How AI Impacts Vulnerable Communities

We've already seen how AI can be weaponized against communities of color, just look at its use in criminal justice, where algorithms like COMPAS have falsely labeled Black defendants as high-risk at nearly twice the rate of white defendants. Are we ready for that same flawed technology to become the backbone of our education system? The Minnesota Spokesman-Recorder's powerful piece "AI in Schools: Revolution or Risk for Black Students" asks this exact question. At a glance, AI in classrooms sounds promising personalized learning, reduced administrative burdens, and faster feedback. However, for Black students, the reality is more complicated; Bias baked into the algorithm: From grading to discipline, AI tools are often trained on data that reflect society's worst prejudices. The digital divide is still very real: Nearly 1 in 4 Black households with school-age children have no access to high-speed internet at home. Whose perspective shaped the tech? A lack of Black developers and decision-makers means many AI systems fail to recognize or respond to our students' lived experiences. And yet, the rollout is happening—fast. One in four educators plans to expand their use of AI this year alone, often without meaningful policy guardrails. We must ask: Who is this tech designed to serve—and at whose expense? This article is a must-read for anyone in education, tech, or equity work. Let's make sure the "future of learning" doesn't repeat the mistakes of the past. #AI #GlobalEducation #publiceducation #CommunityEngagement #equity #Youthdevelopment #AIinEducation #DigitalJustice #EquityInTech #EdTechWithIntegrity Read the article here: https://lnkd.in/g9U7za_k

🤖 The Gendered Impact of AI: Why Women—Especially from Marginalised Backgrounds—Are Most at Risk As artificial intelligence continues to reshape the world of work, one thing is becoming increasingly clear: the effects will not be felt equally. A new report from the United Nations’s International Labour Organization and Poland’s NASK reveals that roles traditionally held by women—particularly in high-income countries—are almost three times more likely to be disrupted by generative AI than those held by men. 📉 9.6% of female-held jobs are at high risk of transformation, compared to just 3.5% of male-held roles. Why? Many of these jobs are in administration and clerical work—sectors where AI can automate routine tasks efficiently. But while AI may not eliminate these roles outright, it is radically reshaping them, threatening job security and career progression for many women. This risk is not theoretical. Back in 2023, researchers at OpenAI—the company behind ChatGPT—examined the potential exposure of different occupations to large language models like GPT-4. The results were striking: around 80% of the US workforce could have at least 10% of their work tasks impacted by generative AI. While they were careful not to label this a prediction, the message was clear: AI's reach is widespread and accelerating. 🌍 An intersectional lens shows even deeper inequities. Women from marginalised communities—especially women of colour, older women, and those with lower levels of formal education—face heightened vulnerability: They are overrepresented in lower-paid, more automatable roles, with limited access to training or advancement. They often lack the tools, networks, and opportunities to adapt to digital shifts. And they face greater risks of bias within the AI systems themselves, which can reinforce inequality in recruitment and promotion. Meanwhile, roles being augmented by AI—like those in tech, media, and finance—are still largely male-dominated, widening the gender and racial divide in the AI economy. According to the World Economic Forum, 33.7% of women are in jobs being disrupted by AI, compared to just 25.5% of men. 📢 As AI moves from buzzword to business reality, we need more than technical solutions—we need intentional, inclusive strategies. That means designing AI systems that reflect the full diversity of society, investing in upskilling programmes that reach everyone, and ensuring the benefits of AI are distributed fairly. The question on my mind is - if AI is shaping the future of work, who’s shaping AI? #AI #FutureOfWork #EquityInTech #GenderEquality #Intersectionality #Inclusion #ResponsibleTech

