Algorithms, Bias, and Decision-Making: Podcast Insights from IBM Leaders
Lisa Seacat DeLuca
To prevent biased algorithms you need to make sure you have unbiased training data on hand. You also need algorithms to be developed by a diverse set of people.
–Lisa Seacat DeLuca, Director of Offering Management and Distinguished Engineer for IBM Watson Internet of Things
Amber Grewal
How do we select vendors and what do we focus on? We must ask the question: what data did AI learn from, how did you arrive at the algorithm you’re using? Are you validating? What is the purpose of the data? How is the AI learning?
–Amber Grewal, Vice President of Global Talent Acquisition at IBM
Show Notes
Recently Amazon announced it had shut down a talent-finding algorithm built by its internal team. Why? Because it was perpetuating bias against women at the tech giant, which is unacceptable in today’s work environment.
With so many bots, algorithms and other tools being used to automate our work and personal lives, it’s important to think about how this affects each of us. Is there bias in the algorithms that drive our decisions? If so, how do we mitigate that?
In today’s episode, Ben talks with two IBM leaders with diverse perspectives on AI, bias, and more. Lisa Seacat DeLuca and Amber Grewal both join the show to talk about how they see AI benefiting the workplace but also how to watch for bias and prevent it from creeping into the finished product.
Links to the references made by Lisa and Amber on the podcast:
- IBM releases world’s largest annotation dataset for studying bias in facial analysis: https://www.ibm.com/blogs/research/2018/06/ai-facial-analytics/
- Test out IBM Watson Candidate Assistant on the IBM Career Page: https://attractive.mybluemix.net/welcome
To learn more, be sure to check out the following resources from IBM:
Website: https://www.ibm.com/talent-management
Twitter: @IBMWatsonTalent
LinkedIn: https://www.linkedin.com/showcase/watsontalent
What are your thoughts? Can algorithms be trained, or will they always be biased to some degree? What can other firms learn from IBM’s trailblazing approach?
Ben Eubanks is the Chief Research Officer at Lighthouse Research & Advisory. He is an author, speaker, and researcher with a passion for telling stories and making complex topics easy to understand.
His latest book Talent Scarcity answers the question every business leader has asked in recent years: “Where are all the people, and how do we get them back to work?” It shares practical and strategic recruiting and retention ideas and case studies for every employer.
His first book, Artificial Intelligence for HR, is the world’s most-cited resource on AI applications for hiring, development, and employee experience.
Ben has more than 10 years of experience both as an HR/recruiting executive as well as a researcher on workplace topics. His work is practical, relevant, and valued by practitioners from F100 firms to SMB organizations across the globe.
He has spoken to tens of thousands of HR professionals across the globe and enjoys sharing about technology, talent practices, and more. His speaking credits include the SHRM Annual Conference, Seminarium International, PeopleMatters Dubai and India, and over 100 other notable events.