Most definitions of algorithmic bias and fairness encode decisionmaker interests, such as profits, rather than the interests of disadvantaged groups (e.g., racial minorities): Bias is defined as a deviation from profit maximization. Future research should instead focus on the causal effect of...

This research investigates the ethical and privacy issues arising from using AI andML in neuromarketing, framed by rule utilitarianism. It assesses the impact of these technologies on consumerprivacy and human rights through a combination of literature review, bibliometric analysis, and...

This study attempts to accelerate the learning ability of an artifcial electric feld algorithm (AEFA) by attributing it with two mechanisms: elitism and oppositionbased learning. Elitism advances the convergence of the AEFA towards global optima by retaining the fne-tuned solutions obtained thus...