⚠️ 𝗠𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗱𝗶𝘀𝗮𝗯𝗹𝗲𝗱 𝗹𝗶𝘃𝗲𝘀 𝗮𝗿𝗲 𝗮𝘁 𝘀𝘁𝗮𝗸𝗲 𝗮𝗻𝗱 𝗔𝗜 𝗜𝘀 𝗱𝗲𝗰𝗶𝗱𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝗳𝗮𝘁𝗲 Every day, biased AI systems make decisions that impact millions of disabled people often without their knowledge or consent. ⚠️ 73 MILLION people in the U.S. have a disability yet too many are left behind by biased AI. 📉 Studies show AI resume screeners reject disabled candidates at higher rates, even when qualified. 🏥 Algorithms deny critical healthcare services, increasing the risk of preventable harm. 📚 88% of schools use AI to monitor students, but disabled students get flagged unfairly 12% more often than their peers. 🚔 Risk assessment algorithms are twice as likely to recommend harsher sentences for disabled people. 💡 The data is clear in the Centre for Democracy & Technology Europe + American Association of People with Disabilities report that without action, AI doesn’t just automate decisions. it amplifies discrimination. 𝗧𝗵𝗲 𝗪𝗮𝘆 𝗙𝗼𝗿𝘄𝗮𝗿𝗱 ►Design AI with diverse, representative data ►Ensure full transparency in AI use ►Keep humans in the loop to avoid automation bias ►Treat AI in benefits, hiring, and healthcare as “high-risk” ►Involve disabled people in design and policy decisions ►Make data privacy a fundamental right ►Continuously audit AI for bias and discrimination 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲  Inclusive AI drives better outcomes and diverse data leads to smarter, fairer decisions that serve everyone. Ignoring accessibility is not only unethical but a blind spot that risks innovation, reputation and real human lives. 💭 What if the next job, the next healthcare decision or the next school evaluation of someone you care about is made by an algorithm… without accountability? Prof. Dr. Ingrid Vasiliu-Feltes|Helen Yu|JOY CASE|Hr Dr. Takahisa Karita|Antonio Grasso|Nicolas Babin |Alberto Espinosa Machado|Dr. Ram Kumar|Phillip J Mostert| Sara Simmonds |Anthony Rochand|Prasanna Lohar|Shalini Rao #AI #EthicalAI #InclusiveTech #AIForGood #AccessibilityMatters #DisabilityInclusion #TechForAll #ResponsibleInnovation

If AI is learning from biased data, what happens to candidates with disabilities? The rise of automated hiring tools may be locking out millions, and no one is noticing, because it's silent. AI now scans resumes and analyzes video interviews, and companies are adopting it faster than ever. A late-2023 IBM survey of over 8,500 global IT professionals found that 𝟰𝟮% 𝗼𝗳 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘂𝘀𝗲 𝗔𝗜 𝗶𝗻 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗶𝗻𝗴, and 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝟰𝟬% 𝗮𝗿𝗲 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗶𝗻𝗴 𝗶𝘁. The hope was that AI would reduce hiring bias. But in many cases, the opposite is happening. When trained on data that excludes people with disabilities, it learns to overlook them, too. In June 2025, the New York City Bar Association released a report on The Impact of the Use of AI on People with Disabilities (linked in the comments). Findings show that the statistical nature of AI often leads to discrimination, especially against people with disabilities who fall outside the "average" profiles these systems are built around. The scale of the issue is hard to ignore. Some might argue that one biased hiring manager could affect dozens of candidates in a year. But, as Hilke Schellmann points out, a flawed algorithm deployed across a major employer could impact hundreds of thousands. And because many vendors are rushing underdeveloped tools to market (driven by demand and profit, of course), there's little transparency or accountability. Companies using them often avoid admitting potential harm, fearing legal risk. So what can be done? Making AI inclusive requires a complete shift in how it's developed and implemented, with disability inclusion embedded from the start: ▶️ Use better data. Train AI using datasets that reflect the full range of human experiences, including physical, sensory, cognitive, and mental health disabilities, collected ethically and with consent. ▶️ Design with accessibility in mind. Build tools that work for everyone from the beginning. That includes compatibility with screen readers, voice recognition, and adjustable visual environments and formats. ▶️ Co-create with disabled people. Involve people with disabilities at every stage, from ideation to testing to launch. Feedback should be continuous, not one-off. ▶️ Test for bias. Run regular audits to detect and address bias. Create clear pathways for users to report issues and request improvements. One promising tool is the Conditional Demographic Disparity test, co-developed in 2020 by Sandra Wachter, Professor of Technology and Regulation at the University of Oxford. This public framework helps detect bias in hiring algorithms and pinpoint decision criteria driving inequality - enabling fairer, more accurate systems. Amazon and IBM are already using it. Be honest - how confident are we in the tools we're using to screen talent? #InclusiveHiring #HiringBias #AIRegulation #DisabilityInclusion

