Black is the new orange: how to determine AI liability

Author:

Padovan Paulo Henrique,Martins Clarice MarinhoORCID,Reed Chris

Publisher

Springer Science and Business Media LLC

Subject

Law,Artificial Intelligence

Reference35 articles.

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2. Aïvodji, U., Arai, H., Fortineau, O., Gambs, S., Hara, S., & Tapp, A. (2019) Fairwashing: the risk of rationalization. In International Conference on Machine Learning (pp. 161–170). PMLR

3. Angelov, P., Soares, E. (2019). Towards Explainable Deep Neural Networks (xDNN) (2019). Cornell University. ArXiv: https://arxiv.org/abs/1912.02523

4. Berendt, B., & Preibusch, S. (2012) Exploring discrimination: A user-centric evaluation of discrimination-aware data mining. In 2012 IEEE 12th International Conference on Data Mining Workshops (pp. 344–351). IEEE

5. Bloch, H. P. (2005) Successful failure analysis strategies. Reliability Advantage: Training Bulletin. Retrieved from http://www.heinzbloch.com/docs/ReliabilityAdvantage/Reliability_Advantage_Volume_3.pdf

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