Ethical AI Integration in Cybersecurity Operations: A Framework for Bias Mitigation and Human Oversight in Security Decision Systems
Keywords:
AI ethics, cybersecurity, algorithmic bias, human oversight, explainable AI, HITL, HOTL, ethical design, bias mitigation, security decision systemsAbstract
One of the emerging ethical issues regarding artificial intelligence (AI) is the use of AI in cybersecurity, particularly algorithmic fairness, transparency, and oversight. This article introduces a mitigated methodology for ethical AI integration, incorporating considerations of fairness, accountability, and human-centered design into security decision-making processes. Based on a synthesis of existing literature, technical case studies, and normative models, the paper presents the main oversight mechanisms, i.e., Human-in-the-Loop (HITL) and Human-on-the-Loop (HOTL) oversight, explainable AI interfaces, and continuous feedback units. The results not only illustrate the potential and the constraints of applying AI ethically in cybersecurity but also point out the most important directions future research and collaboration between various disciplines should take.
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