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The more qualified you are today, the harder it is to get hired. This is not a guess. Its a documented, scientific reality. A recent study published in the Journal of Personality and Social Psychology found that when job candidates were perceived as high-capability, highly experienced, highly credentialed, or simply more advanced than what a role required, they were less likely to be hired than lower-capability applicants, even when all other factors were equal. The researchers behind this study discovered something most hiring managers would never admit: candidates who appear too good for a job are viewed with suspicion. Not because of any specific flaw, but because of what they might do. They might leave too soon. They might expect too much compensation. They might act superior. They might disrupt the hierarchy. Or, they might just get bored and leave. So, employers hedge. They take the path of least resistance. They pass on the most capable candidates, not because they doubt their skills, but because they fear the candidates motives. Increasingly, the overqualified label is used to avoid confronting deeper forms of bias against age, against education, or against those who they think may not fit into a companys hierarchy. These concerns are more emotional than rational, rooted in fear, insecurity, and a desire for safety, calm, and steadiness. If youve been in the job market for a while and you have a long résumé, seniority, and lots of education behind you, youve felt this firsthand. Youve applied to roles that match your background perfectly and heard nothing. Its not in your head. The system is flagging you as a problem. Fortunately, this bias can be overcome. Rewriting the story The same study showed that high-capability candidates can get hired if they know how to rewrite the story that employers are telling themselves. The researchers found that when highly capable applicants took three specific actions, the hiring bias against them disappeared. Not reduced, eliminated. These specific actions include: 1) High commitment to the company and role, 2) Organizational alignment (culture and values), and 3) Hunger for the job at hand, not just any job. Overall, the biggest fear hiring managers have about high-capability candidates is that theyre secretly holding out for something better. Of course, many are. They apply broadly. They keep doors open. They mention that theyre entertaining other opportunities during interviews. And thats exactly what sinks them. Like their search for the right culture fit, employers these days arent just hiring for skills, theyre hiring commitment. If they believe youll accept another offer or back out after an offer is extended, they wont take the risk. Period. The study mentioned previously found that even the most qualified candidates were viewed more positively and were more likely to be hired when they showed high levels of commitment to both the company and the position. Not generic interest. Not professional courtesy. Real, observable, targeted commitment. What to do So how do you show that? You do it three ways: preparation, positioning, and language. All three work together to shift the employers perception of you from flight risk to first choice. Hiring managers can tell when a candidate has done their homework, and for experienced professionals, preparation matters even more. You cant rely on your résumé to do the convincing. You have to show them that you didnt just apply because the job matched a few keywords; you applied because you chose their company for a specific reason. Many overqualified candidates unintentionally undermine their own commitment by saying things like, I already have a lot of experience in this area. Or plainly, Ive done this before. Or self-centeredly, This is a good fit for my background. None of those statements signals loyalty. They signal neutrality at best. They say, I can do this job, not I want this job. Lead with what’s next To keep from accidentally positioning yourself as someone whos just applying to collect a paycheck, you need to stop leading with what youve done and start leading with what you want to do next. That next thing? Make it clear that its this role. Theyre not asking to evaluate your ambitions. Theyre asking to evaluate your loyalty. What they want to hear is simple: I see myself here. Doing what the company needs. Evolving with the team. Staying, contributing, and growing. They want language that says, This is not a temporary stop. This is where I plan to stay. Long-term commitment is what builds trust. Its what gets you hired in a system that assumes people like yousomeone experienced, overqualified, and resourcefulwill walk away the minute something shinier comes along. The current hiring systems are built to minimize perceived risk. And right now, highly capable and credentialed job candidates look risky. Not because of what theyve done, but because of what employers assume theyll do next. If this sounds like you and you want to change it, you have to make new assumptions easier to believe. This isnt about playing small. Its about showing commitment, not ambivalence. Collaboration, not superiority. Focus, not distraction. Removing the risk label requires you to own your experience and your intentions, at the same time.
