BIAS AND FAIRNESS IN ARTIFICIAL INTELLIGENCE APPLICATIONS WITHIN THE FINANCIAL SECTOR

AI-powered automation of credit evaluation, fraud detection, and investment decision-making is revolutionizingthe financial sector. Nevertheless, the widespread use of AI in finance is also raising considerable concernsabout bias and fairness. Bias is when algorithms, which have been trained on biased or unbalanced data, performin an unfair or discriminatory manner. In finance, this means that some people would have a limited creditaccess, make unjustified loan rejections, or a particular demographic group would be excessively over-flaggedfor triggering of transactions.These biases are very often invisible and can come from biased datasets, improperly chosen features, orimplicitly from human decisions taken during the designing of models. Once such AI is released, it incitesfeedback loops that may deepen the original inequalities, damage the principle of financial inclusion, anddecrease the level of trust of people in the system.To tackle such issues, companies have decided to incorporate data practices that are sensitive to fairness,perform algorithmic audits, and use explainable AI models like LIME and SHAP. Governance frameworksbased on ethics and worldwide rules such as the EU AI Act, which set transparency and accountability asprerequisites, are becoming the norm.In the end, it is the use of AI in a morally right way that calls for a proper balancing of ethics and innovation. Bycommitment to fairness, constant oversight, and a multitude of voices in the design of AI, financial institutionswill be able to deploy technology that will be a tool for equity, trust, and growth that is sustainable in the longrun.