Ethical Dilemmas in AI-Driven Decision-Making Processes

Abstract

This study aims to explore the ethical dilemmas associated with AI-driven decision-making processes, focusing on issues such as bias, transparency, accountability, and fairness in various sectors. A qualitative research design was used, employing semi-structured interviews to gather data from 31 participants with experience in AI applications across technology, healthcare, finance, and education. Participants were recruited through online professional networks, and data were analyzed using thematic analysis with the assistance of Nvivo software. Theoretical saturation was reached when no new themes emerged from the interviews. The study identified several key ethical concerns, including algorithmic bias, lack of transparency, and accountability. Participants expressed that AI systems often perpetuate biases in decision-making, especially in recruitment, credit scoring, and criminal justice. The opacity of AI decision-making processes was also highlighted, with concerns about the lack of clarity in how AI systems arrive at decisions. Additionally, the issue of accountability was discussed, with participants emphasizing the need for clear liability frameworks when AI systems make harmful or incorrect decisions. Fairness and equity were also significant themes, with participants noting the risk of AI exacerbating societal inequalities if not properly designed. The findings underscore the importance of addressing ethical dilemmas in AI to ensure that AI systems are transparent, accountable, and equitable. Developers and organizations must prioritize diversity in training data, implement clear accountability structures, and adopt explainable AI techniques to mitigate risks. Further research and regulatory measures are needed to promote ethical AI use across various sectors.