Tech Hiring and Neurodiversity: Escaping AI Bias Into Human Prejudice

The software engineer had prepared for weeks. They'd studied algorithms, practised coding problems, reviewed the company's tech stack. What they couldn't prepare for was the fluorescent lighting that felt like needles in their skull, the unexpected background chatter from an open office that fragmented their thoughts, and the overwhelming cognitive demand of writing code whilst simultaneously explaining their reasoning to three strangers who were judging their every word. Twenty minutes into the pair programming interview, they froze. Not because they didn't know the answer. Because their autistic brain, overwhelmed by sensory chaos and social performance demands, simply shut down.

They didn't get the job. The feedback cited “communication issues” and “inability to think under pressure.” What the feedback didn't mention: their GitHub profile showed five years of elegant, well-documented code. Their portfolio included contributions to major open-source projects. In their actual work environment, with noise-cancelling headphones and asynchronous communication, they excelled. But the interview measured none of that.

When Amazon scrapped its AI recruiting tool in 2018 after discovering it systematically discriminated against women, the tech industry collectively shuddered. The algorithm, trained on a decade of predominantly male hiring decisions, had learned to penalise CVs containing the word “women's” and downgrade graduates from all-women's colleges. Engineers attempted repairs, but couldn't guarantee neutrality. The project died, and with it, a cautionary tale was born.

Since then, companies have fled AI-assisted hiring in droves. Following New York City's 2021 requirement that employers audit automated hiring tools for bias, every single audit revealed discrimination against women, people of colour, LGBTQ+ candidates, neurodivergent individuals, and non-native English speakers. The message seemed clear: algorithms cannot be trusted with something as consequential as hiring.

Yet in their rush to abandon biased machines, tech companies have doubled down on interview methods carrying their own insidious prejudices. Pair programming sessions, whiteboard challenges, and multi-round panel interviews have become the gold standard, positioned as objective measures of technical skill. For neurodivergent candidates (those with autism, ADHD, dyslexia, anxiety disorders, and other neurological differences), these “human-centred” alternatives often prove more discriminatory than any algorithm.

The irony is stark. An industry built on innovation, celebrated for disrupting ossified systems, has responded to AI bias by retreating into traditional interview practices that systematically exclude some of its most talented potential contributors. In fleeing one form of discrimination, tech has embraced another, older prejudice hiding in plain sight.

The Numbers Tell an Uncomfortable Story

Researchers estimate there are 67 million neurodivergent Americans, representing 15% to 20% of the global population. Yet unemployment rates for this group reach 30% to 40%, three times higher than for people with physical disabilities and eight times higher than for non-disabled individuals. For college-educated autistic adults, the figure climbs to a staggering 85%, despite many possessing precisely the skills tech companies desperately seek.

A 2024 survey revealed that 76% of neurodivergent job seekers feel traditional recruitment methods (timed assessments, panel interviews, on-the-spot coding challenges) put them at a disadvantage. Half of neurodivergent adults report experiencing discrimination from hiring managers or recruiters once they disclosed their neurodiversity, with 31% seeing their applications abandoned entirely post-disclosure. A Zurich Insurance UK report found even more troubling statistics: one in five neurodivergent adults reported being openly laughed at during job searches, and one in six had job offers rescinded after disclosing their neurodivergence.

Within the tech sector specifically, nearly one in four neurodivergent workers recalled instances of discrimination. A 2024 BIMA study surveying 3,333 technology workers uncovered significant discrimination related to neurodivergence, alongside gender, ethnicity, and age. More than a third of respondents in a Prospect union survey reported discrimination related to their neurodivergent condition, whilst four in five faced direct workplace challenges because of it. A third said their workplace experience negatively impacted their mental wellbeing; a fifth said it harmed their ability to perform well.

These aren't abstract statistics. They represent brilliant minds lost to an industry that claims to value talent above all else, yet cannot recognise it when packaged differently.

The Evolution of Tech Interviews

To understand how we arrived here, consider the evolution of tech hiring. In the 1990s and early 2000s, companies like Microsoft and Google became infamous for brain teasers and logic puzzles. “Why are manhole covers round?” and “How would you move Mount Fuji?” were considered legitimate interview questions, supposedly revealing problem-solving abilities and creativity.

Research eventually exposed these questions as poor predictors of actual job performance, often measuring little beyond a candidate's familiarity with such puzzles. The industry moved on, embracing what seemed like better alternatives: technical assessments that directly tested coding ability.

