Media and Public Perception

🌍 Ten Years After Paris: is the Climate Crisis a Disinformation Crisis? In 2015, the world made a historic promise: to keep global warming well below 2°C, and ideally below 1.5°C. We committed to major emission cuts by 2030, and net-zero by 2050. The Paris Agreement marked a new era of global climate cooperation. But ten years on, we're still struggling with cooperation while the World Meteorological Organization tells us that the Earth’s average temperature exceeded 1.5°C over a 12-month period (Feb 2023–Jan 2024) for the first time. Why? 🔍 A groundbreaking new study, led by 14 researchers for the International Panel on the Information Environment, reviewed 300 studies from 2015–2025. The findings are alarming: powerful interests – fossil fuel companies, populist parties, even some governments – are systematically spreading misleading narratives to delay climate action. 🧠 Misinformation isn't just about denying climate change. It’s now about strategic skepticism – minimizing the threat, casting doubt on science-based solutions, and greenwashing unsustainable practices. 📺 This disinformation flows through social media, news outlets, corporate reports, and even policy briefings. It targets all of us – but especially policymakers, where it can shape laws and delay critical decisions. 💡 So what can we do? 1️⃣ Legislate for transparency and integrity in climate communication. 2️⃣ Hold greenwashers accountable through legal action. 3️⃣ Build global coalitions of civil society, science, and public institutions. 4️⃣ Invest in climate and media literacy for both citizens and leaders. 5️⃣ Amplify voices from underrepresented regions – like Africa – where more research is urgently needed. We must protect not only the planet’s climate, but the integrity of climate information. 🔗 Read more on how disinformation is undermining climate progress – and what we can do about it: https://lnkd.in/eDN9hKAJ 🕰️ The window is small. But with truth, science, and collective action, we can still turn the tide.

Separate reports by the publicity firm Edelman and Pew Research (links in orig text, below) show that Americans, and more broadly large parts of Europe and the western world, do not trust AI and are not excited about it. Despite the AI community’s optimism about the tremendous benefits AI will bring, we should take this seriously and not dismiss it. The public’s concerns about AI can be a significant drag on progress, and we can do a lot to address them. According to Edelman’s survey, in the U.S., 49% of people reject the growing use of AI, and 17% embrace it. In China, 10% reject it and 54% embrace it. Pew’s data also shows many other nations much more enthusiastic than the U.S. about AI adoption. Positive sentiment toward AI is a huge national advantage. On the other hand, widespread distrust of AI means: - Individuals will be slow to adopt it. For example, Edelman’s data shows that, in the U.S., those who rarely use AI cite Trust (70%) more than lack of Motivation and Access (55%) or Intimidation by the technology (12%) as an issue. - Valuable projects that need societal support will be stymied. For example, local protests in Indiana brought down Google’s plan to build a data center there. Hampering construction of data centers will hurt AI’s growth. Communities do have concerns about data centers beyond the general dislike of AI; I will address this in a later letter. - Populist anger against AI raises the risk that laws will be passed that hamper AI development. To be clear, all of us working in AI should look carefully at both the benefits and harmful effects of AI (such as deepfakes polluting social media and biased or inaccurate AI outputs misleading users), speak truthfully about both benefits and harms, and work to ameliorate problems even as we work to grow the benefits. But hype about AI’s danger has done real damage to trust in our field. Much of this hype has come from leading AI companies that aim to make their technology seem extraordinarily powerful by, say, comparing it to nuclear weapons. Unfortunately, a significant fraction of the public has taken this seriously and thinks AI could bring about the end of the world. The AI community has to stop self-inflicting these wounds and work to win back society’s trust. Where do we go from here? First, to win people’s trust, we have a lot of work ahead to make sure AI broadly benefits everyone. “Higher productivity” is often viewed by general audiences as a codeword for “my boss will make more money,” or worse, layoffs. As amazing as ChatGPT is, we still have a lot of work to do to build applications that make an even bigger positive impact on people’s lives. I believe providing training to people will be a key piece of the puzzle. DeepLearning.AI will continue to lead the charge on AI training, but we will need more than this. [Truncated for length. Full text, with links: https://lnkd.in/gUgMDMGS ]

