How to Identify and Mitigate Organizational Biases and Why They Matter More than Unconscious Bias
Day 1Session Code: S1-C
When: March 27, 2023
Track: DEI Strategy
Presenter: Dr. Paolo Gaudiano, Aleria Research Corp (ARC)
Prerequisite: Familiarity with the meaning of unconscious bias and typical UBT training; Access to company data on retention, compensation, etc.; Have a leadership role or work closely with leadership to gain buy-in and green-light initiatives
While much focus is placed on unconscious bias training, most organizations fail to realize that many of their processes, best practices, and policies are biased or inadvertently allow biases to go unchecked.
This though-provoking session begins by introducing a formal framework to learn to recognize organizational biases, explaining what they are, and why they are much more likely to harm companies and negatively impact their DEI efforts than unconscious biases. The session then shows a step-by-step approach to identify organizational biases using retrospective, reactive, and proactive methods and offers guidance on how to mitigate the biases identified.
The session explains how to identify internal data sources that can help to uncover some of the biases and shows how to prioritize efforts based on a combination of severity of biases, breadth of impact, and availability of data. We will also discuss several tangible benefits of focusing on organizational biases to create an organization that is more inclusive, diverse, and equitable.
By the end of the workshop, you will feel very comfortable with the framework and will be able to apply it to any number of operational areas within your organization.
• Understand different types of organizational biases and how they differ from individual biases
• Acquire the skills you need to audit your company’s processes for organizational biases using the retrospective, reactive, and proactive methods
• Learn to link organizational biases with typical corporate KPIs, to better understand the ROI of mitigating organizational biases