When discussing youngsters' life outcomes, school is often the first place someone starts investigating. And yet school time is dwarfed by the amount of time kids spend with their parents or other adult caregivers. Recognizing this, developmental psychologist Ariel Kalil, the Daniel Levin Professor at the University of Chicago Harris School of Public Policy, focuses her child development research on parenting.

"My argument, in a nutshell," she tells interviewer David Edmonds in this Social Science Bites podcast, "is that we spend a lot of time investigating what happens to kids during the school hours and thinking of school as the site for child skill development, when in fact, it's the out-of-school time that dwarfs the amount of time that children will spend in school.'

And the academic kicker, she adds, is "we know much less about how to understand that time."

Kalil has been looking at questions around economic conditions, parents and child development for a long time. In 2003, for example, she was the first-ever recipient of the Society for Research in Child Development Award for Early Research Contributions, one of several awards or research opportunities centering on her work she has received. Kalil also directs the Center for Human Potential and Public Policy and co-directs the Behavioral Insights and Parenting Lab at Chicago.

One of the promises of artificial intelligence is that it will mimic, and perhaps even improve, on human thinking. One of those hoped-for improvements was that AI would not exhibit human biases. Turns out that in one area, AI can indeed mimic human thinking, and it's in that field of bias. As Harvard psychologist Mahzarin Banaji -- one of the creators of the widely used implicit bias test -- explains in this Social Science Bites podcast, AI platforms both mimic human bias and even amplify it.

In her second appearance on the podcast series, Banaji tells interviewer David Edmonds that even she was surprised how overtly bias shows up in AI results. She recalls her jaw dropping after she queried a large language model about what biases it might have, and it replied "I am a white male," and then how, a month later when queried the same thing it came back with a lengthy 'correct' answer about how it could be biased.

"[W]hat stunned me, and why I began to work on these LLMs, is because it became clear that the creators of these models were actually doing us a massive disservice by creating in these machines two kinds of thought: what the machine knows that it's learned, and now what the machine is going to say, which I'll just call LLM hypocrisy."

Banaji is the Richard Clarke Cabot Professor of Social Ethics in the Department of Psychology at Harvard, a position she has held since 2002. She is also the first Carol K. Pforzheimer Professor at the Radcliffe Institute for Advanced Study, and the George A. and Helen Dunham Cowan Chair in Human Dynamics at the Santa Fe Institute. A former president of the Association of Psychology Science (2010-11), she was named William James Fellow by the APS and is also a fellow of the American Academy of Political and Social Science, the Society for Experimental Psychologists, Society for Experimental Social Psychology, and the American Academy of Arts and Sciences.

The human brain works very hard behind the scenes even in the most mundane aspects of daily life, like enjoying a nice day or determining the meaning of chit-chat with a friend. Ferreting out the basis and structures of our brain's labor is the domain of Daniel Yon, a psychologist and neuroscientists and director of the Uncertainty Lab at Birkbeck, University of London.

In this Social Science Bites podcast, Yon - author of the 2025 book A Trick of the Mind: How the Brain Invents Your Reality -- details for interviewer David Edmonds why he feels that just as science itself represents a solid - but not "bullet-proof" way of interpreting the natural world, science also well describes how the brain itself does the same.

"I think that at the heart of what I think science and the brain share is this preoccupation with building theories and models based on the data that you've gathered and using those theories to make sense of the world around you. That's a very powerful way to make sense of things," he explains, before adding the caveat, "but it also means that once you start to build your theories and paradigms, they can become the filter and the lens through which everything else gets seen."

Yon's scholarship has earned him a number of honors, such as the Experimental Psychology Society's EPS Prize and the Janet Taylor Spence Award from the Association for Psychological Science. He has also been named a Rising Star by the Cognitive Neuroscience Society and received a mid-career fellowship by the British Academy.