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New research by the psychologists Lucía Vicente and Helena Matute from Deusto University in Bilbao, Spain, provides evidence that people can inherit artificial intelligence biases (systematic errors in AI outputs) in their decisions. The astonishing results achieved by artificial intelligence systems, which can, for example, hold a conversation as a human does, have given this technology an image of high reliability. More and more professional fields are implementing AI-based tools to support the decision-making of specialists to minimise errors in their decisions. However, this technology is not without risks due to biases in AI results. We must consider that the data used to train AI models reflects past human decisions. If this data hides patterns of systematic errors, the AI algorithm will learn and reproduce these errors. Indeed, extensive evidence indicates that AI systems do inherit and amplify human biases. The most relevant finding of Vicente and Matute’s research is that the opposite effect may also occur: that humans inherit AI biases. That is, not only would AI inherit its biases from human data, but people could also inherit those biases from AI, with the risk of getting trapped in a dangerous loop. Scientific Reports publishes the results of Vicente and Matute’s research. In the series of three experiments conducted by these researchers, volunteers performed a medical diagnosis task. A group of the participants were assisted by a biased AI system (it exhibited a systematic error) during this task, while the control group were unassisted. The AI, the medical diagnosis task, and the disease were fictitious. The whole setting was a simulation to avoid interference with real situations. The participants assisted by the biased AI system made the same type of errors as the AI, while the control group did not make these mistakes. Thus, AI recommendations influenced participant’s decisions. Yet the most significant finding of the research was that, after interaction with the AI system, those volunteers continued to mimic its systematic error when they switched to performing the diagnosis task unaided. In other words, participants who were first assisted by the biased AI replicated its bias in a context without this support, thus showing an inherited bias. This effect was not observed for the participants in the control group, who performed the task unaided from the beginning. These results show that biased information by an artificial intelligence model can have a perdurable negative impact on human decisions. The finding of an inheritance of AI bias effect points to the need for further psychological and multidisciplinary research on AI-human interaction. Furthermore, evidence-based regulation is also needed to guarantee fair and ethical AI, considering not only the AI technical features but also the psychological aspects of the IA and human collaboration.

Large language models like ChatGPT efficiently provide users with information about various topics, presenting a potential substitute for searching the web and asking people for help online. But since users interact privately with the model, these models may drastically reduce the amount of publicly available human-generated data and knowledge resources. This substitution can present a significant problem in securing training data for future models. In this work, we investigate how the release of ChatGPT changed human-generated open data on the web by analyzing the activity on Stack Overflow, the leading online Q\&A platform for computer programming. We find that relative to its Russian and Chinese counterparts, where access to ChatGPT is limited, and to similar forums for mathematics, where ChatGPT is less capable, activity on Stack Overflow significantly decreased. A difference-in-differences model estimates a 16\% decrease in weekly posts on Stack Overflow. This effect increases in magnitude over time, and is larger for posts related to the most widely used programming languages. Posts made after ChatGPT get similar voting scores than before, suggesting that ChatGPT is not merely displacing duplicate or low-quality content. These results suggest that more users are adopting large language models to answer questions and they are better substitutes for Stack Overflow for languages for which they have more training data. Using models like ChatGPT may be more efficient for solving certain programming problems, but its widespread adoption and the resulting shift away from public exchange on the web will limit the open data people and models can learn from in the future.

This week, I attended a round table discussion at the House of Commons with politicians and experts from across the education sector to feed into UK policy on AI in Higher Education. Unsurprisingly, one of the key areas of concern and discussion was the impact of AI on academic integrity: in a world where AI can write an essay, what does AI mean for what we assess and how we assess it? And how do we respond in the short term? In this week’s blog post I’ll summarise the discussion and share what we agreed would be the most likely new model of assessment in HE in the post-AI world.

Aquí están seis consejos esenciales publicados por el MIT Technology Review sobre cómo comenzar a dar a tu hijo una educación en IA. Como me pareció interesante hice esta traducción que sirve también para los docentes.