Truist (formerly SunTrust and BB&T) warmly invited me as a speaker for their Inclusion in Data – Educational Series. Over three sessions, I shared with Truist team members across their Enterprise Data Office and Inclusion & Diversity Office:
How to identify algorithmic biases in Machine Learning (ML) & Artificial Intelligence (AI), manifesting as public value failures
The Participation, Access, Inclusion and Representation (PAIR) principles framework and how it could mitigate the impact of bias in data
How to deploy data science in ways that reflect the values of the communities we aim to serve
Tracy Daniels, Truist Chief Data Officer, and I talked about the role of diversity, equity and inclusion in driving data innovation outcomes, implementing the PAIR principles, being more intentional in data use and so much more!
Tracy is a rockstar and it was such a pleasure to listen to her insights. She currently leads the Truist team that is dedicated to enterprise capabilities of data including data strategy, governance, delivery for Client, Master and Reference Data, Analytics and Data Science workbench and the Business Intelligence platforms. Daniels joined Truist (Heritage SunTrust) from Bank of America in August 2018. She has over 25 years of banking and technology experience leading high performing technology portfolio, development, infrastructure and global operations organizations.
Check out this fireside chat recap from Anqi Zou, one of the Truist team members -
Yesterday I had the honor and pleasure of moderating an intimate fireside chat between our guest speaker, educator and entrepreneur Dr. Brandeis Marshall and Truist Chief Data OfficerTracy Daniels as the final part of our Truist "Inclusion in Data" educational series, with Dr. Marshall.
The two influential #WomenInTech, from academia and the public sector respectively, conversed about Truist's vision for Data & Analytics, how Dr. Marshall's PAIR (Participation, Access, Inclusion, Representation) Principles framework can be applied throughout our organization, and how every teammate can be involved in realizing the power of data, with a mindset in Diversity, Equity, and Inclusion(DEI).
A few key questions I will be sure to ask myself in the future…
❓How can we make data more representative of the people it serves?
❓Who is creating the algorithms, and what bias may they have?
❓Who is NOT at the table and how do we invite them to the table?