AI or Bye: Tackling Ethical Dilemmas in Financial Automation

The primary feature of financial automation is the use of technology, particularly artificial intelligence (AI), to streamline, amplify, and optimize established financial processes. However, its usage has grown at a very high pace, and this has raised some very important ethical issues. Challenges include the following: algorithmic bias, absence of clear self-direction, lack of direct accountability, data privacy, and the effect of algorithms on human employment. This chapter discusses these ethical issues, providing an overview of where they originated, what their ramifications are, and how they may be resolved.Some of the main ethical issues associated with AI include the emergence of prejudices in AI systems and how they might work against deserving client credit scores, loan approvals, and fraud detections. These concerns revolve around the idea of transparency, as AI systems are opaque and thus bring difficulties in the interpretability of such technical decision-making. Another issue is accountability—who is to blame for an AI’s mistake is still unclear. Lastly, there exist problems of data protection and information security, especially with the use of big data, which impact consumer confidence and regulatory concerns.It is in this regard that this chapter seeks to offer a comprehensive strategy for addressing these challenges; more specifically, this chapter suggests the adoption of programs such as fairness-aware machine learning and explainable AI while further advocating for a legal requirement approach toward ethical compliance in this field, involving stakeholders from academia, businesses, and government. If these issues are tackled adequately, financial institutions may attain a position to enhance responsible AI that promotes fairness, transparency, and accountability and protects privacy.