INTEGRITY IN BIG DATA AND UTILITARIANISM: THE RESPONSE OF BERNARD WILLIAMS

Abstract

This paper examines the problem of integrity in the decision of the big data-driven algorithms. Big data refers to datasets that are too large, complex, or fast-moving for traditional processing methods to handle effectively. Data-driven algorithms often rely on historical data to make predictions, but when this data is biased, the outcomes are similarly skewed. The reliance on a utilitarian approach in big data practices; prioritizing efficiency and statistical outcomes for the majority, risks compromising individual integrity by neglecting personal autonomy, dignity, and the complexity of human experience. It is on the basis of this, this paper examines the ethical implications of utilitarianism in big data and algorithmic decision-making, using Bernard Williams’ concept of integrity as a theoretical framework. The paper argues that the utilitarian justification for maximizing utility through big data often leads to the erosion of personal integrity, especially when individuals are treated merely as means to an end. Through evaluative and conceptual analysis methods, this paper advocates for an ethical reevaluation of big data practices, proposing that Williams’ insights into integrity should guide the development of more ethically responsible technologies that preserve personal and moral authenticity.