Artificial intelligence (AI) and machine learning can deliver unprecedented value to the business. Unfortunately, fantastic findings often get lost in translation. From expressing metrics in unfamiliar terminology to presenting odd visualizations, today I see a massive gap when it comes to data literacy and storytelling skills for AI. In my InformationWeek article “Stop Talking Gobbledygook to the Business” and Art of AI Storytelling webinar, I further discussed these common challenges.

Learning AI Lingo

Accurately interpreting and explaining findings from machine learning is becoming a crucial skill to bridge language barriers. In February, Gartner shared new research titled “Fostering Data Literacy and Information as a Second Language: A Gartner Trend Insight Report”. They opened with a call to action for AI creators and consumers to “speak data” using a common language. World Economic Forum, McKinsey, and other industry leaders are expressing similar concerns.

Call to action for AI creators and consumers to “speak data” using a common language

Qlik launched a free educational program on data literacy basics last year. I’m seeing similar initiatives popping up now in other communities. Most recently a super talented peer of mine at DataRobot, Colin Priest, wrote a series of articles that were summarized in his ebook on understanding machine learning models. I highly recommend his phenomenal work to help you advance your own AI data literacy skills.

Colin's ebook

Building Trust in AI

In his ebook, Colin quotes thought leader Thomas Davenport as saying “Humans will want to know how … technologies came up with their decision or recommendation. If they can’t get into the black box, they won’t trust it as a colleague.” To trust AI, you minimally need the answers to a few questions.

  • How accurate is the model?
  • When is it most accurate and when is it not so accurate?
  • What process or pipeline did it follow?
  • Which data was important?
  • What patterns were found in the data?
  • Why did the AI make a particular decision?

Please stay tuned for more data literacy resources. If you are aware of any that I did not mention, please share them with me. Thanks!