Friday, April 16, 2021
Humans are biased creatures. Therefore, data around human behavior will reflect these biases. As AI solutions are by design built to pick up on complex patterns, biased data left unchecked will propagate through the AI solution, potentially leading to unintended and inequitable outcomes. As more and more decisions with increasing impact are automated through AI, this problem becomes increasingly front and center. However, there are steps that can be taken to mitigate bias throughout the process – beginning with the way the initial question is framed all the way to data and outcome evaluation. This talk will focus on ways to address unintended consequences and ethical considerations throughout the data science process from project inception to operationalization using a combination of thoughtful investigation and machine learning-based techniques.
The importance of data quality is often undermined in the rush to acquire and process as much data as possible. Data science and plethora of opportunities it presents including decision making, compliance and confidence is only as good as the data itself. This is a talk that deconstructs the good, bad and ugly of data quality.