What harm may arise from AI's potential for discrimination against specific population subgroups?

The choice of group harms as the correct answer highlights the systemic nature of discrimination that can occur when AI systems are applied to real-world scenarios. Group harms refer to the adverse effects that specific demographic groups may experience as a result of biased algorithms or data. These can manifest in various ways, such as unequal access to services, perpetuation of stereotypes, and exacerbation of existing social inequalities.

When AI systems are trained on data that reflects societal biases or when they fail to accurately represent diverse populations, they can lead to decisions that disproportionately impact certain groups. This can occur in areas such as hiring practices, law enforcement, healthcare, and lending, resulting in unfair treatment or exclusion of specific populations based on race, gender, or other characteristics.

This distinction is important because while economic, individual, and reputational harms are significant, they typically arise as secondary consequences of the foundational issue of group harms. For instance, individuals within a discriminated group may experience personal setbacks (individual harms) which could also lead to broader economic consequences (economic harms) for the community. Moreover, organizations that utilize biased AI may suffer reputational damage (reputational harms) for appearing unfair or discriminatory, but the central concern remains the direct impact on the affected groups themselves. Understanding these