Diversity's Critical Role in AI and Innovation
Artificial intelligence and Machine Learning models are heavily reliant on the data that feed them. While AI can improve on human decision making; however, since data can be biased based on human decisions made in the past, AI output may inherit or even amplify biases. There are different solutions that could help mitigate bias, such as interrogating the data to better understand any inherent bias beforehand or conducting fairness tests to check if the model output may unfairly discriminate against protected groups. One way to provide additional perspectives and mitigate bias that perhaps we don't often talk about is having a more diverse, multi-disciplinary workforce who works in AI. Several sources estimate only 10% to 20% of those who work in AI are women, this percentage being stagnant over the years. Education, mentorship, role models, sponsorship, and recruitment practices can play critical roles to bring more diversity into AI
Jane leads a Data & Analytics team within our Innovation, Technology and Shared Services (ITSS) group. She has been with TD for five years and has led Analytics teams for customer facing contact centre and collections businesses. Prior to TD, Jane held increasingly senior roles in Marketing and Analytics in Telecommunications, Retail and Insurance.
Jane is the Co-Chair of Women in Data & Analytics at TD, and is also President of the Queen's University Smith Analytics & AI Alumni Club. Jane holds a Master of Management Analytics from Queen's University Smith Business School, an MBA from IMD in Switzerland, and HBA from the Ivey School of Business at Western University.