Algorithmic Accountability in AI Driven Public Systems: Fairness in Allocation, Workforce and Safety
DOI:
https://doi.org/10.15680/IJCTECE.2024.0705011Keywords:
Accountability by algorithm, Artificial intelligence, Government regulation, Human resource management, Equity, Resource distribution, Safety in society, Openness, Socio technical systems, Versatile AI.Abstract
The rising use of the artificial intelligence (AI) in the systems of the public has posed a great challenge to the concerns of fairness, accountability, and governance. This paper looks at the concept of algorithmic accountability in three key areas namely the allocation of resources, the handling of the workforce and the safety-related areas of the population. The analysis, based on an integrative qualitative review of chosen scholarly sources, assesses the conceptualisations of accountability mechanisms and their possible application in AI-based decision-making. The results indicate that there is a strong theoretical basis in the current literature, but the application is still small and disparate. Algorithms bias in the allocation systems is also present because of the use of historically (skewed) data and lack of explicit policy frameworks based on equity. When there is low transparency and contestability in the framework of workforce management, it adversely affects institutional trust and procedural fairness. The conflict between speed of making decisions and accountability limits the usefulness of standard accountability frameworks in safety-related scenarios. In all areas, responsibility is often spread among various actors, which create responsibility and implementation loopholes. The research also indicates that transparency is not enough in itself, since in most systems we cannot get readable and actionable information that we can act on to conduct meaningful oversight. The discussion provides the significance of a socio-technical, lifecycle-based approach to accountability, which incorporates both technical design and institutional governance, and regulatory supervision. The best way to tighten the accountability of algorithms is to have more explicit responsibility frameworks, enforceable standards, and increase institutional capacity. The paper comes up with the conclusion that AI-driven public systems should have legitimacy based on the capacity to achieve efficiency, fairness and accountability where technology progress becomes equitable and responsible in governing people.
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