LSE Online’s Post

AI is transforming our world. But as the relationship between humans and machines evolves, a new approach is needed to study its impacts, argues #DSIAffiliate Milena Tsvetkova. Read more about her collaborative research in a new article from the latest issue of #ResearchfortheWorld, the online social science magazine from The London School of Economics and Political Science (LSE). https://lnkd.in/eXZcVPt5

New Journey to Begin 🎓 📚 I am thrilled to announce that after finishing my Research Master in Economics and Business at the University of Groningen in the coming summer, I will be starting my PhD in Marketing from September at the Faculty of Economics and Business - University of Groningen. Together with my supervisor, Mehrad Moeini Jazani, we aim to explore the fascinating dynamics of consumer behavior in interactions with AI. #PhDinMarketing #ConsumerBehavior #AI

Excited to share my final milestone in my AI journey before the Capstone Project! 🥅 🏅 🚀 The project focuses on analyzing articles to predict their relevance to US Economics. It includes various text preprocessing steps and the development of predictive models for article relevance. #LearnApplyRepeat #TextPreprocessing #NaturalLanguageProcessing #Classification #SupervisedLearning #MyGreatLearning

Fantastic insights at the Queensland University of Technology Economics and Financial Society's Premiere event! The talks about the effect of Covid and Artificial Intelligence on our workplace were especially insightful. It's always great to hear from experts in the field and gain new perspectives on the ever-evolving business landscape. Thank you to the QUT Economics and Financial Society for hosting such a valuable event! #QUTEFS #Economics #Finance #AI #COVID19 #WorkplaceInsights

I'm excited for the panel discussion "Generative AI in health and healthcare, help or hindrance?" at The London School of Economics and Political Science (LSE). #healthtech #ai #digitalhealth #digitaltransformation

Learn more about AI and the work of the future in our upcoming live seminar with Professor David Autor, the Daniel (1972) and Gail Rubinfeld Professor in the MIT Department of Economics. https://lnkd.in/gru7Ducj

🔍 Critical Perspective in Research: A Necessary Check As researchers, it's easy to get deeply invested in our work, sometimes losing sight of the broader critical perspective. The project below serves as a timely reminder that even the most advanced and sophisticated models can be vulnerable to oversight—whether by cunning or naivety. Continuous self-reflection and scrutiny are vital in our field, ensuring that our findings stand the test of both rigor and real-world application. #ResearchExcellence #CriticalThinking #EconomicsResearch #AgriculturalEconomics #NaturalResourceEconomics #EnvironmentalEconomics #AcademicResearch #ModelValidation

I didn’t like this column very much. The thing about scientific breakthroughs is that they are always products of tons and tons of research done by people who came before as well as whoever gets to take credit. No one does it alone in any field, not even economics. Also, the boundary between research that is “purely abstract and useless in the real world” and stuff that is “very practical and welfare improving” is far blurrier than the author suggests, if it actually exists at all. Just because the “real world applicability” of the findings of some paper are not immediately obvious today doesn’t mean that nobody will ever dust that paper off sometime in the future and use it (among tons of other work by other people, some famous, some obscure, some in between) to start building a body of work that could one day lead to a Nobel prize. One of the main reasons why there isn’t enough basic scientific research getting done is because it’s not immediately commercializable. Economists who are doing cutting edge theoretical work that hardly anybody can comprehend, theoretical econometricians doing their own stuff. Without people like them the field would be far poorer than it is today. The stuff they do is the basic research of the field, and economists generally agree that basic scientific research is still valuable and worth subsidizing even if it doesn’t have immediate commercial applications because it helps push the technological frontier forward. I think that’s something worth recalling whenever screeds like these get published.

Economics is often considered to be a technocratic science, evidence-based, and politically neutral. This does not seem to be the whole story. Just like what we have learned about AI, seemingly neutral predictive algorithms can produce estimates biased towards the political preferences of careful researchers. In the Economic Journal, Zubin Jelveh, Suresh Naidu, and I published a study using machine learning from data that permitted us to train an algorithm to extract from the text of academic economic articles the political leanings of individual economists. We find that your slant will bias the prediction of the values of meaningful statistics, e.g., optimal tax rates, or employment effects under minimum wage policies. Our paper is now hotly debated on X. Have a look. b kogut

AI, & bias checking Below i repost a link to an article co-authored by Bruce Kogut, Columbia professor and a long ago colleague and friend (his wedding precipitated a trip to one of my ancestral homelands, Sweden, and various adventures of note--but not here!). Bruce and his co-authors use AI to document and explore what we know to be true in so many settings--but perhaps would prefer not to be true --namely, that our orientation/perspective affects how & what we 'see', including what we see in economic data. Given that, what form does it take in economics? Bruce and his co-authors explore that question and, perhaps more importantly, remind us that we need to learn how to learn and to discuss the connection between how we see and what we see... and to do so in the service of cleaning our lenses--relentlessly (a most valuable skill in and of itself) ... self delusion so often traveling in the company of inaccuracy and consequential error. thanks, Bruce. #bias #kogut #economics #AI