In an episode of the satirical British political sitcom Yes, Prime Minister from the 1980s, Sir Humphrey Appleby once explained to Bernard Woolley (two of the characters) how it is possible to get contradictory polling results by asking a series of leading questions beforehand. The polling discussed in the episode concerns whether the public is for or against the reintroduction of national service. Recently, the leading questions outlined by Appleby were put to the public by the market research and polling giant Ipsos, the findings of which have been made public to raise awareness of the fact that people can be misled by means of a such questions [1]. The actual experiment conducted by Ipsos is explained on their web site: "Ipsos interviewed a representative quota sample of 2,158 adults aged 16-75 in Great Britain. Half saw the'Sample A' questions, reflecting a positive view about national service. Half saw'Sample B', reflecting a negative view."

This study consists of qualitative empirical research, conducted through exploratory tests with two different Large Language Models (LLMs) chatbots: ChatGPT and Gemini. The methodological procedure involved exploratory tests based on prompts designed with a probability question. The "Linda Problem", widely recognized in cognitive psychology, was used as a basis to create the tests, along with the development of a new problem specifically for this experiment, the "Mary Problem". The object of analysis is the dataset with the outputs provided by each chatbot interaction. The purpose of the analysis is to verify whether the chatbots mainly employ logical reasoning that aligns with probability theory or if they are more frequently affected by the stereotypical textual descriptions in the prompts. The findings provide insights about the approach each chatbot employs in handling logic and textual constructions, suggesting that, while the analyzed chatbots perform satisfactorily on a well-known probabilistic problem, they exhibit significantly lower performance on new tests that require direct application of probabilistic logic.

Since the seminal paper by Tversky and Kahneman, the conjunction fallacy has been the subject of multiple debates and become a fundamental challenge for cognitive theories in decision-making. In this article, we take a rather uncommon perspective on this phenomenon. Instead of trying to explain the nature or causes of the conjunction fallacy (intensional definition), we analyze its range of factual possibilities (extensional definition). We show that the majority of research on the conjunction fallacy, according to our sample of experiments reviewed which covers literature between 1983 and 2016, has focused on a narrow part of the a priori factual possibilities, implying that explanations of the conjunction fallacy are fundamentally biased by the short scope of possibilities explored. The latter is a rather curious aspect of the research evolution in the conjunction fallacy considering that the very nature of it is motivated by extensional considerations.

The field of Artificial Intelligence (AI) is focusing on creating automated decision-making (ADM) systems that operate as close as possible to human-like intelligence. This effort has pushed AI researchers into exploring cognitive fields like psychology. The work of Daniel Kahneman and the late Amos Tversky on biased human decision-making, including the study of the conjunction fallacy, has experienced a second revival because of this. Under the conjunction fallacy a human decision-maker will go against basic probability laws and rank as more likely a conjunction over one of its parts. It has been proven overtime through a set of experiments with the Linda Problem being the most famous one. Although this interdisciplinary effort is welcomed, we fear that AI researchers ignore the driving force behind the conjunction fallacy as captured by the Linda Problem: the fact that Linda must be stereotypically described as a woman. In this paper we revisit the Linda Problem and formulate it as a fairness problem. In doing so we introduce perception as a parameter of interest through the structural causal perception framework. Using an illustrative decision-making example, we showcase the proposed conceptual framework and its potential impact for developing fair ADM systems.

"Will AI take over jobs?" is a very controversial and interesting question that has been around for many years, and yet it will be questioned even more in the upcoming years, as artificial intelligence rapidly develops. Some people believe that AI will create more jobs than it destroys. They argue that as AI automates certain tasks, it will free up workers to do other, more creative or complex tasks. For instance, a bank teller whose job is automated by AI may be able to use their freed-up time to provide financial planning services to customers. Similarly, a manufacturing worker whose job is taken over by a robot may be able to move into maintenance or quality control. Others believe that AI will destroy more jobs than it creates.

But before delving into'behind-the-scenes' of US banking industry meeting ATM, let's turn back time for a second -- on March 27th, 1998, in the New Tech 1998 conference in Denver, Colorado. Here, Neil Postman, a prominent American cultural critic and professor at New York University, gave a keynote lecture. Professor Postman has been a long-time scholar of how new technologies relate to human society, and the book'Amusing Ourselves to Death', a 1985 book that rose to stardom, shows how television technology is destroying public discourse and turning everything into entertainment. I think it has something to do with how we feel about the impact of today's media and how our lives exposed to it are deteriorating. Since this book, Professor Postman has strongly criticized the tendency to respond to all social problems through technical solutions.

The World Economic Forum says technologies like artificial intelligence (AI) will displace 75 million jobs by 2022 but will also create 133 million new roles. To prepare workers for these new jobs, organizations will have to provide significant resources for upskilling their workforces. And employees will need to take personal responsibility for their career development in a context of rapid technological change. How can HR professionals prepare employees and organizations for a present and future where AI is increasingly working with humans to drive business outcomes? "HR professionals need to begin by shifting their mindsets about AI," said Jeff Schwartz, a principal with Deloitte Consulting.

The World Economic Forum says technologies like artificial intelligence (AI) will displace 75 million jobs by 2022 but will also create 133 million new roles. To prepare workers for these new jobs, organizations will have to provide significant resources for upskilling their workforces. And employees will need to take personal responsibility for their career development in a context of rapid technological change.How can HR professionals prepare employees and organizations for a present and future where AI is increasingly working with humans to drive business outcomes? The'Right' Mindset about AI"HR professionals need to begin by shifting their mindsets about AI," said Jeff Schwartz, a principal with Deloitte Consulting. "It's not about dividing work between people and AI--it's not an either/or proposition--but about how to create new value in new ways."

In the age of artificial intelligence, predicting which jobs will fall to automation is as much about what machines can do as it is about what they can't. More than half of all jobs in America -- both blue and white-collar -- are resistant to automation, according to an acclaimed study published in 2013 by two Oxford University researchers. Co-author Carl Benedikt Frey, who directs Oxford's Technology and Employment program, broke down three areas where human intelligence still beats artificial intelligence: perception and manipulation, social intelligence; and creativity. Each type has what Frey calls a "bottleneck," which slows the pace at which certain workforces can be automated. The premise is simple: Technology won't replace human workers if it can't do the job.

This op-ed originally appeared in Real Clear Markets on October 30, 2019. Robots have been coming for and successfully eliminating jobs for a long time: ask the iceman, elevator operator, or travel agent (if you can still find one). But what happens when the robots come for your job, succeed, and your job remains? Sounds strange but consider the conflicting reality of bank tellers and the robot designed to replace them: the Automated Teller Machine (ATM). The first ATM appeared in America in 1969.