Machine Learning Needs Care Too! Machine Learning Needs Care Too!
We agree, machine learning is a powerful technology. But before the technology can unleash its potential in your business, taking care of machine learning... Machine Learning Needs Care Too!

We agree, machine learning is a powerful technology. But before the technology can unleash its potential in your business, taking care of machine learning is your duty!

We have been hearing about tons of machine learning applications for quite some time now. But, machine learning algorithms fail to offer optimized results without careful supervision. Hence, taking care of machine learning is as important as implementing it in a business.

ISSUES WITH MACHINE LEARNING

There’s no doubt that no one will ever need a machine learning system that predicts the wrong outcomes. But the fact is that machine learning systems aren’t error-proof and will make mistakes. For instance, Gideon Mann and Cathy O’Neil, while talking about the application of machine learning in recruitment, have said in their HBR blog post that,

“Man-made algorithms are fallible and may inadvertently reinforce discrimination in hiring practices. Any HR manager using such a system needs to be aware of its limitations and have a plan for dealing with them. Algorithms are, in part, our opinions embedded in the code. They reflect human biases and prejudices that lead to machine learning mistakes and misinterpretations.”

The highlight of the above statement is that anyone using a machine learning system must be aware of its limitations too. No user should get so overwhelmed by the possibilities that arise with machine learning that she forgets about its limitations.

Now, allow us to break a machine learning system into:

  • active system
  • passive system

In a nutshell, active systems are those where humans control the operations. On the other hand, passive systems are those where machines control all the processes, with negligible human interference. As humans rule the active systems, there is a need for highly experienced and qualified analysts to take care of the machine learning system.

However, most of the companies lack employees who have the necessary expertise. Therefore, even with the emergence of forward-looking technologies, the traditional software like spreadsheets continue to dominate analytical studies. Also, overfitting is another major issue with machine learning where the system gets confused with the large datasets fed to it. As a result, the systems sometimes end up focusing on unnecessary data.

Passive model, on the other hand, creates another set of issues altogether. Machines trained to respond on their own can prove risky. For instance, imagine a situation where you are interacting with a chatbot, asking for some urgent help. Now, what will your reaction be if the bot replies – “Sorry, I understand your issue well, and I would love to help you, but I am still learning new words and commands with time.” You’d expect bette›r support here, isn’t it? Therefore, machine learning needs care too!

TAKING CARE OF MACHINE LEARNING

  • Carry out these simple steps before implementing machine learning in your organization to enhance productivity and to increase revenue.Understand the areas where machine learning algorithms can drive your business with maximum profits. After getting a comprehensive idea about it, managers can make the best use of machine learning technology in their organization.
  • Organizations must employ a transparent machine learning model, which enables the top-level management to keep track of all decisions.
  • Create a map that swaps the active and passive models, so that necessary support to users is always available.

Taking care of machine learning systems is complex yet straightforward. Focus on the list above before you implement machine learning in your business to easily implement machine learning in your business.


 

Original Source

Naveen Joshi

Naveen Joshi

Seasoned professional with more than 20 years of experience, with extensive experience in customizing open source products for cost optimizations of large scale IT deployment. Currently working on IoT solutions with Big Data Analytics