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Exploring the Ethical and Societal Implications of Incorporating User Data into the ML Workflow Lifecycle"
  • Ladan Farbiz
Ladan Farbiz
Malmö University

Corresponding Author:[email protected]

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Abstract

Abstract— The use of user data is a critical component of the machine learning (ML) workflow lifecycle, with data being used to train and improve the performance of ML models. However, the collection and use of user data raises significant ethical and societal concerns that must be addressed. This paper provides an in-depth exploration of the ethical and societal implications of incorporating user data into the ML workflow lifecycle, specifically during the design, implementation, evaluation, and maintenance phases. The paper examines key ethical considerations such as privacy, bias, potential misuse of data, job displacement, and broader societal concerns related to the role of technology in society. Additionally, the paper emphasizes the need for transparency in how user data is collected, used, and shared, and for ensuring that training data is diverse and representative of the population to avoid bias in ML models. Through a critical analysis of existing literature, this paper provides insights into best practices and current research related to the use of user data in the ML workflow lifecycle and offers recommendations for organizations to develop ethical and responsible approaches to incorporating user data into their ML workflows. The findings and recommendations in this paper can help guide organizations to develop more responsible and ethical approaches to incorporating user data into their ML workflows, ultimately leading to a more trustworthy and beneficial use of machine learning models for society.