Security in the Digital Age: The Computational Intelligence Approach
Pravin Madhukar Dhanrao, Suchita Walke, Sweta Priya, Manesh Ramkrishna Palav, Bınod Kumar, Pushpesh · Apple Academic Press eBooks · 2025
In developing and implementing artificial intelligence (AI) systems, it is crucial to mitigate algorithmic biases, as the abstract examines. The unintentional reinforcement or amplification of societal biases is a serious problem in the context of our growing reliance on algorithms. In order to 186 fully understand the mechanics underlying certain outcomes, this essay emphasizes the significance of transparency in algorithmic decisionmaking processes. An essential component of preventing inadvertent biases is having development teams with diverse and inclusive representation. Over the course of an AI system’s lifecycle, biases can be found and fixed with the use of ongoing audits, monitoring, and the inclusion of fairness measures. To further promote justice, accountability, and transparency in AI, proactive bias prevention, moral standards, and public discussion are essential. In view of algorithmic biases’ multidimensional nature and the rapidly changing field of AI technology, the abstract emphasizes the need for a thorough and cooperative approach.