Issues of using Machine Learning and unethical data collection in large corporations to identify functional error

Abstract

The integration of machine learning (ML) algorithms within large corporations has provided valuable insights and improved operational efficiency across various domains. However, the pursuit of these benefits has raised concerns regarding the ethical collection of data and the potential for biases and discrimination. This abstract discusses the issues surrounding the use of ML and unethical data collection practices in large corporations when identifying functional errors.
Firstly, ML algorithms heavily rely on large datasets to make accurate predictions and identify functional errors. However, the acquisition of such datasets can raise ethical concerns when large corporations employ unethical data collection practices. These practices may involve the surreptitious collection of personal information without individuals' consent or the exploitation of vulnerable populations. Unethical data collection not only violates privacy rights but also perpetuates systemic biases, leading to inaccurate and discriminatory outcomes when identifying functional errors.