Adopting blockchain architecture enhances transparency and traceability of data and wages, making bias-driven decisions transparent and promoting women as co-authors of technological progress.

Current AI algorithms often mask prejudice under the guise of neutrality, with biased outputs reinforcing stereotypes, making recourse difficult for women.

Algorithms can hide prejudice behind mathematical processes, which complicates bias detection and perpetuates inequalities.

Legislation should mandate AI training data transparency via permissionless ledgers and prioritize onchain-audited systems in government procurement to promote gender balance.

Policies requiring AI data provenance disclosure and onchain transparency can accelerate progress toward fairer, gender-balanced AI systems.

Watermarking data with gender-disaggregated metadata and cryptographic signatures enables tracing biases and holding developers accountable.

AI automation disproportionately threatens jobs held by women, with nearly 10% of administrative and clerical roles at risk, and perpetuates gender stereotypes in system outputs.

Blockchain’s transparency can address biases in datasets and models, enabling civil-society oversight, and provide unforgeable proof of pay parity and data source watermarking.

The underrepresentation of women in decentralized finance and governance weakens trust and limits blockchain's potential to foster equality.

Advocates like Lisa Loud emphasize using blockchain to combat gender bias in AI systems and restore women's economic power.

Blockchain-based data provenance allows auditors to trace biases in AI models back to training data flaws, supporting accountability and retraining.

Women are underrepresented in AI engineering, constituting only 29.4% globally, which perpetuates bias in training data and workplace exclusion.