On AI fairness, transparency, and bias. From the summary: "Employing a broad definition of AI, this report represents the first known effort to comprehensively explain and quantify the reach of AI-based decision-making among low-income people in the United States. It establishes that essentially all 92 million low-income people in the U.S. states—everyone whose income is less than 200 percent of the federal poverty line—have some basic aspect of their lives decided by AI." 🧑👧👦 92 million Americans live below 200% of the federal poverty line and have some aspect of their lives decided by AI 🏥 73 million low-income people face AI decisions in Medicaid through eligibility, enrollment, and service determinations 👴 16.5 million low-income people encounter AI through Medicare Advantage prior authorization processes 🏢 30.6 million low-income people deal with AI in private health insurance prior authorization systems 🛒 42 million low-income people face AI in SNAP (food assistance) through eligibility and fraud detection systems ♿ 10.6 million low-income people experience AI decisions in Social Security disability benefits programs 💼 32.4 million low-wage workers face AI in employment, including hiring, surveillance, and wage-setting 🏠 39.8 million low-income people encounter AI through landlord screening and rent-setting algorithms 🏫 13.25 million low-income children face AI decisions in schools through dropout prediction and surveillance 🗣️ 5 million low-income people with limited English proficiency encounter AI-based translation services 👮 2 million low-income people face AI through police risk assessment systems for domestic violence 👶 72,000 low-income children encounter AI through child welfare agency neglect prediction systems 💳 1.1 million low-income people face AI in unemployment insurance through eligibility and fraud detection

A few years ago, I visited a remote clinic far from any metropolitan skyline. No advanced diagnostics. No specialist on call. One physician serving thousands. Paper files stacked in corners. Patients traveling hours for basic consultations. Yet the need for care there was no less urgent than in the most sophisticated tertiary hospital. That moment stayed with me. Because rural healthcare is not a secondary system. It is the frontline of health equity. Today, I’m sharing the latest edition of AI Health Equity Chronicles and it reflects a conviction I have carried for years: Artificial Intelligence must serve the last mile, not just the luxury tier of healthcare. At TECHMEDO, we began with a simple but ambitious question: What if a rural clinic could think like a tertiary hospital? What if a nurse in a remote village could access AI-assisted diagnostics with and withour access to cloud? What if chronic patients could be monitored from home instead of traveling long distances? What if humanitarian relief teams could triage populations with predictive insight instead of reactive response? This is not theoretical anymore. AI today enables: • Early risk identification for diabetes, cardiac disease, maternal complications • AI-assisted imaging in the absence of radiologists • Remote patient monitoring for blood pressure, glucose, oxygen levels • Structured digital records in low-connectivity environments • Intelligent referral systems connecting primary care to higher centers But the impact goes beyond rural geographies. In humanitarian relief operations where infrastructure may be disrupted and resources are scarce AI-powered platforms help medical teams prioritize high-risk patients, coordinate mobile units, and maintain continuity of care in unstable settings. For us at TECHMEDO, rural health and humanitarian response are not separate conversations. They are part of the same systems design challenge: how to deliver intelligent, accessible, and financially sustainable care regardless of geography. AI is not about replacing clinicians. It is about extending expertise. Augmenting limited resources. Bringing structured decision support where it was previously unavailable. The real question is no longer whether AI belongs in rural healthcare. The real question is how fast we can deploy it responsibly, sustainably, and equitably. Because if artificial intelligence only enhances urban hospitals, it has failed its broader mission. But if it empowers the rural nurse, strengthens the primary care physician, and reaches the communities beyond the skyline then it becomes transformational. Healthcare equity is not a slogan. It is a responsibility. And the frontier of innovation is not always in smart cities. Sometimes, it begins in the most remote clinic where impact matters most. #RuralHealthcare #DigitalHealth #AIinHealthcare #HealthEquity #Telemedicine #HumanitarianRelief #PrimaryCare #Innovation