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New York City Mayor Zohran Mamdani faced his first snowstorm as mayor over the weekend wearing a trio of jackets that had his new job title embroidered on the chest and sleeve. One was custom with a message written on the inside collar and typography on the front pulled from New York’s past. Contrary to what you might assume, being elected mayor of New York doesn’t automatically get you access to a wardrobe of customized city agency jackets with “Mayor” embroidered on the outside hanging in the closet for you at Gracie Mansion. Those have to be given or made. [Photo: Adam Gray/Bloomberg/Getty Images] Two of the jackets he wore were given to him: a green fleece from the New York City Department of Sanitation (DSNY), and a black windbreaker from the New York City Emergency Management Department (NYCEM). A third, black, custom Carhartt jacket was personalized at the Brooklyn embroidery shop Arena Embroidery. [Photo: Michael Appleton/Mayoral Photography Office] The custom jacket features “The City of New York” written out in long-limbed serifs originally found on old municipal stationery letterhead from the 1980s and ’90s. The wordmark appears in white on the front right chest. Written inside of the collar, hidden from view of the cameras, is the phrase “No Problem Too Big, No Task Too Small.” View this post on Instagram The typographic style of the “The City of New York” mark is vintage, but it’s also back in vogue. Noah Neary, a senior adviser to Mamdani’s wife, Rama Duwaji, designed the mark, and the style can be seen on items like “New York or Nowhere” brand totes, or even on an “Eric Adams Raised My Rent” shirt from Mamdani’s mayoral campaign. For elected officials, these officially embroidered jackets have become the unofficial uniform at public events when Mother Nature strikes. Surveying fire damage last year in California, for example, President Donald Trump wore a windbreaker with the presidential seal on the front and California Gov. Gavin Newsom wore a quarter-zip with a bear, referencing the state flag. For Mamdani, his jackets signaled common cause with the city’s workers during a deadly storm. Political natural disaster wardrobe choices can easily veer into cosplay, like Republican lawmakers who dress like they’re going to a war zone when they’re just going to Texas. And simply wearing the right clothes to an event is not foolproof. What people remember about Trump’s visit to Puerto Rico after Hurricane Maria in 2017 wasn’t his jacket, but the image of him tossing paper towels and the delay of billions of dollars worth of aid. [Photo: Kara McCurdy/Mayoral Photography Office] Dressing more casually, though, does serve as an important form of visual communication when storms, fires, earthquakes, or other threats arise. You don’t show up to a disaster zone in a suit and tie. For Mamdani, his jackets showed solidarity with a city, its workers, and its citizens during his first snowstorm in office with a custom nod to city history.
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Have you ever had the experience of rereading a sentence multiple times only to realize you still dont understand it? As taught to scores of incoming college freshmen, when you realize youre spinning your wheels, its time to change your approach. This process, becoming aware of something not working and then changing what youre doing, is the essence of metacognition, or thinking about thinking. Its your brain monitoring its own thinking, recognizing a problem, and controlling or adjusting your approach. In fact, metacognition is fundamental to human intelligence and, until recently, has been understudied in artificial intelligence systems. My colleagues Charles Courchaine, Hefei Qiu, Joshua Iacoboni, and I are working to change that. Weve developed a mathematical framework designed to allow generative AI systems, specifically large language models like ChatGPT or Claude, to monitor and regulate their own internal cognitive processes. In some sense, you can think of it as giving generative AI an inner monologue, a way to assess its own confidence, detect confusion, and decide when to think harder about a problem. Why machines need self-awareness Todays generative AI systems are remarkably capable but fundamentally unaware. They generate responses without genuinely knowing how confident or confused their response might be, whether it contains conflicting information, or whether a problem deserves extra attention. This limitation becomes critical when generative AIs inability to recognize its own uncertainty can have serious consequences, particularly in high-stakes applications such as medical diagnosis, financial advice, and autonomous vehicle decision-making. For example, consider a medical generative AI system analyzing symptoms. It might confidently suggest a diagnosis without any mechanism to recognize situations where it might be more appropriate to pause and reflect, like These symptoms contradict each other or This is unusual, I should think more carefully. Developing such a capacity would require metacognition, which involves both the ability to monitor ones own reasoning through self-awareness and to control the response through self-regulation. Inspired by neurobiology, our framework aims to give generative AI a semblance of these capabilities by using what we call a metacognitive state vector, which is essentially a quantified measure of the generative AIs internal cognitive state across five dimensions. 