This session is meant to encourage participants to consider their own biases (and the impact they may have on machine learning models), and to provide the tools needed to create more fair and just systems. We will begin with an interactive and human-focused discourse about bias: what it is, the forms it appears in, and how it permeates our society. Then we'll explore different types of bias within the context of machine learning, such as overfitting bias, algorithmic bias, and prejudice bias, using real-world stories and examples. Together we will examine the consequences of bias left unattended, including the potential for discrimination and unfair decision-making.

We will delve into the latest research and several approaches to mitigating bias, including data preprocessing, fair representation learning, and bias correction algorithms. We will also work through one example together to give participants a hands-on understanding of how to apply these techniques in practice. By understanding bias and taking the steps necessary to quell its effects, we can ensure that machine learning is used responsibly and ethically in the future, resulting in a world with more trustworthy technology.