Auditing Bias in AI and Machine Learning-Based Credit Algorithms: A Data Science Perspective on Fairness and Ethics in FinTech

The legal, regulatory and corporate environment that artificial intelligence (AI) and machine learning (ML) use in creditscoring and credit lending situations has changed significantly in the financial services industry. Nevertheless, this changeof technology has raised even more issues related to the bias in algorithms and their influence on fairness, equity, andcompliance. The problem of auditing the potential bias in AI and ML-based credit algorithms is explored in this paperthrough the lens of data science with a focus on methodological aspects in an effort to track, quantify, and remove thediscriminatory patterns hiding inside the training sets and model architecture. This paper helps to bridge the gap in thediscussion of responsible AI in the context of financial technology because it provides a critical assessment of the existingbody of knowledge, a proposed framework of auditing, and practical advice to the stakeholders in FinTech. The evidencehighlights the necessity of open, responsible, and morally responsible data science in order to make certain that creditdecisions are not direct and worsen any existing forms of social injustice.