Whiteboard interviews became ubiquitous. Candidates stood before panels of engineers, solving complex algorithms on whiteboards whilst explaining their thought processes. Pair programming sessions followed, where candidates collaborated with current employees on real problems, demonstrating both technical skills and cultural fit.

These methods appeared superior to arbitrary brain teasers. They tested actual job-relevant skills in realistic scenarios. Many companies proclaimed them more objective, more fair, more predictive of success.

For neurotypical candidates, perhaps they are. For neurodivergent individuals, they can be nightmarish gauntlets that have little relation to actual job performance and everything to do with performing competence under specific, high-pressure conditions.

What Happens When Your Brain Works Differently

Consider the standard pair programming interview from a neurodivergent perspective. You're placed in an unfamiliar environment, under observation by strangers whose judgement will determine your livelihood. You're expected to think aloud, explaining your reasoning in real-time whilst writing code, fielding questions, reading social cues, and managing the interpersonal dynamics of collaboration, all simultaneously.

For someone with ADHD, this scenario can severely impair short-term memory, memory recall, and problem-solving speed. The brain simply doesn't have the bandwidth to handle spontaneous problem-solving whilst maintaining the social performance expected. As one industry observer noted, coding interviews with whiteboarding or code pairing become “excruciating” when your brain lacks the speed for instant detailed memory recall.

Research confirms that adults with specific learning disabilities who have low sensory thresholds tend to notice too many stimuli, including irrelevant ones. This sensory overload interferes with their ability to select relevant information for executive functions to process. When cognitively overloaded, sensory overload intensifies, creating a vicious cycle.

For autistic candidates, the challenges multiply. Studies show neurodivergent employees experience disproportionate stress in team interactions compared to neurotypical colleagues. Whilst pair programming may be less stressful than large meetings, it still demands interpersonal communication skills that can be emotionally draining and cognitively expensive for autistic individuals. Research found that autistic people felt they had to hide their traits to gain employment, and many worried about discrimination if they disclosed during hiring.

During whiteboard challenges, candidates often stand before groups ranging from two to sixteen interviewers, facing a wall whilst solving complex algorithms. For autistic candidates, this setup makes concentration nearly impossible, even on simple questions. It's an experience they'll never encounter in the actual job, yet it determines whether they're hired.

The physical environment itself can be overwhelming. Bright fluorescent lights, background noise from open offices, unexpected sounds, strong smells from nearby kitchens or perfumes, all create sensory assaults that neurotypical interviewers barely notice. For sensory-sensitive candidates, these distractions aren't minor annoyances; they're cognitive impediments that dramatically impair performance.

Timed assessments compound these difficulties. Pressure intensifies anxiety, which for neurodivergent candidates often reaches paralysing levels. Research shows autistic job applicants experience significantly more interview anxiety than neurotypical candidates and worry intensely about how potential employers perceive them. This anxiety can cause candidates to freeze, unable to think on the spot regardless of their knowledge or experience.

The phenomenon called “masking” adds another layer of exhaustion. Eighty-five percent of neurodivergent tech workers in the Prospect survey reported masking their condition at work, consciously suppressing natural behaviours to appear neurotypical. This requires enormous cognitive effort, leading to mental and physical fatigue, increased anxiety and depression, and reduced job satisfaction. During interviews, when cognitive resources are already stretched thin by technical challenges and performance pressure, the additional burden of masking can be devastating.

Štěpán Hladík, a technical sourcer at Pure Storage who has disclosed his neurodivergence, feels “truly privileged to have been around colleagues who are willing to understand or actively try to learn about biases.” But he notes previous experiences at other companies left him feeling misunderstood and frustrated. Many neurodivergent workers don't disclose their conditions, citing “fear of discrimination as well as ignorance of colleagues” and concerns about career progression. In one study, 53% said potential outcomes of disclosure weren't worth the risk, 27% cited stigma concerns, and 24% feared career impact.

When candidates attempt to request accommodations, the consequences can be severe. Industry reports suggest that when candidates gently ask about available disability accommodations during interviews, they're dropped “about 60% to 70% of the time” as companies “freak out and wash their hands of it to keep things simple.” One tech worker shared observations about Meta: “I've seen a lot of neurodivergent people really struggle” there, having heard “you can be immediately rejected by asking for accommodations.” They noted that “the tech industry has always been rife with discrimination.”

