Which type of bias is associated with societal factors in AI testing?

Societal bias is the correct answer because it specifically refers to prejudices and inequalities that are present within society and how they can influence the behavior and outcomes of AI systems. This type of bias arises from social norms, cultural contexts, and existing inequalities, which can be embedded within the data used to train AI models or influence the design of these systems.

When AI systems are developed, they often reflect the values and assumptions of the society in which they were created. For instance, if historical data is biased due to social inequities—such as gender or racial discrimination—those biases can be carried over into AI algorithms, perpetuating the same problematic societal patterns. This emphasizes the need for a more conscious approach to AI governance that acknowledges these societal influences.

Other biases mentioned, such as computational, cognitive, and contextual biases, address different aspects of bias in AI. Computational bias deals with technical shortcomings in algorithms or data; cognitive bias refers to human judgment errors influencing AI design or data interpretation; and contextual bias looks at how specific situations can impact the effectiveness and fairness of AI systems. However, none of these specifically highlight the role of broader societal factors as directly as societal bias does.