Downloaded 11 times
- 1 / 59
- 2 / 59
- 3 / 59
- 4 / 59
- 5 / 59
- 6 / 59
- 7 / 59
- 8 / 59
- 9 / 59
- 10 / 59
- 11 / 59
- 12 / 59
- 13 / 59
- 14 / 59
- 15 / 59
- 16 / 59
- 17 / 59
- 18 / 59
- 19 / 59
- 20 / 59
![Measurement defines:
- your variables of interest,
- the process for turning your
observations into numbers,
- how you actually collect the
data
[Fairness and Machine Learning, 2018]
Photo by Iker Urteaga on Unsplash](https://image.slidesharecdn.com/fairnessinmlcodemotion-190325141233/85/Fairness-in-Machine-Learning-Codemotion-23-320.jpg)
![The target variable is the
hardest to measure.
It is made up for the purpose
of the problem.
It is not a property that
people possess or lack
Ex. “creditworthiness”, “good
employee”, “attractiveness”
[Fairness and Machine Learning, 2018]
Photo by David Paschke on Unsplash](https://image.slidesharecdn.com/fairnessinmlcodemotion-190325141233/85/Fairness-in-Machine-Learning-Codemotion-24-320.jpg)
![labor statistics and the
male-as-norm bias
almost perfectly predict
which pronoun will be
returned
[Caliskan et al., 2017]](https://image.slidesharecdn.com/fairnessinmlcodemotion-190325141233/85/Fairness-in-Machine-Learning-Codemotion-27-320.jpg)
Skip to main content
Uploaded byAzzurra Ragone
Fairness in Machine Learning @Codemotion
The document discusses the importance of addressing bias in machine learning (ML) systems, highlighting how unfair data can lead to detrimental societal impacts, such as discrimination in critical areas like job offers and healthcare. It emphasizes the need for careful data measurement, analysis for missing values, and tools to detect and mitigate biases to ensure AI systems are fair and beneficial. The document ultimately calls for a cultural shift in AI research to prioritize fairness and accountability in automated decision-making.
