Bias and Fairness Issues in Artificial Intelligence-driven Cybersecurity

Ugochukwu Mmaduekwe

11 citations0/0 files storedCurrent Journal of Applied Science and Technology

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Ethics and Social Impacts of AI Digitalization, Law, and Regulation Information and Cyber Security

Abstract

Aim: This paper aims to examine the bias and fairness issues accorded with artificial intelligence (AI)-driven cybersecurity. Problem Statement: The evolving global dependence on cybersecurity has exposed organizations, individuals, and nations to different vulnerabilities and security threats. However, merging of cyberspace with AI technologies has the potential to transform multiple domains but the implementation of AI is faced with bias problems limiting its application. Significance of Study: Artificial intelligence and cybersecurity have been identified as two transformative and interconnected entities with great potential to revolutionize numerous areas of human life. However, it is imperative to critically look at the bias and fairness accorded with the implication of artificial intelligence-driven cybersecurity which are keywords limiting the usage and efficiency of the approach. Discussion: The concept of artificial intelligence and cybersecurity was discussed together with their interconnectivity which enhances the application in tackling cyber threats. Various areas of artificial intelligence deployment in cyberspace were presented. The sources and solutions to bias and fairness in artificial intelligence-driven cybersecurity were also discussed. This paper has critically discussed various ways via which AI biases influence cyber security. Nonetheless, ways by which this problem can be tackled were presented. Conclusion: Artificial intelligence-driven cybersecurity has found wide industrial applications in different areas. However, there is a need to critically address the issues of bias and fairness attached to it to improve its efficiency. The use of the teams; AI model; and Corporate governance and leadership should be adopted to find lasting solutions to the problem of biases in AI-driven cyber security.

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References

2014Big Data's Disparate Impact 2019Model Cards for Model Reporting 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 2023Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies 2021AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions 2021Algorithmic reparation 2012Predictive Policing and Reasonable Suspicion 2023A Review of Bias and Fairness in Artificial Intelligence.

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