Bias in AI systems can manifest in various ways...
Citations
- Barocas, S., et al. (2024). "Fairness and Machine Learning: Limitations and Opportunities." MIT Press.
- Google AI. (2024). "Model Cards for Model Reporting." FAT '24 Conference Proceedings.
- AI Now Institute. (2024). "Discriminating Systems: Gender, Race, and Power in AI." NYU Report.
- Microsoft Research. (2024). "Fairlearn: A Toolkit for Assessing and Improving Fairness." MSR Technical Report.
Apply this now
Practice prompt
Re-write your AI Bias Detection: A Practical Framework prompt with explicit success criteria and critique instructions.
Try this now
Run the Analyzer pack twice (manual vs. AI) and compare the Overestimation Δ.
Common pitfall
Skipping reviewer verification time hides the real cost of rework and hallucinations.
Key takeaways
- •Run a manual vs. AI comparison to see actual lift.
- •Capture Overestimation Index and micro-TLX together.
- •Document what “good” looks like so teams can replicate it.
See it in action
Drop this into a measured run—demo it, then tie it back to your methodology.
See also
Pair this play with related resources, methodology notes, or quickstarts.
Next Steps
Ready to measure your AI impact? Start with a quick demo to see your Overestimation Δ and cognitive load metrics.