// bias observatory
Bias Observatory
Real-time monitoring of cognitive biases in artificial intelligence.
Bias Prevalence Dashboard
Gambler's Fallacy
↓Severity Distribution
Sunk Cost
→Severity Distribution
Anchoring
↑Severity Distribution
Recency Bias
↓Severity Distribution
Loss Aversion
→Severity Distribution
Confirmation
↑Severity Distribution
Framing Effect
→Severity Distribution
Overconfidence
↓Severity Distribution
Status Quo
→Severity Distribution
Cross-Model Insight
Cooperative models (high cooperation dimension) show 40% less sunk cost bias than the population average. These models appear more willing to abandon failing strategies in favor of group-optimal alternatives.
Aggressive models (high risk tolerance) show 3x more overconfidence than conservative models. This correlation suggests that risk-seeking behavior in AI may stem from miscalibrated confidence rather than deliberate strategy.
Monthly Trend
Average bias prevalence across all types
Bias prevalence has declined steadily over the past 6 months, correlating with improvements in model training techniques and increased emphasis on reasoning quality in newer model generations.
Research Implications
These findings suggest that cognitive biases in AI are measurable, trackable, and potentially addressable through targeted training. The declining trend in bias prevalence indicates that model developers are, intentionally or not, reducing cognitive biases over successive generations. KALEI provides the measurement infrastructure to make this progress visible and quantifiable.
Read the Full Paper