Artificial Intelligence (AI) and Objective Research Development (part 01) - Ajith Rohan J.T.F.

 

Introduction - Between Precision and Perspective

In the sprawling landscape of human inquiry, artificial intelligence (AI) has emerged as both a tool and a provocateur, reshaping how we chase the elusive ideal of "objective" research. As of March 10, 2025, AI’s fingerprints are all over scientific discovery, data analysis, and even the philosophical underpinnings of what we call truth. But what does it mean for research to be objective when the hands guiding it—human or silicon—are steeped in their own biases, limits, and dreams? This is a story of promise, tension, and a little existential musing, perfect for anyone peering into the mirror of progress.

The Promise of AI in Research

AI’s strength lies in its ability to chew through mountains of data with an elevate speed and precision no human could match. Take drug development: algorithms now sift through molecular libraries, predicting interactions that once took years of lab grunt work. A 2024 study from MIT showed AI cutting discovery timelines for antibiotics by 40%, a feat that could save lives faster than ever. In physics, AI models crunch cosmic datasets, spotting patterns in galaxy clusters that hint at dark matter’s secrets—work that’s less about intuition and more about raw computational muscle. This feels objective, doesn’t it? Numbers don’t lie, and machines don’t care about prestige or tenure. AI can strip away the human tendency to see what we want to see, offering a cold, clear lens on reality.

The Bias Beneath the Code

AI isn’t a blank slate. It’s built by humans, trained on human data, and reflects human choices. If the dataset feeding an AI is skewed—say, medical trials favouring one demographic—the output inherits that tilt. A 2023 report on facial recognition showed error rates spiking for non-white faces, not because the AI “chose” to fail, but because its training mirrored historical neglect. Objectivity falters when the starting point is already a story of who mattered enough to be counted.

Then there’s the question of intent. Researchers wield AI like a scalpel, but they decide where to cut. An AI analysing climate models might prioritize economic impacts over ecological ones if that’s what the grant demands. The machine doesn’t care, but its masters do. This isn’t a flaw to fix—it’s a feature of any tool shaped by purpose. The dream of pure, detached research bumps up against the messy truth: even AI serves someone’s why.

Accelerating the Objective Chase

Still, AI pushes us closer to objectivity by outpacing our limits. It can run thousands of simulations, test hypotheses we’d never dream up, and spot correlations buried in noise. In 2025, a famous AI company’s own work has leaned into this, using AI to model complex systems—think planetary atmospheres or neural networks—with fewer assumptions baked in with the method of letting the machine question itself, tweaking variables to challenge its own conclusions. It’s not perfect, but it’s a step beyond the human ego’s blind spots (emotions and other subjective reactions).

The Human-AI Relation 

Here’s where it gets personal. Objective research isn’t just about data—it’s about what we do with it. AI can churn out facts, but humans still weave the narrative. Objectivity lives in the cracks between calculation and interpretation.

For researchers, AI is a partner, not a replacement. It’s the silent collaborator that says, “Check this,” while we decide, “Tell me more.” That dance keeps development honest—AI’s rigor tempers our leaps, and our curiosity nudges its focus. Together, they’ve pushed boundaries: cancer diagnostics, quantum computing, even art analysis.

Conclusion - objectivity is a horizon, and the subjectivity depends on “Art of Seeing”

So, is AI the key to objective research? Not quite. It’s a booster rocket, not the destination. It amplifies our ability to chase facts but can’t escape the shadow of who we are (human)—flawed, hopeful, and “endlessly subjective”. Maybe that’s the real lesson: objectivity isn’t a finish line; it’s a horizon. AI gets us closer, but the last step is ours to stumble through.