Fairness in Artificial Intelligence

 

Machine decisions can affect our rights, and we need to ensure that Artificial Intelligence does not absorb biases by being trained on biased data.

Three papers to find out more: Machine Decisions and Human Consequences Teresa Scantamburlo, Andrew Charlesworth, Nello Cristianini in: Algorithmic Regulation, Yeung, K and Lodge, M eds, Oxford University Press (in press) https://arxiv.org/abs/1811.06747

Jia S., Lansdall-Welfare T., Cristianini N. (2018) Right for the Right Reason: Training Agnostic Networks. In: Duivesteijn W., Siebes A., Ukkonen A. (eds) Advances in Intelligent Data Analysis XVII. IDA 2018. Lecture Notes in Computer Science, vol 11191. Springer,  – https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_14 

Sutton A., Lansdall-Welfare T., Cristianini N. (2018) Biased Embeddings from Wild Data: Measuring, Understanding and Removing. In: Duivesteijn W., Siebes A., Ukkonen A. (eds) Advances in Intelligent Data Analysis XVII. IDA 2018. Lecture Notes in Computer Science, vol 11191. Springer, https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_27