Many discussions about democracy focus on misinformation. But another challenge may be equally important: distorted perceptions of social reality. Social media often functions like a funhouse mirror, amplifying the most extreme voices who dominate online discourse. As a result, people come to falsely believe those voices represent society and public opinion far more than they actually do. People often describe social media as a town hall or a technology that fosters democratic participation. But our data suggests the opposite pattern in terms of how people actually use it. For instance, authoritarian tendencies are about 3X as likely to predict whether people share their political opinion on social media (compared to pro-democratic values). I presented this research today at a special panel on threats to democracy at the Association for Psychological Science conference in Barcelona. I am convinced that these perception distortions is crucial for understanding democratic discourse in the age of AI and social media. You can read our paper on the funhouse mirror here: https://lnkd.in/e_aCGbg4

WOMEN ARE SUPPOSED TO BE SILENT. ROMAN COLISEUM EVENTS ALIVE AND WELL At 57, with white hair and no carefully managed public image, Mary Beard appeared on a BBC panel to discuss immigration and politics. She answered questions, cited evidence, made her arguments—and went home. The next morning, her inbox was filled with rape threats, death threats, and attacks on her appearance, age, and voice. The abuse wasn't about debate. It was about punishing a woman for speaking with authority in public. Most people would have retreated. Beard went looking for the deeper pattern. A Cambridge classicist and one of the world's leading scholars of ancient Rome, she traced the hostility back through centuries of history and literature. In Homer's Odyssey, she found an early example: when Penelope speaks publicly, her son Telemachus orders her back to her room, declaring that speech is the business of men. For Beard, this wasn't ancient history—it was a blueprint. Across Western history, women who spoke with authority were dismissed, mocked, or excluded. In ancient Rome, women's public speech was treated as noise rather than argument. In the medieval period, women claiming authority were branded witches. Even Queen Elizabeth I had to frame herself in masculine terms to be accepted as a ruler. The pattern, Beard argued, is not accidental. For thousands of years, public authority has been defined in ways that exclude women. In Women & Power: A Manifesto (2017), she made a simple but powerful case: the problem is not that women lack the ability to lead. The problem is that traditional models of leadership were built by men and for men. When women don't fit those models, the fault is not theirs—the model was never designed to include them. The answer, Beard said, is not to teach women to imitate existing forms of power, but to rethink what power itself can look like. She continued teaching, writing, and appearing in public exactly as she was: knowledgeable, calm, and unapologetic. The abuse never fully stopped, but another response emerged. Women and girls wrote to tell her that her work helped them understand something important: they weren't failing to fit into the room. The room had been built without them in mind. That isn't a personal flaw. It's a historical design choice. By documenting that history and naming the pattern, Mary Beard gave people a way to see it—and challenge it. Telemachus claimed that speech was the business of men. Three thousand years later, Mary Beard has shown why he was wrong. https://lnkd.in/ewkSPpHS

Let’s talk about white women & the Extraction of Black Emotional Labor White women’s support often arrives full of feeling but rarely backed by action. And when that emotional proximity is challenged, it quickly shifts to silence, defensiveness, or retreat. Often, it begins with public oversharing disclosing how moved or seen you feel by Black women’s work, or sharing their burdens but then placing the weight of those feelings on us to hold. Recently, a white woman commented under a post about my work on African neurodivergence: “Is it weird that I, as a white woman, find solace in your work?” I responded: “Are you asking if research on systems your ancestors helped destroy now brings you comfort?” She deleted the comment. This is not an isolated moment. It’s a recurring pattern where Black scholarship and labour reclaiming ancestral work is consumed for emotional relief, but rarely supported in practice. I don’t do this work to comfort whiteness. I do it to restore, reclaim, and return systems of care and knowledge that were deliberately dismantled. Systems that would benefit everyone, if they were respected rather than pathologised or colonised. Too often, white women forget that power doesn’t trickle down, but our labor always trickles up. And doing this work while holding history, lineage, and ongoing harm requires energy, research, and emotional clarity that’s rarely resourced, but constantly relied on. If you feel held by the work, ask what you’re holding in return. If it speaks to you, ask how you’re helping ensure it continues to exist. All of this applies to white men too, doubly so but white women are in close proximity and think shared womanhood means evading accountability. It doesn’t. #BlackScholarship #EmotionalLabor #Reciprocity #Neurodivergence #DEI #AntiRacism #WhiteWomanhood #LovetteJallow

