Archives

  • Advances and Challenges in Artificial Intelligence and Cybersecurity
    Vol. 1 No. 1 (2024)

    The first issue of the International Journal of Artificial Intelligence and Cybersecurity delves into the transformative impact of artificial intelligence on the field of cybersecurity. This issue highlights the dual role of AI as both a powerful tool for securing digital systems and a potential enabler of sophisticated cyber threats. It explores cutting-edge research and innovative approaches that harness AI to address the complexities of modern cybersecurity challenges.

    This issue includes contributions that address:

    • AI-powered solutions for threat detection, malware analysis, and anomaly detection.
    • The use of machine learning algorithms in building predictive cybersecurity models.
    • AI in securing IoT devices and autonomous systems against cyber vulnerabilities.
    • Emerging threats posed by adversarial AI, such as deepfakes and AI-driven attacks.
    • Ethical and regulatory considerations for AI integration in cybersecurity frameworks.

    By bringing together a diverse range of perspectives from academia, industry, and government, this issue sets the stage for meaningful discussions and collaborations. It aims to provide readers with a comprehensive understanding of how AI is shaping the future of cybersecurity, addressing challenges, and paving the way for innovative solutions in a rapidly evolving digital ecosystem.

  • Bias and Fairness in AI Models
    Vol. 1 No. 3 (2025)

    Bias and Fairness in AI Models is a critical issue in the integration of artificial intelligence (AI) in healthcare. It refers to the potential for AI systems to perpetuate or exacerbate existing biases and disparities, leading to unfair treatment or outcomes for certain groups of patients. This can occur due to several factors:

    Data Bias: AI models are often trained on datasets that may not adequately represent diverse patient populations, such as racial and ethnic minorities. This can result in models that perform less accurately for these groups, leading to misdiagnosis or unequal access to treatment.

    Algorithmic Bias: Biases can be embedded in the algorithms themselves, affecting how data is processed and decisions are made. For example, some algorithms may use cost as a proxy for illness, which can inadequately identify health needs in certain populations.

    Healthcare Disparities: AI can perpetuate existing healthcare disparities by disproportionately benefiting well-resourced populations while excluding those with limited access to healthcare services26.

    Ethical and Legal Concerns: Bias in AI models raises ethical and legal questions about accountability, transparency, and patient consent. Ensuring fairness requires diverse and representative data, algorithm audits, and collaboration among stakeholders.

    Strategies to address these biases include using diverse datasets, promoting transparency in AI decision-making, and engaging patients and communities in AI development. By addressing bias and ensuring fairness, AI can be used more effectively to improve healthcare outcomes for all patients.

  • Advances and Challenges in Artificial Intelligence and Cybersecurity
    Vol. 1 No. 2 (2025)

    The second issue of the International Journal of Artificial Intelligence and Cybersecurity delves into the transformative impact of artificial intelligence on the field of cybersecurity. This issue highlights the dual role of AI as both a powerful tool for securing digital systems and a potential enabler of sophisticated cyber threats. It explores cutting-edge research and innovative approaches that harness AI to address the complexities of modern cybersecurity challenges.

    This issue includes contributions that address:

    • AI-powered solutions for threat detection, malware analysis, and anomaly detection.
    • The use of machine learning algorithms in building predictive cybersecurity models.
    • AI in securing IoT devices and autonomous systems against cyber vulnerabilities.
    • Emerging threats posed by adversarial AI, such as deepfakes and AI-driven attacks.
    • Ethical and regulatory considerations for AI integration in cybersecurity frameworks.

    By bringing together a diverse range of perspectives from academia, industry, and government, this issue sets the stage for meaningful discussions and collaborations. It aims to provide readers with a comprehensive understanding of how AI is shaping the future of cybersecurity, addressing challenges, and paving the way for innovative solutions in a rapidly evolving digital ecosystem.