PROMOTE TRUST IN AI-DRIVEN SYSTEMS BY ADDRESSING FEARS AND EMPHASIZING THE IMPORTANCE OF HUMAN INVOLVEMENT

This study examines AI adoption's ethical, social, and economic aspects, addressing public fears and prioritizing human involvement to build trust in AI-driven systems. Eight questions guide the research, which uses qualitative and quantitative methods to examine AI systems' multifaceted effects on various sectors and stakeholders. Public and stakeholder concerns like job displacement, data privacy risks, and ethics influence AI adoption. Algorithmic bias, often caused by unrepresentative training data, perpetuates inequality, especially for marginalized groups. Explainable AI (XAI) and human-in-the-loop (HITL) systems are key to improving accountability and fairness in AI decision-making. Due to socioeconomic impacts like job displacement and economic disruption, reskilling and upskilling are needed to transform the workforce.