Bias Bounty

Unlike traditional bug bounties that target code errors, Humane Intelligence’s algorithmic bias bounties focus on discovering the root causes of biased or exclusionary outcomes in AI systems. Bias bounties are collaboratively designed sets of challenges that bring together researchers, impacted communities, and domain experts to rigorously examine and improve AI / ML systems, models, and datasets. Instead of treating bias as an abstract or philosophical debate, bias bounties create a structured process where bias can be systematically surfaced, measured, and addressed.

Key Goals of Bias Bounties

• Center lived experience — surface bias by co-designing challenges with the people most affected.
• Generate actionable improvements — move beyond theoretical discussions toward concrete fixes and retraining inputs.
• Build inclusive defaults — design tools, datasets, and models that perform equitably across diverse contexts.
• Strengthen the AI ecosystem — build a fairness-focused community of practice, equip developers with practical tools, and generate insights that enhance partner systems and foster public trust.

About our challenges

Humane Intelligence takes a hands-on approach to ensure every bounty is impactful, well-executed, and aligned with our partners’ goals. We combine expertise in bias, sociotechnical research, and data science, and work closely with our organizational partners to co-design each challenge scope, engage the right participants, and evaluate findings in a way that honors impacted communities while also driving technical improvement.

Participants use systematic testing methods to uncover issues like biased training data, discriminatory default settings, and algorithmic blind spots that fail to account for human diversity. Beyond documenting exclusionary patterns, participants also design and develop technical solutions that enhance system performance in real-world conditions.