When I’m getting ready to reason with a man, I spend one-third of my time thinking about myself and what I am going to say and two-thirds thinking about him and what he is going to say. Abraham Lincoln (1809-1865)
If human beings had to reason with a machine, or more specifically, had to teach machines to reasonis Lincoln’s formula still relevant?
Framing the debate on the superiority of machine learning vs. domain expertise
But the real question lies not in exploring whether data scientists require domain knowledge to build expert systems, but whether the representation phase of data can be accurately achieved without involving domain experts. Domain experts are presumed to be far more capable of identifying, articulating, and demonstrating day-to-day process problems in business. As these experts can jolly well explain a research problem to peers, it is probably absurd to even consider that an expert system can be constructed without their involvement or guidance. The same should hold true in the case of superior algorithms required to create such systems.
An example from the field of Molecular Biology
Machine learning may be necessary to research the human cell, but it cannot be the starting point. Therefore, data scientists must collaborate with molecular biologists to understand the complexity of cell behavior under different conditions to analyze or process the findings of one’s research. The biologist is equipped with a priori knowledge, known as domain expertise that provides accurate insights for monitoring and analyzing the results of an experiment or a series of experiments.
The flip side of the argument: Is domain knowledge really necessary?
An example of a competition in a crowd-sourced environment
Let’s compare the relative strengths and weaknesses of domain experts and data scientists.
Machine Learning Experts/Data Scientists: Pros
- Can ask questions without understanding processes or tasks
- Can study data to discover repetitive patterns
- Can reconstruct process knowledge by studying data
- Can use data patterns to predict results
Machine Learning Experts/Data Scientists: Cons
- Cannot analyze the existing models of business processes accurately
- Have the potential to misuse models
- Lack depth of understanding of business functions
Domain Experts: PROs
- Can provide practical insights from past experience
- Can help refine a question with practical knowledge
- Can accurately shape or model tasks for analysis
- Can guide analytics in the right direction
- Can evaluate the effectiveness of a result
A domain expert’s strength lies in close observation of day-to-day process problems, while a data scientist’s strength is building generalized solutions in the form of algorithms by studying specific data patterns.
An example: Learning Management Systems (LMS)
A critical first step in developing a learning system starts with gathering information about potential learner’s competency in specific skills or tasks. The objective behind collecting this information is to estimate the scope of the learning system in terms of topic or task coverage. Quite often, the approach is back-to-front; the learning outcomes and performance tests are defined before creating the learning content.