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The Five Biggest Blunders of Machine Learning (and How to Avoid Them)

April 9, 2019

The Five Biggest Blunders of Machine Learning (and How to Avoid Them)

If you don't want to get knocked over as the machine learning wave hits talent and recruiting, you'll need some hints on how to avoid some of the obvious mistakes you're sure to encounter.  Here are five we think you really need to know.

Inadequate Datasets    

The reason that machine learning is so incredibly powerful lies in processing power, and more recently in datasets. The quality of the information you receive from AI technology is only as good as what you put into it. Some businesses get ahead of themselves when it comes to AI implementation and sacrifice the quality of their data in the name of getting work done.

Mistakes like these can lead to tremendous failures when implementation rolls around. Take the time to form an understanding of what data you need and how to effectively collect and curate it it before machine learning capabilities are in your hands. This will help ensure that you're able to leverage the technology to your benefit rather than your detriment

Indiscriminately Selecting the Data You Use to Train Models    

In terms of importance, quality data will trump flashier algorithms any day of the week. 

Your modeling process should only involve data that's been relentlessly explored, assessed, labelled and cleaned before input. It's ideal to use different datasets for training and testing algorithm models. If you make the mistake of evaluating your model using the same data that trained it, the model can wind up overfit and all but useless.

Relying on Unexplainable Outcomes

Teams tend to get excited about the seemingly infinite possibilities that machine learning capabilities have to offer. When you're inexperienced in the process, machine learning seems like an answer to every single question you've ever had and every problem you've faced at work.

The fact of the matter is that AI isn't human-- that means that it can't produce results based on feelings and ideas. Instead, you'll need to get literal. 

When you're leveraging machine learning to make hiring and workplace decisions, it's imperative that you have a solid knowledge of the outcomes you're seeking. Abstract ideas are great for brainstorming moments around the desk with colleagues-- but leave them there. Go into machine learning projects with concise explanations for how your workplace will look after you address problems.

Using Algorithms Based on Biased Perspectives  

We all have different ideas about how we want to do work, who we want to do that work with, and what that work requires. While these varying opinions and ideologies can be helpful when teams work together towards a common goal, they aren't actually conducive to making logical decisions in the best interest of a company. 

The entire purpose of leveraging machine learning in the workplace is to remove human error from decision-making purposes. Bias is perhaps the greatest human error of all-- and when you rely on algorithms that are biased, you defeat the purpose of implementing machine learning technology at all.

Make sure that the parameters you're telling AI to use for decision-making are neutral, fair, and unbiased. There's no use in paying for special software to make educated decisions if you're going to create roadblocks that stand in the way. 

Neglecting the Need for Functional Expertise

All too often, offices will get their hands on tech with machine learning capabilities with the belief that workplace processes will become hands-off after implementation. This couldn't be further from the truth.

The trick to leveraging AI is to work in tandem with the technology-- not to work against or take a back seat to it. Machine learning software may be able to make more informed decisions than the average employee, but there's something you have that it doesn't: experience.

If something seems outright wrong with the way your technology is working or the suggestions it's offering up, don't hesitate to revisit those ideas (and your programming choices). Sometimes, AI is just wrong; at other times, you may need to tweak the inputs you're offering up for optimal results. 

At Swoop Talent, we're passionate about talent and recruiting success-- and we want to bring that passion into your office. Contact us today to learn more about machine learning-based hiring solutions, options for implementation, and the most reliable data to ensure your hiring process nets you the talent you need. Our team will be happy to work with you and create a roadmap for success with AI and machine learning in talent.

Photo by Debora Cardenas on Unsplash

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