Bias in Algorithmic Decision-Making: A Social Perspective AI-Powered Intrusion Detection Systems

Abstract

The proliferation of algorithmic decision-making systems across various sectors has raised concerns about the potential biases embedded within them. These biases, whether unintended or systemic, can have profound social implications, particularly in areas such as law enforcement, hiring, healthcare, and finance. This paper explores the issue of bias in algorithmic decision-making from a social perspective, emphasizing how these systems can perpetuate and amplify existing societal inequalities. The paper also explores the role of AI-powered intrusion detection systems (IDS), which have become an integral part of cybersecurity. IDS are designed to identify and mitigate potential security threats by analyzing patterns of network traffic using machine learning algorithms. However, similar to other AI applications, these systems can also exhibit bias, leading to skewed outcomes in threat detection, especially when trained on data that is not representative of diverse cyberattack scenarios. The paper will examine the sources of bias in both algorithmic decision-making and intrusion detection systems, assess the social consequences of biased systems, and discuss potential mitigation strategies. It will also explore the ethical challenges of deploying AI-based systems in sensitive areas and propose best practices for ensuring fairness and transparency. Ultimately, this paper calls for a multidisciplinary approach to address bias in algorithmic decision-making, with a focus on both technical solutions and social justice considerations.