Skeptical about the data and model congruity used for AI-derived recommendations?

As a senior executive or CIO, how can you assure yourself that Artificial intelligence (AI) or Machine Learning (ML)-derived recommendations are reasonable and flow logically from the project work that has been performed?

While you want to be supportive and encouraging of your team’s work, you don’t want to be misled inadvertently, and you want to confirm that the data science team hasn’t misled itself.

“Model effectiveness is measured by the depth and relevance of the training data employed,” said Dr. Mingxi Wu, VP of Engineering at TigerGraph, a leading vendor of graph database software. “I recommend to our clients to have some tuning mechanism in the spirit of reinforcement learning paradigms to adapt the model to fit the changing real-world data.”

Here are some high-level questions that you can ask the team about the data and model congruity. They’re designed to raise everyone’s assurance that the AI/ML recommendations are sound and can be confidently implemented even though you and everyone else know you’re not an expert. Start by selecting one question that you’re most concerned about and you’re most comfortable asking.

Data and AI/ML model congruity

The selected data and its AI/ML model must work well together. The confidence you can have in AI/ML-derived recommendations is highly dependent on the congruity between the two. Here are some related questions that will illuminate data and AI/ML model congruity:

  1. How do we know that the data you’re using to support the model is the correct or optimum data for the model?
  2. Considering the wide variety of data sources this project employed, how do we know that subject-matter expertise with the various data sources was sufficient within the project team?
  3. How did you engage subject-matter experts to weed out seemingly exciting correlations in the results that are already well understood and are therefore not valuable insights?
  4. How did you approach data and model governance to build assurance that the data quality is adequate, and the model is reliable?
  5. Can you explain the variance in model outputs between using training data and using real-world data?
  6. Can you explain the model outputs considering the data that you used?
  7. Were you able to compare model outputs using similar data from different data sources that cover the same variables?

Evaluating answers

Here’s how to evaluate the answers that you’ll receive to these questions from your data science team:

  1. If you receive blank stares, that means your question’s topic has not been addressed and needs more attention before the recommendations should be accepted. It will be necessary to add missing skills to the team or even replace the entire team.
  2. If you receive a lengthy answer filled with a lot of data science jargon or techno-babble, the topic has not been sufficiently addressed, or worse, your team may be missing critical skills required to deliver confident recommendations. Your confidence in the recommendations should decrease or even evaporate.
  3. If you receive a thoughtful answer that references uncertainties and risks associated with the recommendations, your confidence in the work should increase.
  4. If you receive a response that describes potential unanticipated consequences, your confidence in the recommendations should increase.
  5. If the answers you receive are supported by additional slides containing relevant numbers and charts, your confidence in the team should increase significantly.
  6. If the project team acknowledges that your question’s topic should receive more attention, your confidence in the team should increase. It will likely be necessary to allocate more resources, such as external data science consultants, to address the deficiency.

For a summary discussion of the topics you should consider as you seek to assure yourself that AI/ML recommendations are sound, please read this article: Skeptical about AI-derived recommendations? Here are some tips to get you started.

What ideas can you contribute to help senior executives assure themselves that the AI/ML-derived recommendations are reasonable and flow logically from the project work performed? Let us know in the comments below.

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Jim Love, Chief Content Officer, IT World Canada
Yogi Schulz
Yogi Schulz
Yogi Schulz has over 40 years of Information Technology experience in various industries. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy, and systems project management.

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