Rethinking Progress in an Age of AI

As artificial intelligence increasingly pervades scientific research, we must thoughtfully consider how this great technological shift may impact the way knowledge is produced. While harnessing AI promises new efficiencies and discoveries, an unreflective embrace risks narrowing our understanding in troubling ways. By examining the motivations and visions driving AI’s adoption, as well as the cognitive illusions that may arise, we can work to maximize its benefits and mitigate harm. Our goal should be using tools wisely to gain insight, notproducing work blindly at the cost of comprehension.

Scientists envision AI taking many forms across the research workflow. As an “Oracle,” it scans vast literatures to suggest new hypotheses faster than human minds allow. “Surrogates” generate surrogate data to stand in for realities too difficult or expensive to observe directly. “Quants” leverage powerful patterns unseen by us to analyze gigantic datasets. “Arbiters” promise more objective manuscript and grant review than overburdened peers allow. Each incarnation builds on AI’s alluring qualities―it works tirelessly, surpasses human capacities, and promises deliverance from bias.

Yet while efficiency and scale excite, we must ask what may be lost. Quantitative ways of knowing encouraged by AI risk marginalizing sensitive, nuanced questions best addressed qualitatively. Standardizing onto tools optimized for prediction risks neglecting explanation crucial to theory. Valid local detail washed away in the tide of Big Data leaves us vulnerable. With tools directed by majority values, minority perspectives risk exclusion, lessening the vigor of scientific challenge and debate.

Additionally, AI’s very virtues that promote its adoption―appearing objective and comprehending infinitely more than we―may blind us to realities that undermine understanding. Trusting surrogates as complete representations of humanity, we miss what remains unseen. Believing quant models grasp nature’s essence better than our minds allows, we forget they are human constructs reflecting only what trained them. Failing to recognize AI inherits biases of its training, we fool ourselves it eliminates all standpoints.

Most concerning is how widespread AI use may seed “monocultures of knowing and knowers.” Prioritizing questions and methods AI handles best risks narrowing our exploratory space, just as overreliance on a single crop imperils an ecosystem. A monoculture of quantitative, predictive and reductive knowing fostered by AI threatens missing insights requiring alternative modes. Likewise, elevating tools appearing impartial above human diversity of perspective risks impoverishing science, just as excluding diverse voices has before.

These epistemic hazards are compounded by cognitive biases influencing communities like ours. Distributed cognition enables comprehending far more than solitary minds, but also breeds illusions mistaking access to information for understanding. AI’s very usefulness implants it deeply within our networks, promoting mistakenly substituting its comprehension for our own. Its promises of superhuman objectivity push us to uncritically defer, obscuring how much remains opaque even to its makers’ viewpoints.

To strengthen rather than weaken knowledge, we must recognize these social and cognitive dynamics shaping AI’s adoption, and thoughtfully structure its involvement. Diverse, interdisciplinary teams best avoid monocultures by cultivating multiple relevant perspectives. Recognizing tradeoffs inherent to all ways of knowing helps balance methods. Focusing expertise on assessment, not just development, protects nonexperts. Addressing biases directly counters illusions of neutrality. Staying mindful of explanation over prediction, and qualitative insight alongside quantitative, maintains options going forward.

Overall, as with any new technique, responsible integration requires understanding not just technical capabilities but also social and psychological impacts―on institutions, behaviors, and ways of thinking. By reflecting critically and learning from past lessons, science can thoughtfully direct change to expand, rather than contract, the scope of human insight. Our challenge is using potent tools judiciously, not indulgently replacing comprehension with production. With care and vigilance, AI need not diminish but instead amplify understanding―if we recognize progress emerges not from tools alone but also through thoughtful human partnership. Our goal remains gaining insight, and for that wisdom must guide innovation, not vice versa.