ETHICAL CHALLENGES IN AI AND AUTOMATION — A CASE STUDY PERSPECTIVE

Abstract

Artificial Intelligence (AI) and Machine Learning have rapidly transformed many industries, and their adoption raises important ethical questions about how these technologies should be used responsibly. This paper reviews the core principles of AI ethics — fairness, accountability, transparency, privacy, and human oversight — and examines real-world cases across healthcare, the military and defence, finance and banking, e-commerce, social media, science and academia, aviation, autonomous vehicles, the workplace, consumer privacy, and AI-driven cyber threats. To ground the fairness discussion in evidence rather than anecdote, the healthcare section analyses published results from the Adversarial Medical Question Answering (AMQA) benchmark (Zhou et al., 2025), which systematically probes demographic bias in large language model diagnostic recommendations. Across domains, the cases reveal recurring risks — bias, misinformation, fraud, security threats, and the erosion of human oversight — that call for stronger regulation, clearer ethical guidelines, and effective accountability mechanisms in AI development and deployment.