Advanced Computing in the Age of AI | Tuesday, June 25, 2024

The Importance of Humanized Autonomous Decision-Making in AI 

Advanced automation technologies like artificial intelligence, coupled with data generated from the internet, smart devices and social networks, are making it easier than ever to off-load real-time decision making from humans to algorithms.

This is more than just recommending your next television show to binge, Instead, it is evidence that we are increasingly relying on machines to make critical business decisions in the moment involving billion stock-dollar trades, to decisions about industrial processes, systems operations and more.

Taking the human out of the equation may work for basic automation—such as a die-cutting press punching out thousands of identical circles or applications that automatically move data from one field to another. But for a wide range of other important decisions, we still have not determined how to program aspects such as humanity, ethics and values into machines.

This is where progress is still needed to keep the human element present in AI, which is imperative.

In a healthy data architecture, data is fed into an algorithm throughout its lifecycle, continually informing and developing decisions. The goal is to always ensure optimal results, even as factors change. But these algorithms are only as good as the data that is fed into them.

Taking a step back to make sure that there are people responsible for the accuracy, reliability, integrity and confidentiality of your data ensures that the human touch is always present throughout your autonomous decision making. This leads to automation that aligns with corporate values, ensures optimal outcomes and most importantly keeps the machines that generate them fair.

Here are three steps that businesses which rely on data-driven automation for critical decision-making can use to maintain the human touch in their decision making:

  1. Ensure Data Quality

As data moves through a system, getting accessed, updated and combined with other records, any issues or errors that exist in the initial entry will be horribly exacerbated. To prevent this, the very fabric of each company’s underlying data structure must embed human-led data quality checks. This must be done not just to detect and correct errors as the data is ingested, but also to constantly monitor the data as it is accessed and used. This will catch a wide array of potential hazards – from mistaken data entry and duplicate records to mislabeled fields – that can lead to catastrophic data breaches.

  1. Make Data Accessible to All

It is critical to ensure that the right data is accessible to all the right people and applications. If an algorithm processes only a fraction of the relevant data, it will produce erroneous or biased results. Consequently, the reports and analyses that rely on those results, as well as the decisions made on them, will be just as flawed. A healthy data architecture does not impose artificial caps on data consumption. Rather, it needs to ensure that all available and relevant data is fed into decision making computations. At the same time, it provides all users with visibility on that data.

  1. Prioritize Security and Compliance

An algorithm may not care whether a given data set or record contains personally identifiable information, but a data breach could be a catastrophe for a business and a horrifying ordeal for its customers. Every company needs to have clearly articulated, reliably documented, regularly updated and consistently enforced policies and protocols for securing sensitive data and ensuring regulatory compliance. And it is humans – not machines – that need to audit these policies on a regular schedule.

A Human Touch

Using human-led models that ensure data confidentiality in the real world is easy to imagine.

Consider a company that maintains two distinct data sets that contain information about the same group of people. The first set lists tens of thousands of full names. The second set contains a similar number of residential addresses and district schools.

The two datasets, when viewed separately, pose little threat to privacy. But the tables are turned when an automated algorithm is run to correlate them and produce valuable contact records. This could be completed in just a few seconds, while it would take uncountable manual hours for a human.

AI can be extremely helpful to take the work out of working with the data, but without proper human supervision of the results or how they are accessed and by whom, it can lead to unprecedented privacy and security breaches.

A data governance system that requires just enough human intervention to ensure data quality, while making data accessible to all at the same time it is prioritizing security and compliance, allows businesses and users to compensate and correct for the limitations of machines. Data quality, completeness and accessibility are already critical for today’s data-driven business decisions. As AI and automation become more pervasive in our day-to-day lives, these three aspects will become ever more important.

About the Author

Julinda Stefa of Talend

Julinda Stefa is a senior product manager for Talend, a global leader in cloud data integration and data integrity.  Formerly, she was an associate professor at the department of computer science at Sapienza University of Rome, Italy. She earned her doctorate and bachelor’s degrees in computer science from Sapienza.