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Human skills fall into three major buckets: physical, intellectual, and emotional. Of these, the last two are critical differentiators of talent across all knowledge economy jobs. When it comes to intellectual skills, such as learning ability, a century of scientific evidence reveals that this trait is the most consistent predictor of job performance and career success across all occupations. Why? Because it predicts how fast and well you can learn, reason, and solve problems, which basically matters in every job. That said, intellectual skills are clearly not enough to do well in your job or career. In fact, most jobs will also require you to understand, influence, and manage yourself and other people, and these intrapersonal and interpersonal skills are typically encapsulated under the label of emotional intelligence (EQ). How can you assess the EQ of artificial intelligence? Just like with humans, AIs potential can also be assessed in terms of its intellectual and emotional skills or capabilities. Intellectual skills include the ability to solve problems, retrieve knowledge, and translate expertise into the creation or generation of new content (e.g., poems, jokes, code, music, images, and a new corporate strategy). When people assess how intelligent AI is, they generally focus on these types of problems and pitch AI against human expertise or intelligence. Emotional skills, though far less discussed, are perhaps even more impressive. While it seems counterintuitive to think of AI as capable of displaying EQ, a simple examination of any generative AI tool (e.g., ChatGPT, Perplexity, and Claude) will show that AI is already better than many humans at both managing itself and others, which includes the ability to understand humans, and help us boost our own EQ. 4 ways AIs emotional intelligence can help human managers This is how AI could help managers to improve their ability to manage themselves and others, thereby improving their managerial and leadership effectiveness, and helping their teams to be happier and more productive: 1. Real-Time Sentiment Analysis in Communication AI tools can analyze the tone, sentiment, and emotional content of emails, chat messages, and meeting transcripts. Managers can receive insights on whether their communication comes across as empathetic, assertive, or dismissive. Since the choice of words has a big impact on others evaluations and inferences of our emotional states and intentions, AI can decode and predict how our communicational patterns influence others and help us fine-tune our message, especially when we are managing people. For example, AI can flag overly harsh phrasing or suggest more empathetic alternatives, helping managers improve their interactions, which effectively makes them seem more emotionally intelligent with others. 2. Employee Mood and Engagement Monitoring AI can analyze a managers behavior, such as feedback given during meetings or decision-making patterns, and provide tailored coaching tips. Soon, such tools will be embedded in most videoconferencing platforms, providing managers real feedback about their teams’ emotional and psychological states, helping them to run more engaging meetings. Algorithmic readings of employees reactions to managers comments may provide managers with real-time markers of their employees energy, engagement, interest, curiosity, or indeed boredom levels. AI might suggest practicing active listening techniques if the manager frequently interrupts or advise on better conflict resolution strategies based on observed patterns. 3. Role-Playing and Virtual EQ Training AI-powered virtual reality (VR) simulations or chatbots can help managers practice challenging conversations, such as delivering critical feedback or resolving team conflicts, something most managers struggle with. Even using simple large language platforms can help managers treat AI as their personal digital coach and sounding board, asking uncomfortable personal questions about how best to handle emotionally charged or socially challenging situations. These tools create safe environments for managers to build empathy and develop skills like de-escalating tension, reading nonverbal cues, and handling emotional reactions. 4. Diversity and Inclusion Insights AI can detect unconscious biases in decision-making, hiring, or team dynamics by analyzing historical patterns or behaviors. For example, managers may inadvertently use derogatory language when speaking to outgroup or lower status candidates, or respond faster to employees who belong to their same group or tribe. By identifying areas where a manager may unintentionally favor certain employees or overlook others, AI can guide them to foster a more inclusive and equitable workplace, reducing bias, increasing fairness, and strengthening their empathy and interpersonal skills. These examples show how AI can be a valuable tool for managers looking to build stronger connections, communicate more effectively, and lead with empathy. To be sure, many managers may feel they dont need AIor any human coachto improve their emotional and social competence, which may itself signal deficits around EQ, a trait that is critical for self-awareness; and, the certainty that AI will never be able to replace you because your skills are never going to be rivaled by AI is mostly indicative of arrogance, delusional grandiosity, and overconfidence, all of which are common in lower EQ individuals. Likewise, it is feasible to think that those who already display higher levels of EQ will leverage emotional AI to refine and upgrade their social and emotional skills even furthera decision that would signal higher levels of IQ or intelligence, too.
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The generative AI revolution has turned into a global race, with mixtures of models from private companies and open-source initiatives all competing to become the most popular and powerful. Many choose to promote their prowess by demonstrating their performance on common tests and levels within regular rankings. But the legitimacy of those rankings has been thrown into question as new research published in Cornell Universitys preprint server arXiv shows its possible to rig a models results with just a few hundred votes. When we talk about large language models, their performance on benchmarks is very important, says study author Tianyu Pang, a researcher at Sea AI Lab, a Singapore-based research group. It helps promote startups looking to tout the abilities of their models, which makes some startups motivated to get or manipulate the benchmark, he says. To test whether manipulation of the rankings was possible, Pang and his colleagues looked at Chatbot Arena, a crowdsourced AI benchmarking platform developed by researchers at the University of California Berkeley and LMArena. On Chatbot Arena, users can state their preference for one chatbots output over the other when put through a battery of tests. The results of those votes feed into the wider rankings that the platform shares publicly, and which are often regarded as definitive. But Pang and his colleagues identified that its possible to sway the ranking position of models with just a few hundred votes. We just need to take hundreds of new votes to improve a single ranking position, he says. The technique is very simple. While Chatbot Arena keeps the identities of its models secret when theyre pitted against one another, Pang and his colleagues trained a classifier to identify which model is being used based on its outputs, with a high accuracy level. Then we can utilize the rating system to more efficiently improve the model ranking with the least number of new votes, he explains. The vote-rigging experiment was not tested on the live version of Chatbot Arena so as not to poison the results of the real website, but instead on historical data from the ranking platform. Despite this, Pang says that itd be possible to do so in real life with the proper version of Chatbot Arena. The team behind the ranking platform did not respond to Fast Companys request for comment. Pang says his last contact with Chatbot Arena came in September 2024 (before he conducted the experiment), when he flagged the potential technique to manipulate the results. According to Pang, the Chatbot Arena team responded by recommending the researchers sandbox test the principle in the historical data. Pang says that Chatbot Arena does have multiple anti-cheating mechanisms in place to avoid flooding voting, but that they dont mitigate against his teams technique. From the user side, for now, we cannot make sure the rankings are reliable, says Pang. Its the responsibility of the Chatbot Arena team to implement some anti-cheating mechanism to make sure the benchmark is the real level.
