A Note on Bias to Complete
This work addresses bias in LLMs for societal applications, but it appears incremental as it builds on existing bias concepts without demonstrated results.
The paper revisits bias definitions by discovering new bias types in dynamic environments and proposes a framework with eight hypotheses and corresponding bias-minimizing strategies, though its implementation remains incomplete.
Minimizing social bias strengthens societal bonds, promoting shared understanding and better decision-making. We revisit the definition of bias by discovering new bias types (e.g., societal status) in dynamic environments and describe them relative to context, such as culture, region, time, and personal background. Our framework includes eight hypotheses about bias and a minimizing bias strategy for each assumption as well as five methods as proposed solutions in LLM. The realization of the framework is yet to be completed.