The Research on Interview Bias

Whilst tech companies abandoned AI tools due to proven bias, research reveals traditional interview methods carry substantial biases of their own. A 2024 study published by IntechOpen found that interviewing processes are “inherently susceptible to human bias, which can adversely affect the fairness and validity of outcomes, leading to discrimination and a lack of diversity.”

Interviewers make decisions based on extraneous elements like age, gender, ethnicity, physical attributes, and other personal traits instead of professional qualifications. They succumb to confirmation bias and the halo effect, distorting assessments and creating less diverse workforces. These biases stem from subconscious prejudices, stereotypes, and personal preferences, including entrenched notions about gender, race, and age.

Unstructured interviews, despite receiving the highest ratings for perceived effectiveness from hiring managers, are among the worst predictors of actual job performance. They're far less reliable than general mental ability tests, aptitude tests, or personality tests. Yet they remain popular because they feel right to interviewers, confirming their belief that they can intuitively identify talent.

Traditional interviews test whether candidates can perform interviews, not whether they can perform jobs. For neurodivergent candidates, this distinction is critical. The skills required to excel in pair programming interviews (simultaneous multitasking, real-time verbal processing, social calibration, tolerance for sensory chaos, performance under observation) often differ dramatically from skills required for actual software development.

What Neurodivergent Talent Brings to Tech

The tragedy of this systematic exclusion becomes even sharper when considering what neurodivergent individuals bring to technical roles. Many possess precisely the capabilities that make exceptional programmers, data scientists, and engineers.

Pattern recognition stands out as a particular neurodivergent strength. Many autistic and dyslexic individuals demonstrate extraordinary abilities in identifying patterns and making connections between seemingly unrelated information. In scientific research, they excel at spotting patterns and correlations in complex datasets. In business contexts, they identify connections others miss, leading to innovative solutions and improved decision-making. In fields like design, architecture, and technology, they perceive structures and patterns that might be invisible to neurotypical colleagues.

Attention to detail is another common neurodivergent trait that translates directly to technical excellence. JPMorgan Chase found that employees hired through their neurodiversity programme into tech roles were 90% to 140% more productive than others, with consistent, error-free work. Within six months of their pilot programme, autistic employees proved 48% faster and nearly 92% more productive than neurotypical colleagues.

Hyperfocus, particularly common in ADHD individuals, enables sustained concentration on complex problems, often resulting in innovative solutions and exceptional outcomes. When provided with environments that support their working styles, neurodivergent employees can achieve levels of productivity and insight that justify building entire programmes around recruiting them.

Technical aptitude comes naturally to many neurodivergent individuals, who often excel in programming, coding, and computer science. Their analytical thinking and affinity for technology make them valuable in fields requiring technical expertise and innovation. Some possess exceptional memory skills, absorbing and recalling vast amounts of information, facilitating faster learning and enhanced problem-solving.

Deloitte research suggests workplaces with neurodivergent professionals in some roles can be 30% more productive, noting that “abilities such as visual thinking, attention to detail, pattern recognition, visual memory, and creative thinking can help illuminate ideas or opportunities teams might otherwise have missed.”

Companies Getting It Right

A growing number of organisations have recognised this untapped potential and restructured their hiring processes accordingly. Their success demonstrates that inclusive hiring isn't charity; it's competitive advantage.

SAP launched its Autism at Work initiative in 2013, creating an alternative pathway into the company that maintains rigorous standards whilst accommodating different neurological profiles. The programme operates in 12 countries and has successfully integrated over 200 autistic individuals into various positions. SAP enjoys a remarkable 90% retention rate for employees on the autism spectrum.

Microsoft's Neurodiversity Hiring Programme, established in 2015, reimagined the entire interview process. Instead of traditional phone screens and panel interviews, candidates attend a multi-day “academy” that's part interview, part workshop. This extended format allows candidates to demonstrate skills over time rather than in high-pressure snapshots. The company runs these sessions four to six times yearly and has hired 200 full-time employees spanning customer service, finance, business operations, and marketing.

JPMorgan Chase's Neurodiversity Hiring Programme began as a four-person pilot in 2015 and has since expanded to over 300 employees across 40 job categories in multiple countries. According to Bryan Gill from JPMorgan Chase, “None of this costs a lot and the accommodations are minimal. Moving a seat, perhaps changing a fluorescent bulb, and offering noise-cancelling headphones are the kinds of things we're talking about.”

