Responsible artificial intelligence practices and adoption framework a study of higher educational institutions in emerging market

Abstract

If AI systems are to be understood in terms of their social consequences, they must be newlinerecognized as more than the sum of their software components. The social environment in newlinewhich AI systems are built, used, and acted upon is essentially socio-technical, with its newlinediverse set of stakeholders, institutions, cultures, conventions, and locations. While newlineconsidering the governance and implications of AI technology or the artefact that integrates newlineit, it is critical to understand that the technical aspect and the socio-technical system are newlineintricately intertwined. Interactions between individuals and groups performing various roles newline(creator, manufacturer, user, bystander, policymaker, etc.) comprise this system, as do the newlineprotocols and processes that govern those interactions. AI rules, principles, and methods newlinemust stress this socio-technical approach. In reality, it is not the ethical, dependable, or newline newline144 newline newlineresponsible part of an AI artefact or application. Rather, those who build, develop, or use newlinethese systems should take responsibility and act in accordance with moral and ethical norms newlineso that society can have faith in the system as a whole and the outcomes. Using ethical newlineprinciples in AI, contrary to popular belief, does not exonerate individuals and organisations newlineof accountability by handing robots any semblance of andquot;responsibilityandquot; for their actions and newlinechoices. Conversely, AI ethics advocates for greater accountability and responsibility on the newlinepart of the persons and groups involved, both for the decisions and actions of the AI newlineapplications and for their own decision to deploy AI in a specific application setting. newline