Two drawbacks of machine learning algorithms

Legal managers need to be alert to marketing hype, which is markedly present in the scrum of “AI” for lawyers.  Machine learning can fall deep into that and be extolled as a powerful tool able to leap tall concepts at a single bound.  Well, to some, not quite so super.

One drawback (bit of kryptonite?) of machine learning is its black box nature.   “It is difficult to explain specifically why the system arrives at a particular conclusion, and to correct it if it is erroneous.” [italics in original]  The quote comes from KMWorld, Oct. 2016 at S19, by Daniel Mayer of Expert System Enterprise.   A neural net, for instance, conceals its inner analytical processes quite effectively and offers users crude parameters to tweak.

A second drawback that Mayer points out is the labor-intensiveness of machine learning.  Its application requires “large training sets that the need to be built and maintained over time to ensure quality results.”  True enough, but so do data sets that natural language processing (NLP) works on, which is his favored tool.  It may be true, however, that text requires less cleaning and maintenance than numeric data.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.