The business case extends beyond retention and productivity. EY's Neurodiverse Centres of Excellence have generated one billion dollars in revenue and saved over 3.5 million hours through solutions created by neurodivergent employees. A 2024 study found that 63% of companies with neuro-inclusive hiring practices saw improvements in overall employee wellbeing, 55% observed stronger company culture, and 53% reported better people management.

These programmes share common elements. They provide detailed information in advance, including comprehensive agendas and explicit expectations. They offer accommodations like notes, questions provided beforehand, and clear, unambiguous instructions. They focus on work samples and portfolio reviews that demonstrate practical skills rather than hypothetical scenarios. They allow trial projects and job shadowing that let candidates prove capabilities in realistic settings.

Environmental considerations matter too. Quiet locations free from loud noises, bright lights, and distracting smells help candidates feel at ease. Ubisoft found success redesigning workspaces based on employee needs: quiet, controlled spaces for autistic employees who need focus; dynamic environments for individuals with ADHD. This adaptability maximises each employee's strengths.

Practical Steps Towards Inclusive Hiring

For companies without resources to launch comprehensive neurodiversity programmes, smaller changes can still dramatically improve inclusivity. Here's what accommodations look like in practice:

Before: A candidate with auditory processing challenges faces a rapid-fire verbal interview in a noisy conference room, struggling to process questions whilst managing background distractions.

After: The same candidate receives interview questions in writing (either in advance or displayed during the interview), allowing them to process information through their strength channel. The interview occurs in a quiet room, and the interviewer types questions in the chat during video calls.

Before: A candidate with ADHD faces a three-hour marathon interview with no breaks, their cognitive resources depleting as interviewers rotate through, ultimately appearing “unfocused” and “scattered” by the final round.

After: The interview schedule explicitly includes 15-minute breaks between sessions. The candidate can step outside, regulate their nervous system, and approach each conversation with renewed energy. Performance consistency across all rounds improves dramatically.

Before: An autistic candidate receives a vague email: “We'll have a technical discussion about your experience. Dress business casual. See you Tuesday!” They spend days anxious about what “technical discussion” means, who will attend, and what specific topics might arise.

After: The candidate receives a detailed agenda: “You'll meet with three engineers for 45 minutes each. Session one covers your recent database optimisation work. Session two involves a code walkthrough of your GitHub project. Session three discusses system design approaches. Here are the interviewers' names and roles. Interview questions are attached.” Anxiety transforms into productive preparation.

Replace timed, high-pressure technical interviews with take-home projects allowing candidates to work in comfortable environments at their own pace. Research shows work sample tests are among the strongest predictors of on-the-job performance and tend to be more equitable across demographic groups.

Provide interview questions in advance. This practice, now standard at some major tech brands, allows all candidates to prepare thoughtfully rather than privileging those who happen to excel at impromptu performance. As AskEARN guidance notes, candidates can request questions in writing without disclosing a diagnosis: “I have a condition that affects how I process verbal information, so I would like interview questions provided in writing.”

Offer explicit accommodation options upfront, before candidates must disclose disabilities. Simple statements like “We're happy to accommodate different working styles; please let us know if you'd benefit from receiving questions in advance, having extra time, taking breaks, or other adjustments” signal that accommodations are normal, not problematic. Under the Americans with Disabilities Act and Rehabilitation Act, employers are legally required to provide reasonable accommodations during hiring.

Implement structured interviews with standardised questions. Whilst unstructured interviews are biased and unreliable, structured interviews predict job performance with validity of 0.55 to 0.70, outperforming traditional approaches requiring up to four rounds for comparable accuracy.

Consider alternative formats to live coding. Code walkthroughs of recent projects on-screen, where candidates explain existing work, can reveal far more about actual capabilities than watching someone write algorithms under pressure. Portfolio reviews, GitHub contributions, and technical writing samples provide evidence of skills without performative elements.

Ask direct, specific questions rather than open-ended ones. Instead of “What can you bring to the table?” (which neurodivergent brains may interpret literally or find overwhelming), ask “Can you talk about a key project you recently worked on and how you contributed?” Open-ended questions cause neurodivergent minds to flood with information, whilst direct questions work better.

Reduce panel sizes. One-to-one interviews reduce anxiety compared to facing multiple interviewers simultaneously. If panels are necessary, provide clear information about who will attend, their roles, and what each will assess.

