Algorithmic Accountability: How Artificial Intelligence Is Confronting Science's Reproducibility Problem
A quiet but consequential transformation is underway in how researchers evaluate the reliability of published findings. Artificial intelligence and machine learning tools are increasingly being deployed to detect flawed methodologies and statistical anomalies that human reviewers routinely miss. Yet as these computational systems take on greater authority in validating scientific work, scholars must also reckon with their inherent limitations and the new categories of bias they introduce.