How does CueCrux avoid being accused of bias or discriminatory outputs?

Bias concerns typically arise when AI models produce ungrounded or one-sided content. CueCrux mitigates this through:

• Evidence-weighting instead of opinion-weighting, ensuring answers reflect the distribution of credible sources.
• Counterfactual exposure, which surfaces alternative perspectives automatically.
• Transparent weighting factors, including source authority, recency and domain diversity.
• Explicit labelling of disputes, avoiding artificial certainty.
• Receipts, enabling auditors to trace each signal used in the answer.

This makes CueCrux less vulnerable to accusations of systemic bias, because it does not rely on opaque model behaviour. Instead, it reveals the underlying evidence, allowing users and regulators to inspect weighting choices and verify fairness. If bias is present in source evidence, CueCrux surfaces the bias as a property of the source ecosystem, not the model.