The Human Element in Machine Learning
Machine Learning has delivered exponential benefits to society, far beyond the capabilities of the human mind—specifically in space, basic science and medical research. Now the advent of Cloud computing has brought massive computational power within the reach of small businesses.
This has expanded the application of ML into the realm of businesses, impacting individuals. Consequently, implications assume whole new dimensions—variables become unlimited and business imperatives are specific to situations. Designing algorithms require transparency, deep understanding of organizational goals, how output will be consumed and how user behaviour will be impacted.
This is where the human element come in—machines by themselves cannot function optimally unless they are guided, trained and manipulated by highly intelligent coders and subject matter experts. Even as the hype around ML continues, a crucial part of the conversation must focus on the role of people who develop computational models and others in the organization who must work collaboratively to create perfect result-oriented models. The leap from hype to reality rests on the successful creation of this cadre of professionals.
There is no framework, description or reference about the exact qualification for these roles. Not surprising because we are pushing frontiers—learning and experimenting as we innovate to know more with the help of technology.
ML professionals must know which models to use, which datasets will work, which metrics to continue and which ones to discard. They must have deep insights into business challenges and understanding about variables that impact probabilities and then refine models with the right data sets till the ideal combination is reached.
The focus on human input is crucial and urgent as ML adoption will not reach a tipping point unless algorithm-based business decision-making becomes a reality. The requires gaining confidence of end users with algorithmic solutions that answer why it solves a business problem. Developers must not only have complete understanding of business challenges and but must be keenly aware of the consequence if the solution fails, taking into account how individual biases can impact results.
For example, businesses must be able to predict how many customers walking into the outlet will actually spend. If the algorithm does not return high levels of accuracy, another logic must be applied. If the second logic works, then you must return to the old logic applying another dataset to understand how to improve its accuracy. By trying various permutations and combinations which take into account business scenarios, data scientists will help businesses arrive at optimized solutions.
Observing, iterating and improving models are significant efforts on which machines build intelligence to deliver exceptional outputs. While the excitement around machines is well-founded, the bedrock of success is the input that goes into machines designed by human intelligence, insights and specific goals.
For all the hype surrounding machine capabilities, the enormous computational powers will yield profitable results only when it is guided by superb logic rooted in outcome-based business reality.