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If you’re a leader tasked with generating business and org. value through ML/AI and analytics, you’ve probably struggled with low user adoption. Making the tech gets easier, but getting users to use, and buyers to buy, remains difficult—but you’ve heard a ”data product” approach can help. Can it? My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I offer you a consulting designer’s perspective on why creating ML and analytics outputs isn’t enough to create business and UX outcomes. How can UX design and product management help you create innovative ML/AI and analytical data products? What exactly are data products—and how can data product management help you increase user adoption of ML/analytics—so that stakeholders can finally see the business value of your data? Every 2 weeks, I answer these questions via solo episodes and interviews with innovative chief data officers, data product management leaders, and top UX professionals. Hashtag: #ExperiencingData. PODCAST HOMEPAGE: Get 1-page summaries, text transcripts, and join my Insights mailing list: https://designingforanalytics.com/ed ABOUT THE HOST, BRIAN T. O’NEILL: https://designingforanalytics.com/bio/
Episodes
Tuesday Nov 03, 2020
Tuesday Nov 03, 2020
Chenda Bunkasem is head of machine learning at Undock, where she is focusing on using quantitative methods to influence ethical design. In this episode of Experiencing Data, Chenda and I explore her actual methods to designing ethical AI solutions as well as how she works with UX and product teams on ML solutions.
We covered:
- How data teams can actually design ethical ML models, after understanding if ML is the right approach to begin with
- How Chenda aligns her data science work with the desired UX, so that technical choices are always in support of the product and user instead of “what’s cool”
- An overview of Chenda’s role at Undock, where she works very closely with product and marketing teams, advising them on uses for machine learning
- How Chenda’s approaches to using AI may change when there are humans in the loop
- What NASA’s Technology Readiness Level (TRL) evaluation is, and how Chenda uses it in her machine learning work
- What ethical pillars are and how they relate to building AI solutions
- What the Delphi method is and how it relates to creating and user-testing ethical machine learning solutions
Quotes From Today’s Episode
“There's places where machine learning should be used and places where it doesn't necessarily have to be.” - Chenda
“The more interpretability, the better off you always are.” - Chenda
“The most advanced AI doesn't always have to be implemented. People usually skip past this, and they're looking for the best transformer or the most complex neural network. It's not the case. It’s about whether or not the product sticks and the product works alongside the user to aid whatever their endeavor is, or whatever the purpose of that product is. It can be very minimalist in that sense.” - Chenda
“First we bring domain experts together, and then we analyze the use case at hand, and whatever goes in the middle — the meat, between that — is usually decided through many iterations after meetings, and then after going out and doing some sort of user testing, or user research, coming back, etc.” - Chenra, explaining the Delphi method.
“First you're taking answers on someone's ethical pillars or a company's ethical pillars based off of their intuition, and then you're finding how that solution can work in a more engineering or systems-design fashion. “ - Chenda
“I'm kind of very curious about this area of prototyping, and figuring out how fast can we learn something about what the problem space is, and what is needed, prior to doing too much implementation work that we or the business don't want to rewind and throw out.” - Brian
“There are a lot of data projects that get created that end up not getting used at all.”- Brian
Links
Connect with Chenda on LinkedIn
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