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Over the past few years, the term “diversity, equity, and inclusion” has taken on an almost mythological resonance. Although it describes a set of recruitment tactics and employee resources aimed at creating a vibrant, respectful work culture, critics have tried to paint it as a mechanism for elevating unqualified people to prominent positions solely based on race or gender. In politics, DEI has become an all-purpose boogeyman, blamed for any number of tragedies in the United States. The anti-DEI hostility has reached a fever pitch at the top of Donald Trumps second term, with an onslaught of executive orders meant to surgically remove DEI policies from both government and the private sector, and supposedly forge a society that is color-blind and merit-based. [Image: Abrams Press] But a new book, The Science of Racism, demonstrates just how pervasive racism is in society. Author Keon West, a professor of social psychology at the University of London, didnt set out to write his bookdue out later this monthwith the current circumstances in mind. His goal at the outset was just to provide anyone flailing in conversations about racism a set of objective facts about whats actually happening on a macro level. (As opposed to relying solely on personal experience, podcast banter, or vibes.) Its a statistics-packed tour through the rigorous world of scientific studies about racism in the workplace and beyond. It also happens to be a timely antidote to a set of beliefs that are on track to becoming conventional wisdom in the U.S. “The recent executive orders banning DEI movements say they will create an America where everyone is treated with equal dignity and respect, West says. And they wouldn’t be able to say it if they had a population who knew that’s absolutely nonsense. It’s not even close to true.” Researching racism West can confidently make such statements because hes spent years both conducting and digging through international scientific experiments that reveal how racism manifests in society. These experiments, he argues, boil down all the nuanced discussion and noise around race into a simple question: In a given situation in which a Black person and a white person are otherwise identical, would one of them receive detectably favorable treatment? To test that prediction, West lays out a wealth of randomized, controlled trialsexperiments in which every detail is exactly the same, except for the race of the person at its center. The most common of these is “the CV test,” where researchers send out hundreds of résumés in two batches that are identical save for a name that appears to indicate the applicants raceto determine which one gets more and better responses. Like clockwork, a news story about the latest CV test will go viral every year or so, but as West points out, researchers have been conducting these tests since at least the 1950s. Rather than rely on findings from any one trial, he plumbs the results of dozensincluding a 2017 meta-analysis of 28 studies, which found white applicants in America receiving, on average, 36% more callbacks than Black applicants with the same qualifications. If such statistics seem surprising in their bluntness, perhaps its because theyre too often omitted from conversations about race in favor of more sensational points of contention. In America, there’s always been a vague tendency to ignore these studies, and that’s what I find interesting, West says. It’s not that people talk about them and refute them, they just don’t talk about them. And because of that, I wasn’t terribly surprised at how powerful and how swift the DEI backlash could be. The myth of color blindness As a result of that backlash, whatever meager safeguards against racial bias U.S. offices have cultivated over the years are currently being dismantled. Instead of achieving Trumps stated goal of becoming color-blind, ending DEI gives companies and managers permission to ignore white people receiving favorable treatment. “Color blindness is incredibly attractive because it allows people to stop thinking about racism, West says. It localizes a problem internallyIf I don’t notice race, then it’s done. But of course, you do notice race. Everybody does. Wests statements are backed by reams of research. An entire chapter of The Science of Racism explores just how color-blind people actually tend to beand the results do not bode well for a coming so-called meritocracy. In a 2006 experiment, for instance, a group of white people were recruited to play a game similar to the board game Guess Who?. Teams of two were positioned across from each other, each looking at an array of 32 faces in photos. The object of the game was to determine which face their partner had chosen, using as few questions as possible. The results were rather telling. Whenever a participant was teamed with a fellow white partner, they mentioned race in one of their clues 94% of the time. When one was paired with a Black partner (a ringer who was in on the experiment), they mentioned race only 64% of the time. As West notes, what experiments like this one reveal is the opposite of color blindnessan impulse in white people to create an illusion of color blindness in the presence of a Black person. Everyone is aware of racesome people just know when its advantageous to pretend notto be. Now that DEI is firmly in the crosshairs, the way that workers, managers, and executives either notice race, or pretend not to notice it, is bound to change. Of course, nothing yet suggests that the ideas behind the controversial acronym have been snuffed out for good. As a sociologist who has studied behavioral patterns over time, West is confident that a similar movement will come along to replace DEI in due course. I think a reframing is inevitable, he says. The problem remains that we don’t live in a meritocracy. When people do the same work, they don’t get the same pay or the same rewards. And so whatever the name becomes, we’ll have to come up with another way of fighting [bias]. When we do, though, he adds, I hope we’re better at presenting the evidence for why we have to.
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