Commit 88ba9fb8 authored by Paul Bethge's avatar Paul Bethge
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acknowledgement and caveats

parent 135de735
......@@ -12,6 +12,21 @@ WARRANTIES, see the file, "LICENSE.txt," in this distribution.
BSD Simplified License.
Acknowledgement
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We hereby thank TensorFlow for providing tools and examples to explore the topic of bias in Machine Learning (ML) applications.
We introduced small changes to the original notebook in order to suit our needs for this workshop. Please visit the [website](https://www.tensorflow.org/responsible_ai/) to learn more about how TensorFlow gives opportunities to make AI more responsible (including detecting and mitigating bias). Please find the original notebook [here](https://www.tensorflow.org/responsible_ai/fairness_indicators/tutorials/Fairness_Indicators_TFCO_CelebA_Case_Study).
Caveats
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This notebook uses the CelebA dataset. The dataset should not be interpreted as a true representation of the human population.
It is Insufficient to describe human diversity with binary attributes, i.e. there is no such thing as *young* and *not young*.
Furthermore, human diversity can not be quantified by only 40 attributes.
Nevertheless, it demonstrates perfectly well, how special care needs to be taken when using a not perfectly balanced dataset, to train a neural network to be fair.
How to use this repository
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Here is a quick overview of the necessary steps in this exercise:
......@@ -70,7 +85,7 @@ You may want to discuss the following questions:
#### 6 Optional: try it yourself by taking a selfie
If there is some time left for you, you can play around by looking at the outcome of some test images or by taking a selfie.
Keep in mind that the model has went through all necessary steps to perform good on real world data. This last step is just for demonstration purposes.
Keep in mind that the model did not go through all necessary steps to perform good on real world data. This last step is just for demonstration purposes.
Additional Resources
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