Train interviewers on neurodiversity and inclusive practices. Research found that bias dropped 13% when participants began with implicit association tests intended to detect subconscious bias. Forty-three percent of senior leaders received some neurodiversity training in 2025, up from 28% in 2023.

Create employee resource groups for neurodivergent employees. Ubisoft's ERG has grown to over 500 members globally, helping employees connect and thrive. Dell's True Ability ERG pairs new hires with experienced mentors for ongoing support.

The Deeper Question

These practical steps matter, but they address symptoms rather than the underlying condition. The deeper question is why tech companies, confronted with algorithmic bias, responded by retreating to traditional methods rather than designing genuinely better alternatives.

Part of the answer lies in what researchers call the “objectivity illusion.” Humans tend to trust their own judgements more than algorithmic outputs, even when evidence shows human decisions are more biased. When Amazon's algorithm discriminated against women, the bias was visible, quantifiable, and damning. When human interviewers make similar judgements, the bias hides behind subjective assessments of “cultural fit” and “communication skills.”

AI bias is a feature, not a bug. Algorithms trained on biased historical data reproduce that bias with mathematical precision. But this transparency can be leveraged. Algorithmic decisions can be audited, tested, and corrected in ways human decisions cannot. The problem isn't that AI is biased; it's that we built biased AI and then abandoned the entire approach rather than fixing it.

Meanwhile, traditional interviews embed biases so deeply into process and culture that they become invisible. When neurodivergent candidates fail pair programming interviews, interviewers attribute it to poor skills or bad cultural fit, not to interview design that systematically disadvantages certain neurological profiles. The bias is laundered through seemingly objective technical assessments.

This reveals a broader failure of imagination. Tech prides itself on solving complex problems through innovation and iteration. Faced with biased hiring AI, the industry could have invested in better algorithms, more representative training data, robust bias detection and correction mechanisms. Instead, it abandoned ship.

The same innovative energy directed at optimising ad click-through rates or recommendation algorithms could revolutionise hiring. Imagine interview processes that adapt to candidates' strengths, that measure actual job-relevant skills in ways accommodating neurological diversity, that use technology to reduce bias rather than amplify it.

Some experiments point in promising directions. Asynchronous video interviews allow candidates to answer questions in their own time, reducing pressure. Computer-based assessments provide instant feedback, helping autistic individuals improve performance. Structured digital platforms ensure every candidate faces identical questions in identical formats, reducing interviewer discretion and thus bias.

The Intersectional Dimension

For neurodivergent individuals from ethnic minority backgrounds, challenges compound. Research on intersectional stereotyping shows these candidates face layered discrimination that adversely affects recruitment, performance evaluation, and career progression. The biases don't simply add; they multiply, creating unique barriers that neither neurodiversity programmes nor diversity initiatives alone can address.

Women who are neurodivergent face particular challenges. Amazon's AI tool discriminated against women; traditional interviews often do too, filtered through gendered expectations about communication styles and leadership presence. Add neurodivergence to the mix, and the barriers become formidable.

This intersectionality demands more sophisticated responses than simply adding neurodiversity awareness to existing diversity training. It requires understanding how different forms of marginalisation interact, how biases reinforce each other, and how solutions must address the whole person rather than isolated demographic categories.

Companies ignoring these issues face growing legal exposure. Disability discrimination claims from neurodivergent employees have risen sharply. In fiscal year 2023, 488 autism-related Americans with Disabilities Act charges were filed with the EEOC, compared to just 53 ten years earlier and only 14 in 2003.

Remote work has become the most commonly requested accommodation for neurodivergent employees under the ADA, precisely because it provides control over work environments. Companies that eliminated remote options post-pandemic may find themselves defending decisions that disproportionately impact disabled workers.

The law is clear: employers must provide reasonable accommodations for qualified individuals with disabilities unless doing so would cause undue hardship. Many accommodations neurodivergent employees need cost little to nothing. Companies that refuse face not just legal liability but reputational damage in an industry claiming to value diversity.

What We're Really Measuring

Perhaps the most fundamental question is what interviews actually measure versus what we think they measure. Traditional interviews, including pair programming sessions, test a specific skill set: performing competence under observation in unfamiliar, high-pressure social situations requiring real-time multitasking and spontaneous problem-solving whilst managing interpersonal dynamics.

