If the controversy around the A.I.-based fake news on channels like Facebook and Twitter is any example, contemporary technology doesn't always tell you the truth. That should make you hit pause for a second, because in the age of Big Data, the bulk of what businesses do now relies on circuits and programs. A 2018 report from New Vantage Partners, for instance, found that 97 percent of surveyed businesses are investing in A.I., Big Data, and data analytics initiatives. 

Still, the general tendency is for people to trust that whatever output a technology gives us is honest or accurate, as shown by a study by researchers from Texas Tech University.

Lots of calculators, plenty of trust.

For the study, the research team gave participants calculators. Some participants got calculators that functioned normally. Other participants got calculators that were programed to provide wrong answers. The participants didn't have to use the calculator if they didn't want to, but most did (hey, why not accept a little convenience, right?).

The researchers measured how suspicious the participants were of the calculators' answers by whether they reported a problem, overrode the incorrect answers, or rechecked the answers they got.

The researchers discovered that participants who had better math skills demonstrated a little more suspicion about their devices, as you might expect. But overall, most people didn't bat an eye at the problem responses until the calculators gave answers that clearly were way off.

Balance is key.

Now, it's not feasible for you (or anyone else) to check every single piece of data technology puts in front of you. There's simply too much of it.

But as Monica Whitty, chair in human factors in cyber-security for the University of Melbourne, points out, the study shows a need for leaders and general users to have a good balance between technological trust and skepticism. This is required not only because cybercriminals can intentionally target you, but also simply because virtually any technology can experience glitches, design faults, compatibility problems, and basic wear and tear that can influence performance and signal the need for updates.

Part of the trust-skepticism balance can involve training that sufficiently teaches people how to behave with technology, be discerning, and check facts. For example, users can learn how to cross reference answers across tools or multiple databases, identify personal biases that might cloud analyses, talk to industry experts for insights, or use simple strategies like evaluating URLs, checking for verification symbols, and performing reverse image searches.

But balance also can mean intentionally developing and implementing systems of verification that work alongside our primary technologies. At the most basic level, this should include some manual review, which can ensure that you stay mentally sharp even as you protect yourself. But it also can mean tapping other tools and fighting tech with tech, such as initially vetting information by automatically running it through multiple programs or using a variety of algorithms.

Not every business decision you make will rely on information from a database or device. But many of them will, and achieving good results and making good calls thus requires you to acknowledge the psychological bias you likely have to believe whatever your technologies offer. You also need to clarify this bias to your team and ensure that everyone is on the same page in terms of your technological framework and policies. A little prudence and critical thinking, after all, is never the cloak of a fool.