Fairness in AI is not a vibe; it is a gate.
Here are 5 fairness checks I consider for any model:
1. Worst-group metric (Demography Parity/Disparate Impact/EOd)
2. Intersections (not just single attributes)
3. Min-N for power
4. 95% CI (bootstrap)
5. Pass rule + KPI guardrail
1/ Here’s how pandas groupby() works:
It groups rows in your DataFrame by a key (e.g. product, region, team) and lets you run calculations on each group. Think of it as SQL’s GROUP BY—but with more flexibility. 👇
I am glad to announce that after several months of exploring the often "vague" idea around ethical and responsible AI solutions, I have successfully completed the AI Ethics program with Turing College.
Cheers to responsible AI solutions🥂🎉.
It's so crazy to realize that I have written and published 18 articles on @DataCamp!🙂
I believe these articles will be useful for folks just starting their data analysis journey, so I will be sharing them on here.
A Thread🧵
#DataScience#Python#DataAnalysis
Fairness in AI is not a vibe; it is a gate.
Here are 5 fairness checks I consider for any model:
1. Worst-group metric (Demography Parity/Disparate Impact/EOd)
2. Intersections (not just single attributes)
3. Min-N for power
4. 95% CI (bootstrap)
5. Pass rule + KPI guardrail
Fairness in AI is not a vibe; it is a gate.
Here are 5 fairness checks I consider for any model:
1. Worst-group metric (Demography Parity/Disparate Impact/EOd)
2. Intersections (not just single attributes)
3. Min-N for power
4. 95% CI (bootstrap)
5. Pass rule + KPI guardrail
I think congrats again to OpenAI for cooking with GPT-5 Pro. This is the third time I've struggled on something complex/gnarly for an hour on and off with CC, then 5 Pro goes off for 10 minutes and comes back with code that works out of the box. I had CC read the 5 Pro version and it wrote up 2 paragraphs admiring it (very wholesome). If you're not giving it your hardest problems you're probably missing out.