Ethical Implications of AI in Automated Decision-Making Systems

Abstract

Artificial Intelligence is rapidly embedding in Automated Decision-Making (ADM) systems that transform key sectors like finance, healthcare, or criminal justice with increased decision-making efficiency and scalability. However, the deployment of ADM systems introduces glitches, which are major ethical challenges: bias, transparency, privacy, and accountability. Ethics here review that ADM systems, optimized for performance, often reflect historical biases embedded in their training data which might affect social inequalities. Moreover, the "black-box" nature of many machine learning models is impervious to transparency, further complicating accountability when decisions yield unfavourable results. The analysis shows that ethics schemes should be developed to guide the responsible development and deployment of ADM systems; fairness-aware algorithms, explainable AI techniques, and robust data governance form a suite of basic elements to safeguard individual rights and foster public trust. This paper concludes by recommending ways in which policymakers and practitioners can ensure ADM systems uphold values in society by weighing technological innovation against ethical integrity.