Bias and Fairness
When AI Gets It Wrong
You are previewing the first lesson — free to explore.
In Course 3, you learned where bias comes from technically: unrepresentative training data. Now let's look at a real, well-documented case of what that looks like in the world, and who stepped up to fix it.
In 2018, researcher Joy Buolamwini — at the time a graduate student at MIT — tested several commercial facial-recognition systems and found they were highly accurate for lighter-skinned men, but far less accurate for darker-skinned women, with error rates over 30% higher in some systems.
📍 Research conducted at MIT Media Lab, with Timnit GebruThe datasets used to train those facial-recognition systems were not representative — they contained far more lighter-skinned faces than darker-skinned ones. The AI hadn't 'learned to be unfair' on purpose; it had simply learned an incomplete pattern from incomplete data, exactly as Course 3 described.
This research — published as the 'Gender Shades' study — didn't just criticize the problem; it directly led major technology companies to measurably improve their systems' accuracy across skin tones and genders. It's a powerful example of one researcher's rigorous work changing an entire industry.
Check your understanding
1. What did the Gender Shades research find?
2. What was the real-world impact of this research?