A new study from The Ohio State University highlights the dangers of relying on personalized algorithms for learning. When algorithms curate content during educational tasks, participants without prior knowledge on a subject tend to only engage with a narrow range of information, leading to incorrect answers despite high confidence in their responses.
Researchers have long focused on algorithmic bias in relation to existing opinions. However, this study reveals that biases can form rapidly even in unfamiliar domains, distorting individuals’ understanding of reality. People’s trust in limited algorithm-provided information can lead to sweeping, often inaccurate generalizations.
To investigate these dynamics experimentally, the researchers engaged 346 online participants using a fictional learning task. Participants were presented with features of crystal-like aliens and interacted with them under two conditions: one where all features were visible, and another where a personalization algorithm determined which categories they would most likely revisit.
The study found that those who used the algorithm-driven approach tended to view fewer features, exhibited patterned selection, and performed poorly on tests. Notably, participants in the algorithm-driven group demonstrated greater confidence in their incorrect answers compared to their correct ones.
This research has significant implications for younger learners engaging with online content. As algorithms are designed to maximize content consumption, this could shape their understanding of the world. The study underscores a critical disconnect between content consumption and genuine learning, raising questions about the long-term effects on individuals and society.
Source: https://news.ssbcrack.com/research-indicates-algorithms-can-bias-learning-and-understanding