These capabilities matter for some roles. If you're hiring someone to give live demos to sceptical clients or debug critical systems whilst stakeholders watch anxiously, interview performance may correlate with job performance.

But for most technical roles, day-to-day work looks nothing like interviews. Developers typically work on problems over hours or days, not minutes. They have time to research, experiment, and iterate. They work in familiar environments with established routines. They collaborate asynchronously through well-defined processes, not impromptu pair programming. They manage their sensory environments and work schedules to optimise productivity.

By privileging interview performance over demonstrated ability, tech companies filter for candidates who excel at interviews, not necessarily at jobs. When it systematically excludes neurodivergent individuals who might outperform neurotypical colleagues in actual role requirements, it becomes both discriminatory and economically irrational.

Rethinking Progress

Tech cannot claim to value objectivity whilst relying on subjective, bias-laden interview processes. It cannot champion innovation whilst clinging to traditional hiring methods proven to exclude talented candidates. It cannot celebrate diversity whilst systematically filtering out neurological difference.

The flight from AI bias was understandable but incomplete. Algorithmic hiring tools reproduced historical discrimination, but retreating to equally biased human processes isn't the solution. Building better systems is. Both technological and human systems need redesign to actively counteract bias rather than embed it.

This means taking neurodiversity seriously, not as an HR checkbox but as a competitive imperative. It means redesigning interview processes from the ground up with inclusivity as a core requirement. It means measuring outcomes (who gets hired, who succeeds, who leaves and why) and iterating based on evidence.

The tech industry's talent shortage is partly self-inflicted. Millions of neurodivergent individuals possess precisely the skills companies claim they cannot find. They're filtered out not because they lack ability but because hiring processes cannot recognise ability packaged differently.

The companies demonstrating success with neurodiversity hiring programmes aren't being charitable. They're being smart. Ninety percent retention rates, 48% faster performance, 92% higher productivity, one billion dollars in revenue from neurodiverse centres: these are business results.

Every brilliant neurodivergent candidate filtered out by poorly designed interviews is a competitive advantage surrendered. The question isn't whether companies can afford to make hiring more inclusive. It's whether they can afford not to.

Amazon's biased algorithm taught an important lesson, but perhaps not the right one. The lesson wasn't “don't use technology in hiring.” It was “design better systems.” That principle applies equally to AI and to traditional interviews.

Tech has spent years agonising over AI bias whilst ignoring the bias baked into human decision-making. It's time to apply the same rigorous, evidence-based approach to interview processes that the industry applies to products. Test assumptions, measure outcomes, identify failures, iterate solutions.

Neurodivergent candidates aren't asking for lower standards. They're asking for fair assessment of their actual capabilities rather than their ability to perform neurotypicality under pressure. That's not a diversity favour. It's basic competence in hiring.

The paradox of progress is that moving forward sometimes requires questioning what we thought was already solved. Tech believed it had moved beyond crude brain teasers to sophisticated technical assessments. But sophisticated discrimination is still discrimination.

In fleeing AI's biases, tech ran straight into human prejudice hiding in hiring processes all along. The industry faces a choice: continue defending traditional interviews because they feel objective, or measure whether they're actually finding the best talent. The data increasingly suggests they're not.

Real progress requires acknowledging uncomfortable truths. “Culture fit” often means “people like us.” “Communication skills” sometimes translates to “neurotypical presentation.” The hardest technical problems in hiring aren't algorithmic. They're human.

The question isn't whether neurodivergent candidates can meet tech's standards. It's whether those standards measure what actually matters. Right now, the evidence suggests they're optimising for the wrong metrics and missing extraordinary talent.

That's not just unfair. In an industry built on finding edge advantages through better information and smarter systems, it's inexcusably inefficient. The companies that figure this out first won't just be more diverse. They'll be more competitive.

The problem was never the algorithms. It was the biases we fed them, the outcomes we optimised for, the assumptions we never questioned. Those same problems afflict traditional hiring, just less visibly. Making them visible is the first step. Actually fixing them is the work ahead.

References and Sources

Neurodivergent Employment Statistics

Interview Challenges and Discrimination

AI Bias in Hiring

Inclusive Hiring Best Practices

Sensory Processing and Executive Function

Neurodivergent Strengths

Company Success Stories

Traditional Interview Bias Research

Work Sample Tests and Structured Interviews

Workplace Discrimination

Tim Green

Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

Discuss...