What is the role of people in machine learning?

by Daniel J. Power

Many tools exist for modeling data using machine learning (ML). The tools have been automated and the desire for "easy to use" software is now a reality for complex statistical analysis. Many steps have been automated. A human analyst is still needed for at least 4 important steps: 1) problem finding and framing, 2) determining the question(s) that need to be answered, 3) identifying the relevant data to potentially answer the questions, and 4) understanding the results from machine learning analysis. What is involved in each of these tasks?

Problem finding means discovering a problem that needs to be resolved. Problem framing is the process of describing and interpreting a problem to arrive at a problem statement. Set realistic expectations for the ML project.

Identifying relevant questions to answer -- If you don't ask the right questions, your data won't give you relevant answers.

Data Identification -- Data is the fuel that drives high-scale innovation with AI. Ironically, many organizations struggle to use their data effectively because of the overwhelming number of data sources that are available, and no clear way to identify the most trusted sources of data.

Data Preparation -- Every machine learning algorithm works differently and has different data requirements. Get the right data, take your time. For example, some algorithms require numeric features and some do not.

Understanding results -- Over the past few years, some automated machine learning tools and AI have made massive strides in predictive power, but at the price of complexity. It is not enough for a model to score well on accuracy and speed you also have to trust the answers it is giving. And in regulated industries, you must justify the model to a regulator. DataRobot explains model decisions in a human-interpretable manner, showing which features have the greatest impact on the accuracy of each model and the patterns fitted for each feature. Provide explanations of the why of the results. Managers should expect transparency not black boxes.

People must take responsibility for ML use and results. People should look for bias, monitor the fairness of the results, and work to determine the validity of the model. A human analyst selects data for a learning system and then observes and interprets the outputs of the system to make choices for any subsequent iterations. Make sure the human consumers of ML are respected in the process and as they use the results.

Last update: 2020-06-17 06:25
Author: Daniel Power

Print this record Print this record
Show this as PDF file Show this as PDF file

Please rate this entry:

Average rating: 0 from 5 (0 Votes )

completely useless 1 2 3 4 5 most valuable

You cannot comment on this entry

DSS Home |  About Us |  Contact Us |  Site Index |  Subscribe | What's New
Please Tell Your Friends about DSSResources.COMCopyright © 1995-2015 by D. J. Power (see his home page).
DSSResources.COMsm is maintained by Daniel J. Power. Please contact him at with questions. See disclaimer and privacy statement.


powered by phpMyFAQ 1.5.3