiatives that build user resilience through media literacy and cognitive nudges. The case helps students solve these problems by providing a practical framework for risk assessment and mitigation by empowering them to develop holistic, real-world solutions that combine both technical and behavioral strategies for the safe and responsible deployment of AI.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.