The Ethical Dilemma of Machine Learning: A Study on the Algorithmic Power Alienation of "Big Data Price Discrimination" in Financial Digitalization

Abstract

This article focuses on the phenomenon of "big data price discrimination" triggered by machine learning algorithms in the process of financial digitalization and deeply analyzes the problem of algorithmic power alienation behind it. Through the presentation and characteristic analysis of the "big data price discrimination" phenomenon, this paper reveals the process by which algorithmic power has evolved from a technical tool to a dominant force in the financial field, as well as the ethical dilemmas it brings about, such as the infringement of consumer rights and interests, the destruction of market fairness, and the crisis of social trust. Further explore the root causes of the alienation of algorithmic power, including the subjective intentions of algorithm designers, data bias, algorithmic black boxes, and the lack of supervision, etc. Finally, strategies to deal with the alienation of algorithmic power are proposed, aiming to promote the ethical application of machine learning algorithms in financial digitalization and achieve the harmonious development of technology and society.