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AI Fairness

April 20

The notion of fairness is based on producing results that are independent of sensitive or protected attributes such as traits of individuals with regards to their gender, ethnicity, disability or sexual orientation. Thus, fair AI algorithms cover the need for decisions that are bias and discrimination-free and ensure the avoidance of disparate treatment of groups across different cultural, demographic or phenotyping groups. In this ebook, we discuss cases of bias and fairness in AI, the latest research on the topic and the work Peltarion has put in to ensure ethical and fair AI usage.

We foresee that explainability will become a standard component of the machine learning and deep learning automated production pipeline.

Stella Katsarou, Data Scientist at Peltarion

  • Stella Katsarou

    Stella Katsarou

    Data Scientist

    Stella is a member of the Peltarion AI Research team working as a data scientist. She focuses on NLP and has a strong interest in Bias, Fairness and Explainability in AI.

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