Published Paper Details:

The main aim of this research was to propose the concepts of Explainable and Ethical Machine Learning (EEML) as the solutions to the problems arising from the fast-growing Artificial Intelligence (AI). While technological advancement is inevitable, AI has been growing by leaps and bounds in recent years and changing society in significant ways. Although the rapid progress of AI technology is impressive, it has also highlighted some drawbacks, i.e., lack of transparency and ethical use of algorithms [1]. This paper mainly aims to demystify AI by putting in place standards that govern transparency and ethics in the development of AI. By critically evaluating the present position of AI systems and future insights, the analysis contributes to adopting the EEML approach. These guidelines are directed toward the principle of visibility, trustworthy behaviour, and moral evaluations at the various stages of the AI cycle. Through the campaign to support the implementation of EEML frameworks, stakeholders will contribute to maximizing trust and accountability in AI systems. Transparency of AI algorithms allows users to get the fundamental corrections processes of determining, which builds confidence and makes the technologies relevant. Moreover, ethical issues exist to see that AI systems territorialize on moral norms, thus preventing biased or unfair choices. Also, due to the promotion of socially responsible AI innovation through the EEML policy, all stakeholders in this field can help speed up the process of society's acceptance of AI technology [1]. Consequently, AI developers need to pay special attention to transparency and ethics to troubleshoot privacy violations, discrimination, and other ethical issues with AI deployment. In the end, efforts of an EMLC type are significant for developing innovative, responsible AI and its use. The intersection of transparency and ethical considerations in AI development processes, stakeholders can prevent what may be opaque and biased AI systems from getting damaging results. Consequently, this will be one of the factors impacting the AI tools' performance and positive effects on society.