AI Ethics Risk Fairness

The chapters in this module, in reading order.

#	Chapter
00	AI Ethics, Bias & Fairness — The Five-Year-Old Version
01	When the judge learns discrimination — the clean dashboard lied
02	Sources of bias — how the evidence file gets crooked before training starts
03	Fairness metrics — competing jury instructions for one courtroom
04	Bias detection & auditing — building the appeal process for a deployed judge
05	Interpretability basics — opening the judge without pretending to read its soul
06	Explainability for LLMs — when the judge tells a nice story after the verdict
07	Model cards & documentation — writing the case record before trouble starts
08	Debiasing techniques — changing the evidence file, the judge, or the verdict rule
09	Fairness in LLMs — when the verdict is language, not only a score
10	Regulatory landscape — the rulebooks around the courtroom
11	Responsible AI practices — how institutions keep the courtroom honest
12	Fairness monitoring in production — keeping the appeal process open after launch
13	Honest admission — what AI ethics and fairness still cannot settle cleanly