The Intersection of Explainable Ai and Ethical DecisionMaking: Advancing Trustworthy Cloud-Based Data Science Models

Abstract

Cloud computing technology has dramatically transformed the data science ecosystem by improving its scopes of scalability and accessibility. It provides just-in-time compute capabilities including processing large datasets, deploying models, and conducting experiments. Nonetheless, this transformation triggers a set of ethical issues that need to be handled appropriately to achieve the proper ethical application of AI. Cloud deployed systems are an important aspect of current day AI, thus questions of trust, fairness, accountability, and data protection have come to the forefront further pointing to the challenges involved in the integration of AI at a large scale. The use of AI is essential because it provides interpretations of organizational decisions, and this brings about transparency, eradicating bias. Third, transparency and model interpretability is problematic in cloud platforms or raises objections because many AI models are “black-boxes”. When businesses implement AI using cloud services like AWS, Google Cloud, cloud, and Azure some of the challenges that emerge include data bias, regulatory policies’ compliance, and addressing the negative impacts of huge central AI installations on the environment. This paper discusses how cloud platforms enhance data science, as well as the ethical consideration of a pressing question of using these resources, thus providing a nuanced view of their relative advantages and disadvantages.