5 dimensions of machine self-awareness One way to think about these five dimensions is to imagine giving a generative AI system five different sensors for its own thinking. Emotional awareness, to help it track emotionally charged content, which might be important for preventing harmful outputs. Correctness evaluation, which measures how confident the large language model is about the validity of its response. Experience matching, where it checks whether the situation resembles something it has previously encountered. Conflict detection, so it can identify contradictory information requiring resolution. Problem importance, to help it assess stakes and urgency to prioritize resources. We quantify each of these concepts within an overall mathematical framework to create the metacognitive state vector and use it to control ensembles of large language models. In essence, the metacognitive state vector converts a large language models qualitative self-assessments into quantitative signals that it can use to control its responses. For example, when a large language models confidence in a response drops below a certain threshold, or the conflicts in the response exceed some acceptable levels, it might shift from fast, intuitive processing to slow, deliberative reasoning. This is analogous to what psychologists call System 1 and System 2 thinking in humans Conducting an orchestra Imagine a large language model ensemble as an orchestra where each musician an individual large language model comes in at certain times based on the cues received from the conductor. The metacognitive state vector acts as the conductors awareness, constantly monitoring whether the orchestra is in harmony, whether someone is out of tune, or whether a particularly difficult passage requires extra attention. When performing a familiar, well-rehearsed piece, like a simple folk melody, the orchestra easily plays in quick, efficient unison with minimal coordination needed. This is the System 1 mode. Each musician knows their part, the harmonies are straightforward, and the ensemble operates almost automatically. But when the orchestra encounters a complex jazz composition with conflicting time signatures, dissonant harmonies, or sections requiring improvisation, the musicians need greater coordination. The conductor directs the musicians to shift roles: Some become section leaders, others provide rhythmic anchoring, and soloists emerge for specific passages. This is the kind of system were hoping to create in a computational context by implementing our framework, orchestrating ensembles of large language models. The metacognitive state vector informs a control system that acts as the conductor, telling it to switch modes to System 2. It can then tell each large language model to assume different rolesfor example, critic or expertand coordinate their complex interactions based on the metacognitive assessment of the situation. Impact and transparency The implications extend far beyond making generative AI slightly smarter. In health care, a metacognitive generative AI system could recognize when symptoms dont match typical patterns and escalate the problem to human experts rather than risking misdiagnosis. In education, it could adapt teaching strategies when it detects student confusion. In content moderation, it could identify nuanced situations requiring human judgment rather than applying rigid rules. Perhaps most importantly, our framework makes generative AI decision-making more transparent.Instead of a black box that simply produces answers, we get systems that can explain their confidence levels, identify their uncertainties, and show why they chose particular reasoning strategies. This interpretability and explainability is crucial for building trust in AI systems, especially in regulated industries or safety-critical applications. The road ahead Our framework does not give machines consciousness or true self-awareness in the human sense. Instead, our hope is to provide a computational architecture for allocating resources and improving responses that also serves as a first step toward more sophisticated approaches for full artificial metacognition. The next phase in our work involves validating the framework with extensive testing, measuring how metacognitive monitoring improves performance across diverse tasks, and extending the framework to start reasoning about reasoning, or metareasoning. Were particularly interested in scenarios where recognizing uncertainty is crucial, such as in medical diagnoses, legal reasoning, and generating scientific hypotheses. Our ultimate vision is generative AI systems that dont just process information but understand their cognitive limitations and strengths. This means systems that know when to be confident and when to be cautious, when to think fast and when to slow down, and when theyre qualified to answer and when they should defer to others. Ricky J. Sethi is a professor of computer science at Fitchburg State University and Worcester Polytechnic Institute. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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