The Dangers of Biased Data in AI and Techniques for Avoiding It...

The hype is thankfully gone from AI, and the cold reality is setting in. Does that mean your company shouldn’t use an AI/ML solution? No, of course it should. But you must get realistic about the most essential part of your AI: the historical data you’ve collected and the data points you’ll continue or begin for your data. Equally important is the quality of that data.

85% of AI projects ultimately fail, Per Gartner

AI projects have failed because of data bias, costing businesses and organizations hundreds of thousands, if not millions of dollars. This doesn’t have to be the case if you plan your data as well as prepare for the results you wish to get out of your AI.

There are three main types of data bias in AI projects:

1. Selection Bias – This is where the data sample is not representative of the population your project is supposed to serve. This means you need to expand your data set.
2. Confirmation Bias – This happens when you expect your data set to have specific results, and you collect it in such a way that your data matches your preconceived notions when the reality might be different.
3. Historical Bias – Occurs when past bias examples are already in the data and are not rectified.

So, how can data bias be avoided? Let’s look at the data players…

• Programmer Bias - Accept that programmers are going to think like programmers. Of course, I don’t want to generalize, but programmers are going to believe that everyone understands code and tech. That’s the world they live in, and why wouldn’t everyone else think that? – Get your non-techy people to read over what your programmers are doing, and ensure they address how data is collected and what data is collected.
• Management Bias – Much like programmers thinking like programmers, managers also have their biases, so the data they may want to collect may not be the data that is truly needed. – Get your rank-and-file members to review the data requirements.
• Sales Bias – Bias in sales is bad enough, and way too many leads are prejudged. I’ve been in sales, and yes, I’m guilty, and if you’re in sales, you’ve done the same. – Make sure you have members who aren’t in sales and who are willing to look at your data collection holistically; do not judge if a lead is qualified or not.

Overall, as you plan out your AI project, ensure that your data is clean, unbiased and will serve your needs now and in the future. If this means you need outside help, factor that into your investing. A slightly higher cost is a better ROI than an AI project that fails.

Are you starting an AI project for your business or organization? Reach out to me and ask how my company can help.