Artificial intelligence is often framed through questions of benefit and risk, yet its effects arise from how it reshapes the conditions under which knowledge is produced and acted upon. This paper examines AI for social good as a set of practices that extend the capacity to observe, classify, and infer at scales beyond human perception. These systems change what becomes visible, how that visibility is interpreted, and who is positioned to make use of it. As machine inference assumes more of the observational and interpretive load, authority shifts toward the institutions that design and govern these tools. The analysis identifies accountability, transparency, and equity as the practical conditions that determine whether AI strengthens or weakens the communities and environments it touches. The aim is to clarify how AI reorganizes the relationship between observation, judgment, and responsibility within projects framed as socially beneficial.
Rahul Dodhia (Wed,) studied this question.
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