Artificial Intelligence for Social Good (AI4SG)

Abstract

A truly meaningful Al initiative must follow an impact-first approach, ensuring that success is measured not in terms of accuracy benchmarks or model performance, but in meaningful improvements in human well-being (Moon, 2020). For example, Al used in healthcare should be evaluated not by its processing speed but by its ability to reduce disease prevalence or improve early diagnosis rates. One major challenge is the use of Al-generated content without proper attribution or understanding. When students rely excessively on Al-generated work, they risk bypassing essential learning processes such as critical thinking, argumentation, and problem-solving. Additionally, Al models are known to hallucinate information, occasionally producing inaccurate or biased outputs, which can compromise the quality of academic work. As artificial intelligence continues to shape modern society, the importance of ethical AI development and deployment cannot be overstated. Al systems have the power to enhance human well-being, but without proper governance, they can also cause harm, reinforce inequalities, and operate without accountability. To mitigate these risks, Al must adhere to fundamental ethical principles that prioritize human dignity, fairness, and transparency.