Bias mitigation involves techniques used to reduce or remove unfair prejudices within data or algorithmic processes. In digital systems, this means working to ensure that outcomes are equitable across different groups. It addresses systematic distortions that could lead to discriminatory results in automated decisions or predictions. The aim is to achieve fairer and more objective system operations.
Context
Bias mitigation holds increasing relevance in the development of AI-driven tools and decentralized autonomous organizations (DAOs) within the crypto sphere. Concerns about fairness in smart contract execution, token distribution, or oracle data feeds necessitate robust strategies to prevent unintended systemic partiality. Regulatory bodies are also beginning to consider its importance for responsible innovation.
The Verifiable Entropy Function, a new primitive, guarantees maximal unbiased randomness from distributed inputs, fundamentally securing Proof-of-Stake consensus.
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