Maintaining The Human Factor in Machine Learning

Scaling AI Marie Merveilleux du Vignaux

While some automation in machine learning (ML) models is productive and necessary, taking a human-centric approach to machine learning and AI is essential. Humans have to be part of the ML workflow, included in the feedback loop, and more. This blog post will outline the importance of the human element and walk you through some of the benefits of maintaining a healthy balance between human involvement, machine automation, and AI.

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Where You See Human Involvement in ML

Let's go through some of the basic highlights of where humans are especially critical in the machine learning (ML) process:

  • Putting in place checks and processes to keep the model accurate
  • Checking in with the business to ensure efficacy and common sense
  • Confirming the model is accurate and not just “correct”
  • Ensuring, reevaluating, retraining, and making improvements

Some of these steps can be automated and others cannot. Successful organizations are the ones that blend their approaches by putting humans in the loop at the right position to identify risks and make the necessary changes.

person playing chess

Using MLOps to Augment Human Intelligence

ML models are not, and should not be, positioned to take the place of human intelligence. Rather, organizations should be focusing on augmenting their own intelligence with that provided by machines. While computers can process more data than we could ever hope to — and provide us with an answer — we should use that answer as a guide for our own decisions, especially critical ones, rather than relying on the machine selecting the answer for us.

That is why, with MLOps, it’s important to first understand the boundaries between where we are comfortable with automation and where human supervision becomes appropriate. Once this is agreed on, the organization can choose the appropriate level of automation. Automation with MLOps can then help processes in multiple ways, such as by:

  • Increasing the volume of models that are pushed to production
  • Democratizing AI and enhancing collaboration between teams by streamlining and standardizing processes
  • Assisting data scientists for experiment tracking when looking for the best model
  • Aiding governance in highly regulated environments and enabling reproducibility of models
  • Supporting explainability in both machine and human components

The Human-in-the-Loop Methodology

Organizations must build a human decision making element and always ask themselves one main question: “Does this make sense?” Since models cannot feel, humans have to be the ones to question them and determine whether or not something feels like it works and makes sense. A model may be acting correctly, but a human element needs to be there to determine whether the results are accurate.

It is when human knowledge and machine learning come together that we are able to truly push the boundaries of data science capabilities. By using computers to process data and provide us with an output, we can then use those outputs to influence the most critical business decisions. With a human-in-the-loop methodology, organizations across all industries and use cases can effectively create organizational change with scalable AI systems.

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