📘 Unequal Diffusion of AI by UNDP The Next Great Divergence: Why AI May Widen Inequality Between Countries is a 2025 report by the UNDP Regional Bureau for Asia and the Pacific that examines AI as a transformative general purpose technology and evaluates its distributional risks across Asia and the Pacific. Using a human development framework, it analyzes how AI affects 👥 people, 💼 economies, and 🏛 governance systems in contexts ranging from advanced innovation leaders to lower capacity states. While AI can increase productivity and expand capabilities, unequal readiness in infrastructure, skills, and institutions may widen both 🌍 cross country and 🏙 within country inequality unless proactively addressed. ⚠️ Five Core Concerns 1️⃣ Unequal Diffusion of AI Infrastructure ⚡🌐 AI adoption is constrained by uneven access to electricity, broadband, data ecosystems, and compute capacity, concentrating gains in already advanced economies. #CallToAction: Treat AI infrastructure as core development capital and expand regional investment in connectivity. Prioritize lower capacity countries in financing strategies to prevent structural exclusion. 2️⃣ Skill Polarization and Labor Disruption 👩💻📉 AI complements high skill labor while automating routine cognitive tasks, increasing income concentration and destabilizing existing employment models. #CallToAction: Reform education systems toward adaptive digital competencies and lifelong learning. Strengthen active labor policies to manage transitional displacement. 3️⃣ Data Bias and Algorithmic Exclusion 🧠⚖️ Unrepresentative training data can systematically exclude marginalized populations from credit, healthcare, welfare, and justice systems. #CallToAction: Mandate transparency, impact assessments, and independent audits for high stakes AI systems. Invest in inclusive data governance to ensure diverse representation. 4️⃣ Weak Governance and Regulatory Capacity 🏛🔍 Limited institutional expertise and regulatory clarity increase risks of vendor lock in, misuse, opacity, and cyber vulnerability. #CallToAction: Build specialized AI oversight capacity within public institutions and establish independent supervisory mechanisms. Coordinate regionally to align standards and strengthen regulatory resilience. 5️⃣ Compressed Policy Window ⏳🚀 AI is diffusing faster than prior technological waves, narrowing the opportunity for anticipatory governance and inclusion safeguards. #CallToAction: Implement baseline accountability. Sequence longer term reforms in parallel with technological deployment rather than after harm occurs. More details https://lnkd.in/e8TjEwtA 📚 APA UNDP Regional Bureau for Asia and the Pacific. (2025). The next great divergence: Why AI may widen inequality between countries. UNDP. https://lnkd.in/e8U7AE-F

The headlines this week have been chilling: a former tech executive killed his mother and himself after months of conversations with ChatGPT that reinforced his paranoid delusions. Just a week earlier, the family of 16-year-old Adam Raine sued OpenAI, alleging ChatGPT coached their son on suicide methods rather than directing him to help. These aren't just isolated tragedies. They're urgent wake-up calls about the intersection of AI and mental health that we in tech can no longer ignore. As someone who's spent years building digital products, I'm forced to confront an uncomfortable truth: the tools we create with the best intentions can become dangerous amplifiers for those in crisis. Mental health experts warn that chatbots can reinforce delusions in vulnerable individuals, yet we've deployed these systems at massive scale with insufficient safeguards. The Connecticut case is particularly haunting because it shows how AI can become a trusted confidant for someone spiraling into mental illness. The victim nicknamed ChatGPT "Bobby" and enabled its memory feature to build on previous conspiracy conversations. When someone is losing touch with reality, an AI that validates their fears instead of grounding them becomes not just unhelpful - it becomes dangerous. For teenagers already navigating identity, social pressures, and emotional turbulence, these risks are amplified. Young people often turn to technology for support when they feel they can't talk to adults. If that technology lacks proper crisis intervention protocols, we're failing our most vulnerable users. This isn't about stifling innovation. It's about building responsibility into our systems from day one. We need: • Robust mental health screening in AI interactions • Mandatory crisis intervention protocols that prioritize human connection • Transparency about AI limitations in emotional support • Industry-wide standards for detecting and responding to users in distress The tragic irony? These cases involve a tech executive and a student - people who should have been equipped to understand AI's limitations. If they were vulnerable to these risks, imagine the broader population. Every algorithm we ship, every feature we launch, every interaction we enable carries the weight of human consequence. We have a moral obligation to consider not just what our technology can do, but what it should do when someone is hurting. The question isn't whether AI will continue advancing - it will. The question is whether we'll advance our responsibility alongside it. What safeguards do you think should be mandatory for AI systems that engage in personal conversations? How do we balance innovation with protection?