Is Artificial Intelligence Good or Evil?

How about neither. It’s just a tool.

AI in science isn’t good or evil. It’s just a tool.

Stanley Kubrick’s film 2001: A Space Odyssey opens with the words “The Dawn of Man.” (Spoiler alert if you haven’t seen it.) In the scene, a group of early humans struggles to survive in the African desert—scavenging food, fending off predators, and generally getting the short end of the evolutionary stick.

Then, one of them picks up a femur bone and discovers it’s great for things like smashing the skulls of proto-pigs. Suddenly, the tribe has food, safety, and a competitive edge. Moments later that same bone becomes a lethal weapon against a rival tribe.

Kubrick’s lesson: any tool powerful enough to help can also be used to harm.

Artificial intelligence is a lot like that bone. Not inherently good or evil—just very useful and powerful. And like any tool, how it’s used matters far more than what it is.

Sensationalism Sells, but Reality is More Complicated

Most headlines portray AI as either humanity’s salvation or its doom. It’s either the most important invention since the microprocessor or the end of civilization as we know it.

The truth, of course, is far less dramatic. AI is simply a very competent system for performing certain types of tasks, especially those involving scanning and structuring large volumes of distributed data. That’s what it’s good at.

What it’s not good at is judging the quality of that data, spotting flaws in its own logic, or determining when it’s strayed too far from the truth. We all know that AI can “hallucinate”, generate convincing but entirely false conclusions, and do so at scale, making the errors harder to detect.

None of this means AI is bad. It means AI is a tool; a tool that requires a competent operator to handle it.

The Real Question Isn’t “Is It True?”; It’s “Is It Trustworthy?”

AI can parse information at a speed and scale no human can match. But if we have to manually double-check every result, especially in something as complex as biomedical research, then what’s the point of using it?

So, when we’re using it to make critical decisions, how do we ensure AI’s output is trustworthy?

Do we use more AI to check the work of other AIs? Do we build non-AI systems to validate results? Do we rely on human experts to analyze every detail?

The answer is: all of the above.

Meet Tessa, the Trust Framework

At Siensmetrica, we’ve been developing a framework for trustworthiness we call “Tessa”.

Tessa is an anthropomorphized term for “TES”, short for Transparency, Explainability, and Significance. It is a framework designed to evaluate the underlying components of the research; not just what it says, but where the source materials came from, why it says what it does, and whether the conclusions it reaches are meaningful.

Unlike large language models that predict what comes next, Tessa performs a molecular-level inspection of data inputs. It looks at each data point used to generate the results and asks:

• Is the data transparent? Can we see what was used?
• Is the reasoning explainable? Do the authors show how they arrived at this conclusion?
• Is the data significant? Was it appropriate to include it in the analysis, and are the results useful?

Currently, Tessa’s framework analyzes 163 (and counting) distinct data vectors. The result isn’t a binary “right” or “wrong” judgment but a trust score from 0 to 100. The higher the score, the more confidence users can have in the data’s trustworthiness.

AI Can’t Replace Human Intelligence, It Requires It

When Tessa scores a piece of research highly, it doesn’t mean the answer is correct. It means the process behind the answer is strong and reliable, i.e., trustworthy. It gives researchers and practitioners a quick way to separate high-quality information from low-quality noise and focus their time where it matters most. But while AI can process oceans of information in seconds, it can’t replicate human judgment. And when it comes to science, medicine, and the decisions and policies made from it, judgment is everything.

The Bottom Line

AI isn’t inherently good or bad. Like that femur bone in 2001, it’s a tool and it can be used to help surface truth or harm by reinforcing falsehoods. The only way to separate one from the other is to build systems like Tessa that help us analyze the reliability of information and give us a metric for how trustworthy it is.

Because in a world where the available research is doubling every 10 weeks, identifying trustworthy information, not creating quick summaries, is the real breakthrough.