Steven Bartlett’s AI panel had a major problem, and I’m fed up of pretending this is just an “oversight.” → Fed up of hearing, “We couldn’t find any women.” → Fed up of watching conversations about the future shaped exclusively by men. → Fed up of seeing the same voices, the same perspectives, and the same tired excuses. There wasn’t a single woman in sight. Women aren’t a niche. They’re not a “nice to have.” They’re half the population. A panel on the future… discussing AI, technology, and the impact on our lives, yet it was a room full of men. But this isn’t just about AI. It’s about a much bigger problem. 🚫 Tech events. 🚫 Business panels. 🚫 “Future of work” discussions. Over and over, we see the same story: a lineup of men, shaping decisions that will impact us all. They even talked about the fact that 80% of women are likely to be impacted by AI Agents! This is a choice. A lazy, blinkered, and outdated choice. If you have one of the most influential platforms in the world, like Steven Bartlett does, and you still can’t make space for women, that’s not an accident. That’s a problem. It's a problem that most media - traditional media and films and social media are dominated by men. Even podcasts. 80% of the top podcasts (one of the most influential media now) are hosted or produced by men. Where are the women's voices, opinions, ideas? Yet audiences are 50/50? If we only platform men - how do we ever change the perceptions of women in tech? Women in business? Women shaping the future? Investment in women (which is at a ridiculous 2% of VC money) And don’t get me started on the way we talk about AI itself. Fear-mongering. Clickbait. “AI will take all the jobs.” “Falling birth rates.” “No more work.” When the real conversation should be about responsibility, ethics, and opportunity. Leaning into the ideas and solutions that will help everyone benefit from AI. But without women at the table? We get the same recycled narratives. Women are leading in AI. They’re pioneering ethical tech. They’re building businesses and driving change across industries. But you’d never know it from panels like this. We need to change that. 👇 Who are the women in AI, tech, and leadership that everyone should know about? Drop their names below. Let’s make them visible. And, if you’re going to have a conversation about the future, make sure it actually includes all of us. Not just because it’s the right thing to do. But because it’s the smart thing to do.

A super-majority of the world's citizens (from 74% in the US to 89% in parts of Europe) want their governments to do more about climate change, but the majority of that group is convinced they're a minority. That perception gap has a silencing effect that in turn enables politicians to think they can continue serving the interests of polluting industries with impunity. The media bears some responsibility for that gap. From taking the bait on false equivalence to creating the fossil fuel industry's advertorials pushing false solutions to treating advocates like criminals, media coverage of climate has often perpetuated the idea that only a fringe minority wants action or is willing to pay for it. In the new Climate Social Science Network book Climate Obstruction: A Global Assessment, Melissa Aronczyk and Max Boykoff led a chapter pulling together the peer-reviewed research on the media's role in climate obstruction, and a new book coming from Michelle Amazeen later this month looks at how advertorials in particular have been used to misinform the public on climate. As I wrote in The Nation, the media contributed to the creation and persistence of the perception gap on climate, but it's also uniquely positioned to close it. https://lnkd.in/gUYRAssR #climate #media

