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With AI determining many system-led decisions, from who gets a job, a loan or business opportunity, the need to ensure diversity and inclusivity in the creation of algorithmic rules becomes more important.
In our fifth episode, we have Glenda Crisp, Chief Data Officer at National Australia Bank. She started her career in tech as a programmer in a bank in Canada and then went on to get an MBA. Before being recruited by NAB, she worked at TD Bank in Toronto. As the Chief Data Officer at National Australia Bank, both her business and technological side come together.
Diversity is a key topic at NAB. They have 6 pillars of inclusion that they target, which are: Gender Balance, NABility and Neurodiversity, NAB Pride, Cultural Inclusion, African Inclusion and Indigenous. When it comes to bias you have to actively look for it and manage it, given that AI learns from data, historical data is a result of human decisions and humans are biased. NAB has an ethical framework for the use of data, machine learning and AI.
Quotes:
Thanks to our sponsors:
Shine Solutions Group
Talent Insights
SAS
Women in Analytics (WIA) Network
Growing Data
Read the full episode summary here: #SheLeads Ep 5
Enjoy the fifth episode of our #SheLeads Series!
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With AI determining many system-led decisions, from who gets a job, a loan or business opportunity, the need to ensure diversity and inclusivity in the creation of algorithmic rules becomes more important.
In our fifth episode, we have Glenda Crisp, Chief Data Officer at National Australia Bank. She started her career in tech as a programmer in a bank in Canada and then went on to get an MBA. Before being recruited by NAB, she worked at TD Bank in Toronto. As the Chief Data Officer at National Australia Bank, both her business and technological side come together.
Diversity is a key topic at NAB. They have 6 pillars of inclusion that they target, which are: Gender Balance, NABility and Neurodiversity, NAB Pride, Cultural Inclusion, African Inclusion and Indigenous. When it comes to bias you have to actively look for it and manage it, given that AI learns from data, historical data is a result of human decisions and humans are biased. NAB has an ethical framework for the use of data, machine learning and AI.
Quotes:
Thanks to our sponsors:
Shine Solutions Group
Talent Insights
SAS
Women in Analytics (WIA) Network
Growing Data
Read the full episode summary here: #SheLeads Ep 5
Enjoy the fifth episode of our #SheLeads Series!