Abstract: 

The stories of bias in AI are everywhere: Amazon's recruiting tool, Apple's credit card limits, Google's facial recognition, and dozens more. The quick solution is just to blame the algorithm and its designers. However, as data scientists, its incumbent on us to understand the true source of the bias and improve the underlying process.
AI does not create bias alone; it exposes the latent bias present in the system it was designed to imitate. We need to reframe the conversation around bias in AI to instead identify it as the first step in building a more ethical system.
In this talk, we show how machine learning can make the implicit bias of a human institution explicit. Bias becomes diagnosable, correctable, and ultimately preventable in a way that cannot be replicated in human decision-making, which is opaque and difficult to change. Bias is not new, but AI represents a new toolset to measure and change it.
The goal is not only to provide you a theoretical understanding of bias, but a practical plan that you can start to implement right away. After all, it’s not whether or not you have bias in your institution, but how you plan to handle it.

Bio: 

Haniyeh is a data science researcher at DataRobot's Trusted AI team. Her research focuses on bias, privacy, stability, and ethics in AI and Machine Learning. She has a demonstrated history of implementing ML and AI in retail, finance, and IT companies with expertise in customer relation, human resources, and fraud detection. She has initiated a project to incorporate bias and fairness into DataRobot's products and is a thought leader in the area of bias in AI and AI ethics. Haniyeh holds a PhD in Astronomy and Astrophysics from the Rheinische Friedrich-Wilhelms-Universität Bonn.