Abstract for: A Call to Action for Use of System Dynamics for Reducing Bias in AI

Perceived (and actual!) bias in AI is the number one deterrent to widespread adoption of this emerging technology across society. Different sources of bias will be identified along with possible solutions for remedying the situation when deploying machine learning. In this talk we discuss sources of bias and how system dynamics best practices can be used to understand and directly address lack of transparency and fairness in the deployment of AI. We will discuss real-world examples and make use of reinforcing loops to describe the underlying phenomena of bias in AI. We then make a call to action for practitioners to make use of these techniques to reduce the presence of bias in AI deployment. We will discuss real-world examples and make use of reinforcing loops to describe the underlying phenomena of bias in AI. We then make a call to action for practitioners to make use of these techniques to reduce the presence of bias in AI deployment.