The primary source of bias in machine learning is not in the algorithms deployed, but rather the data used as input to build the predictive models. In this talk we will discuss why this is a huge problem and what to do about it. Different sources of bias will be identified along with possible solutions for remedying the situation when deploying machine learning. We will also speak about the importance of transparency when using machine learning to predict outcomes that impact critical decisions.
- Learn why most predictive models are biased.
- Learn about the sources of bias in predictive models.
- Learn how to reduce the negative impact of potential bias in predictive models.