AI is racing ahead. Public trust isn’t keeping up. New global data from Pew Research Center paints a clear picture: people know about AI, but many are uneasy—and who they trust to regulate it varies widely. What stands out • Awareness is high, but not deep: a third have heard “a lot” about AI, nearly half “a little,” and only a small share nothing at all. Awareness strongly correlates with national income. • Concern outweighs excitement: roughly one in three are more concerned than excited, while only one in six are mostly excited. Older adults and women are more likely to express concern. • Trust to regulate AI is local first: most people trust their own country, followed by the EU, the U.S., and then China. • Generational gap: younger adults are more aware and more optimistic; older adults tend to be more cautious. Heavy internet users are also more positive. Why this matters for healthcare • Adoption hinges on trust. If patients and clinicians are uneasy, even the best AI won’t scale. Regulatory trust favors the EU and domestic systems in many markets, so aligning with clear, transparent guardrails is a strategic advantage—not a checkbox. • Equity risk is real. Lower awareness tracks with lower income and less education. If we don’t invest in AI literacy and explainability, we risk widening digital health disparities right when we promise precision care for all. • Design for the skeptic. Older adults and many women report higher concern. Build workflows and communication that demonstrate safety, reliability, and accountability at the point of care—not just in a white paper. • Localize governance. Health systems that operationalize responsible AI with clear data policies, bias monitoring, and audit trails will win trust faster than those waiting for “global consensus.” My take Trust is now a clinical feature. Organizations that treat governance, transparency, and AI literacy as core product capabilities will move from pilots to impact. Those that don’t will stall in the “concern” zone, no matter how advanced the model. At GE HealthCare, our Responsible AI Principles ensure trust in every solution: Safety, Validity & Reliability, Security & Resiliency, Accountability & Transparency, Explainability & Interpretability, Privacy-Enhanced, and Fairness with Bias Managed. Enjoy the Sunday-Read. Your move How are you measuring and building trust in your AI-enabled care pathways—among clinicians, patients, and regulators? Which single change would boost confidence the most in your organization?

𝗧𝗵𝗲 𝗣𝘀𝘆𝗰𝗵𝗼𝗹𝗼𝗴𝘆 𝗼𝗳 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 @ 𝗖𝗘𝗦 𝘄𝗶𝘁𝗵 𝗡𝗘𝗨𝗥𝗔 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀! As a trained Tech Psychologist, I often observe Robotics from a very different angle. Not only through: → code → models → hardware → infrastructure …but through human behavior. Because the moment a robot enters a room, our brain instantly evaluates: → Size → Voice → Facial expressions → Eye contact → Movement → Human-likeness → Emotional behavior → Distance & proximity → Predictability And all of this determines one thing: 𝗗𝗼 𝘄𝗲 𝘁𝗿𝘂𝘀𝘁 𝗶𝘁… 𝗼𝗿 𝗱𝗼 𝘄𝗲 𝗳𝗲𝗮𝗿 𝗶𝘁? Some fascinating psychological concepts behind Robotics: 𝗨𝗻𝗰𝗮𝗻𝗻𝘆 𝗩𝗮𝗹𝗹𝗲 The more human a robot becomes, the more emotionally connected we feel to it until it becomes “almost human.” Then discomfort suddenly appears. This concept became one of the most influential theories in Human-Robot Interaction. 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝘀𝗺 Humans naturally assign emotions, intentions and personalities to machines even when we know they are not conscious. This is a central research topic in Human-Robot Interaction at places like the MIT Media Lab. 𝗧𝗿𝘂𝘀𝘁 𝗧𝗵𝗲𝗼𝗿𝘆 Studies from institutions like Harvard University show that humans can even begin to overtrust robots especially when machines appear socially intelligent or emotionally aware. 𝗦𝗼𝗰𝗶𝗮𝗹 𝗣𝗿𝗲𝘀𝗲𝗻𝗰𝗲 The more socially a robot behaves, the more emotionally humans respond to it. Which means robotics is no longer only engineering. It’s psychology. And maybe that’s exactly the point many companies still underestimate: People don’t use technology because it is technically perfect. They use it because it feels: → intuitive → safe → emotionally understandable → human-centered If not, even the best technology remains unused. That’s why I believe the future of AI & Robotics belongs not only to engineers… …but also to psychologists, designers, behavioral scientists and Tech Translators. So now I’m curious: Would you trust a robot more if it: → looked human or machine-like? → was taller or smaller than you? → spoke emotionally or neutrally? → had a face or no face at all? How do you feel about robots?