Article

The automation paradox: why humans must remain in the loop even as AI advances

Let the computers and robots and all the other machines take away the drudgery, the chores, and the boring repetitive work. After all, boring, repetitive stuff is what computers are good at. We humans can do the complicated stuff that requires our sophistication, experience, and good judgment. So the more we automate, the better — right?

Automating your data processes is a great idea if the data is consistent, always complete, and arrives regularly, but if your data is of varying quality in terms of format, completeness, or delivery it's more like building an assembly line when you rely on parts that are only delivered once in a full moon. To manage irregular processes like these, we only automate some of the parts – as much as we can of course – but ensure that there's always a human in the loop to verify the results. Picture a four eyes approval process, or just a final "OK" button to accept the results.

This is a good safeguard to have, and works well most of the time, but here's the problem: mission-critical data systems don't run on "works well most of the time" — you need correct results, all the time. When systems provide good results often, very often, it generates trust, and the humans in the loop tend to let their guard down: it's always worked before, why wouldn't it work now, right? If you've never seen the system make a mistake, why would you go through all the trouble of manually checking each record for errors, every single time? But it only takes one such error to lead to million-dollar losses. This situation, where users start to overly rely on machine-generated suggestions or decisions and become more confident in a system than is really warranted, is something we call automation bias.

Large language models (LLMs) are impressive. but they often produce output that looks correct while being totally wrong — a phenomenon known as hallucination. if you rely on them blindly, you’re just layering more automation bias into your systems

We think AI has huge potential — but only if it's used with care. Finding a way to make use of AI and the new potential of large language models is essential to any forward-looking organisation, but it should be done safely and responsibly. At Mesoica we automate your alternatives investment data workflows — without falling into the trap of automation bias. Would you like to know more, or have talk to us about this? Get in touch!

Mesoica’s data quality platform is specifically designed to help LPs and GPs manage their data efficiently. By using our platform, you can seamlessly collect, validate, and monitor data, enhancing communication and collaboration. Our scalable solution adapts to your organization's growing data needs, providing peace of mind and enabling you to become a truly data-driven organization. Start your journey today by visiting our website or contacting us to learn more about how Mesoica can empower your firm to continuously improve data quality.