Fairnessaddresses 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:
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 .
There are reliable methods of identifying, measuring, and mitigating bias in models. The Fairness module of Machine Learning Crash Course provides an in-depth look at fairness and bias mitigation techniques.
People + AI Research (PAIR) offers interactive AI Explorables on Measuring Fairness and Hidden Bias to walk through these concepts. For more terms related to ML Fairness, see Machine Learning Glossary: Fairness | Google for Developers .
