# Fairness

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**Fairness** addresses the possible disparate outcomes end users may experience
related to sensitive characteristics such as race, income, sexual orientation,
or gender through algorithmic decision-making. For example, might a hiring
algorithm have biases for or against applicants with names associated with a
particular gender or ethnicity?

Learn more about how machine learning systems might be susceptible to human bias
in this video:
[Video](https://www.youtube.com/watch?v=59bMh59JQDo)

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For a real world example, read about how products such as Google Search and
Google Photos improved diversity of skin tone representation through the
[Monk Skin Tone Scale](https://blog.google/products/search/monk-skin-tone-scale/).

There are reliable methods of identifying, measuring, and mitigating bias in models. The [Fairness](https://developers.google.com/machine-learning/crash-course/fairness)
module of [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)
provides an in-depth look at fairness and bias mitigation techniques.

[People + AI Research](https://pair.withgoogle.com/) (PAIR) offers interactive
AI Explorables on [Measuring Fairness](https://pair.withgoogle.com/explorables/measuring-fairness/)
and [Hidden Bias](https://pair.withgoogle.com/explorables/hidden-bias/) to walk
through these concepts.
For more terms related to ML Fairness, see [Machine Learning Glossary:
Fairness \| Google for Developers](https://developers.google.com/machine-learning/glossary/fairness).