Ethical AI Auditor for Bias Detecting in AI Models Using Adversarial Debiasing

AI systems become more widely used in crucial sectors like healthcare, finance, and law enforcement. The research proposed to address issues of bias and fairness has become increasingly important. In this research innovation paper, we present an Ethical AI Auditor, a framework designed to detect and mitigate the biases of AI models using Adversarial Detecting Techniques (ADT) and equality measures like statistical parity and disparate impact for systematic assessment of the models and providing actionable mitigation strategies. The methodology Adversarial Debiasing Algorithm detects biases in protected attributes, races, gender, age and adjusts them accordingly for the model to be properly deployed as fair. Experimental evaluation of several AI models numerous configurations demonstrates that Ethical AI Auditor reduces biases dramatically at no loss or even with improvements in model accuracy. In this regard, it promotes ethical AI technology. Future work will be oriented towards perfecting the debiasing algorithms in the Auditor and applying them to a much broader range of AI systems as well as industries. The result proposed model is simulated accuracy values 93% of ADT.