Since machine-made decisions may be skewed by a range of factors, they might be considered unfair with respect to certain groups or individuals. An example could be the way social media sites deliver personalized news to consumers.
Just as satellites orbit the Earth, providing a focused and detailed view of specific regions, our “Sustainability Satellites” offer an in-depth exploration of the Sustainability Directory . Each satellite represents a vital aspect of this broader field, and each is presented as a domain. Glossaries illuminate the key concepts, terminology, and challenges within each area, allowing to build a more complete and nuanced understanding of the interconnected world.
As is the case with several ethical concepts, definitions of fairness and bias can be controversial. In general, fairness and bias are considered relevant when the decision process impacts folks's lives.
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Being a comprehensive set of capabilities, it may be confusing to figure out which metrics and algorithms are most appropriate for a given employ case. To support, we have created some guidance material that can be consulted.
Furthermore, visual representations like the one above help us fully grasp the concept of Ai Algorithmic Fairness Metrics.
The EU AI Act is now real. Enforcement is approaching. And organizations across Ireland and Europe are scrambling to understand what "trustworthy AI" actually means in practice.
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The AI Fairness 360 interactive experience provides a gentle introduction to the concepts and capabilities. The tutorials and other notebooks offer a deeper, data scientist-oriented introduction. The complete API is furthermore available.
