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Biases, in general, describe errors in decision making. A number of cognitive biases have been identified in the area of human decision making (see, e.g., here, here, and here). In the context of Artificial Intelligence, algorithmic biases have become more and more relevant for data-driven decision (see, e.g., here).
The aim of this thesis is to categorize, organize, and interrelate various types of biases (both cognitive and algorithmic) by developing a formal ontology for cognitive and algorithmic biases. Furthermore, this ontology will be used as a foundation for the development of a system for bias management whose aim would be to help identify biases in decision making in a more automated way.
We have different master topics available for ontology templates.
- April 2018, Topic 5 added.
- Feb 2018, Topic 4 added.