Fairness - Bias

Fairness is, by far, the most discussed issue in the literature, remaining a paramount concern especially in case of LLMs and text-to-image models. This is sparked by training data biases propagating into model outputs, causing negative effects like stereotyping, racism, sexism, ideological leanings, or the marginalization of minorities. Next to attesting generative AI a conservative inclination by perpetuating existing societal patterns, there is a concern about reinforcing existing biases when training new generative models with synthetic data from previous models. Beyond technical fairness issues, critiques in the literature extend to the monopolization or centralization of power in large AI labs, driven by the substantial costs of developing foundational models. The literature also highlights the problem of unequal access to generative AI, particularly in developing countries or among financially constrained groups. Sources also analyze challenges of the AI research community to ensure workforce diversity. Moreover, there are concerns regarding the imposition of values embedded in AI systems on cultures distinct from those where the systems were developed.(p. 5)