todoELE

The use of generative AI tools on campus is an excellent opportunity for technology and other leaders to provide guidance to students, faculty, and staff about how to navigate these new technological waters. In April 2023, we were involved in a panel with students at College Unbound. The conversation —"Generative AI and Higher Education: Disruption, Opportunities, and Challenges"— offered many different highlights, and the students brought rich thoughts, provocative considerations, and smart ideas, reinforcing the fact that discussions around what to do about generative AI (or about anything else, for that matter) are enhanced when students are involved. Toward the end of the panel conversation, Stan asked the students what they thought could be done to help faculty, students, and staff navigate the rise of AI. Essentially, he was curious to hear about the roles that technology and other leaders could fulfill. After thinking about their answers and engaging in further reflection, we came up with ten suggestions for how to step up and in to the generative AI discussion in higher education.

Me reúno con Sutskever, cofundador y director científico de OpenAI, en las oficinas de su empresa en una anodina calle del Mission District de San Francisco (California, EE UU). El objetivo es que comparta sus previsiones sobre el futuro de una tecnología mundialmente conocida, y en cuyo desarrollo ha tenido mucho que ver. También quiero saber qué cree que será lo próximo en llega y, en particular, por qué la construcción de la próxima generación de los modelos generativos insignia de OpenAI ya no es el centro de su trabajo. En lugar de construir el próximo GPT o el creador de imágenes DALL-E, Sutskever cuenta que su nueva prioridad es averiguar cómo impedir que una superinteligencia artificial, una hipotética tecnología futura que ve llegar con la previsión de un verdadero creyente, se rebele.

Seguro que ya habrás escuchado hablar de Midjourney y probablemente sepas que es una de las nuevas herramientas de inteligencia artificial que más impacto está teniendo en la manera en que podemos crear materiales visuales. Mediante órdenes de texto, Midjourney puede generar todo tipo de imágenes, desde ilustraciones abstractas hasta representaciones realistas de personas, objetos y escenas. Como se ve, esta tecnología ofrece un gran potencial para que los profesores de idiomas creemos materiales más atractivos y relevantes para nuestras clases de español.

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.