When building Machine Learning / Data Science models in 2018, how often was it important that the model be humanly understandable/explainable?
Fig. 1: How often was it important that Machine Learning Model be Understandable
The poll also asked about employment type, and overall breakdown here was
Company or self, 69% Government/ non-profit, 5% Student, 15% Academia/ University 9% Other, 2%
Was there a difference in importance of understanding Machine Learning results based on employment type? Fig. 2 shows the results by employment type, excluding Other.
Fig. 2: How often was it important that Machine Learning Model be Understandable, by employment Color indicates importance: orange: “Always”, green: “Frequently”, light grey: “Rarely”, dark grey: “Never”.
We note that respondents working for company/self had the highest number of “frequently” answers - 51.6%, while students had the lowest - only 36.7%.
Academic researchers said “Always” more than any other group, suggesting that Machine Learning understanding is an active area of research.
However, overall, understandability was always or frequently important for all groups over 80% of the time. Not surprisingly, the only exception were students, for where it was a little less - only 76%.
The overall breakdown by region was
US/Canada, 36% Europe, 34% Asia, 18% Latin America, 6.0% Africa/Middle East, 3.7% Australia/NZ, 2.8%
We next examine the breakdown of answers for 4 regions with the most answers.
Is understanding of Machine Learning especially important in Europe, given that GDPR went into effect in there on May 25, 2018?
Fig. 3: How often it is important that Machine Learning Model be Understandable, by region
Surprisingly, the region with the highest level of concern for understandability is not Europe but US/Canada. Combining “Always” and “Frequently” answers, we see that US/Canada respondents had the highest concern, with 88.5%, followed by Europe: 81.9%, Asia: 81.0%, and Latin America: 78.2%. The number of respondents in the other 2 regions is too small for a statistical analysis.
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