Quantum Computing and AI Algorithmic Bias
- February 6, 2020
Using quantum computing to train neural networks promises to speed up training time. Doing so in autonomous vehicle applications makes sense and VW is reportedly doing just that. But when you think about quantum computing within the complexity:explainability framework (the more complex the application, the less explainable it is, see also chart below) it becomes apparent that applications that are vulnerable to algorithmic bias (e.g., in the employment screening space, policing, etc.) may become even more so. In other words, quantum computing may have a magnifying negative side effect that could render such applications too risky to use absent special mitigating controls. One proposed form of control comes in the form of an operating license; i.e., using quantum capabilities to train a neural network will require the person to be licensed to do so.
本源量子云平台
https://qcloud.originqc.com.cn/
IBM量子云平台
https://www.ibm.com/quantum-computing/
中科院量子云平台
https://qmlcloud.ai/
https://plato.stanford.edu/entries/qt-quantcomp/
https://law.stanford.edu/2020/02/06/quantum-computing-and-algorithmic-bias/
