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Fundamentals of ML observability
Metrics, feature importance and more
We’re excited ???? to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
Today, machine-learning systems are incorporated and integrated into countless industries and businesses that we rely on, including financial institutions, the housing industry, food and beverage, retail, and numerous others. As a result, they must meet both societal and legal standards to make decisions that are fair and inclusive.
We can determine that we would not want models to be negatively biased on the basis of characteristics such as religion, race, gender, disabilities, and political orientation.
In the simplest form, we can define fairness as equal results achieved for different individuals unless a real justification, in the form of a meaningful distinction, can be drawn between them. In fact, we’re still in the process of defining a whole new vocabulary and set of concepts to talk about fairness.
Learn more about AI Fairness here.
