Bneutrality

Bneutrality is a principle in information ethics and technology governance that seeks to minimize biased influence in the processing, presentation, and dissemination of information. It defines neutrality as a state in which system outputs do not unduly favor, penalize, or silence particular demographic groups, viewpoints, or narratives. Proponents describe Bneutrality as a proactive approach to reduce discriminatory effects in algorithms, data selection, and decision rules while preserving useful distinctions where they are required to meet legitimate goals.

Origins and scope: The term has appeared in speculative discussions and academic writing about fair and responsible AI, data governance, and media ethics in the 2020s. It is distinct from but related to net neutrality and from broader principles of fairness and transparency.

Key principles:

- Bias minimization: efforts to reduce disproportionate impacts across groups.

- Representational balance: inclusion of diverse perspectives in datasets and outputs.

- Transparency and auditability: clear methods and the ability to verify results.

- Accountability: mechanisms to address harms and adjust processes.

Applications: In artificial intelligence, Bneutrality guides training data selection, model evaluation, and deployment monitoring; in search and recommendation systems to avoid echo chambers; in journalism and public information to promote even-handed coverage; and in policy design to assess tradeoffs.

Criticisms: Critics warn that perfect neutrality is difficult or impossible, may obscure legitimate value judgments, or enable false equivalence. Implementations may be computationally costly and can raise concerns about censorship or bias suppression.

See also: neutrality, bias, algorithmic fairness, net neutrality.