Technology And Society

I work in AI. And I worry it’s the biggest risk to women in history. I'm not talking about AI writing assistants. I mean the systems quietly embedding some of the most harmful ideas about women—often without anyone noticing. After hearing Laura Bates speak about her new book today, 'The New Age of Sexism: How the AI Revolution is Reinventing Misogyny', I left the room equally enraged and shaken. But I'm not going to look away. I’m in my 40s. I’ve spent over two decades in male-dominated industries. Like many women, I’ve experienced everyday sexism (and less everyday)—often so normalised I didn’t realise it at the time. Sexism is scaling faster and deeper than ever but it's covert. Because this time, the bias isn’t only cultural, it’s algorithmic, scalable, and it’s invisible—until it’s not. And what's most scary is I work in AI and I didn't realise the scale—so what chance does everyone else have. Here’s what we’re already seeing: – AI girlfriend and chatbot apps—downloaded by hundreds of millions—encourage submissive behaviour and compliance by design. What does this mean for behaviour in the real world? – Images of real women used to create synthetic avatars or s** robots – New profiles of teenage boys on social platforms shown extreme misogynistic content within minutes of joining – AI hiring tools filtering out CVs for words like “netball”—even when anonymised—because they don’t match male-coded patterns of success This is happening now. It’s shaping how women are seen, heard, and valued—online, at work, and in life by hundreds of millions of people today—how many tomorrow? As someone who advises on AI in the workplace, I know this tech has enormous potential to meet so many of our challenges today. It can surface hidden patterns of discrimination. It can improve access to credit, jobs, and healthcare. It can actually support inclusion—if we build it with care. But without ethical guardrails, it will replicate and accelerate the very inequalities we should be solving. Which it is doing right now. Yes we need international regulation to get accountability for companies profiting from misogynistic systems. Yes we need more women in AI (80% of AI firms are male-led) and we need women-led businesses to be invested in to the same level at men. That's going to take a while. Here's what I'm going to do: ✅ Challenge myself, my team and my clients to ask: Who might this tool overlook? Who is it really serving? ✅ Push for transparency, fairness, and safety by design ✅ Support more women to shape, lead, and fund the future of AI ✅ Do everything I can to lobby for regulation To the men reading this: you are huge part of the solution. Boys are being shaped by algorithms—but they listen more to men they know & trust. 📚 Have you read 'The New Age of Sexism' by Laura Bates? What did you think? https://lnkd.in/eTYMZ6MM Thank you Laura.

After a couple of train trips to the CBD last week, I’m midway through 'The New Age of Sexism' by Laura Bates. It has been both fascinating and deeply unsettling to sit with the realities she describes. Bates paints a clear picture of how the inequalities and oppressions of our current world are being “baked into the very foundations” of the digital future we are building at speed. Technologies like the metaverse, AI-generated content, and deepfake pornography are not just neutral tools; they carry forward existing harms, often amplifying them in ways that are harder to prevent, regulate, or even detect. One idea that particularly struck me is her description of the “make it now and fix any safety issues later” approach in emerging tech. This is an attitude we would never tolerate in the offline world and yet, in the online and virtual world, this has become the default operating model. Women and other minoritised groups are, as Bates writes, the “canaries in the coal mine.” Their abuse and suffering provide the early warning signals, the data points that allow companies to tweak systems, while continuing to profit in the meantime. It is a chilling reminder of whose safety is deprioritised when innovation and profits are valued above responsibility. Safety should not be an optional add-on, or a ‘later stage’ consideration. If these technologies really are the foundations of our future society, then safety, equity, and accountability must be treated as the baseline.

[Techno-Patriarchy: How AI is Misogyny’s New Clothes Gender discrimination is baked into artificial intelligence by design and it’s in the interests of tech bros. In my day job, I support our clients using AI to accelerate the discovery of new drugs and materials. I can see the benefits of this technology to the people and the planet. But there is a dark side too. That’s the reason tech -       Disregards women’s needs and experiences when developing AI solutions. -       Deflects its accountability in automating and increasing online harassment -       Purposely reinforces gender stereotypes -       Operationalises menstrual surveillance -       Sabotages women’s businesses and activism I substantiate each of the points above with real examples and the impact on the lives of women.   Fortunately, not all is doom and gloom.   Because insanity is to do the same thing and expect a different outcome, I also share what we need to start doing differently to develop AI that works for women too.   #EthicalAI #InclusiveAI #MisogynisticAI #BiasedAI #Patriarchy #InclusiveTech #WomenInTech #WomenInBusiness

For 60 Years, Kids’ TV Cast Boys As ‘Doers’ And Girls As Passive, Study Suggests: “New research reveals that the language in children’s television is reinforcing harmful gender stereotypes, and that little has improved in 60 years. In some cases, the gender bias is getting worse over time. The study, published this week in Psychological Science, examined scripts from 98 children’s television programs in the U.S. spanning from 1960 to 2018. The researchers employed natural language processing tools to examine which words were more likely to be associated with male characters and which were more likely to be associated with female characters. In total, they analyzed 6,600 episodes, 2.7 million sentences and 16 million words. Among the shows studied were classics like The Flintstones (1960) and more modern series like The Powerpuff Girls (2016) and Lost in Space (2018). In particular, the researchers examined how often male and female characters were portrayed as active agents (those who do) versus passive recipients (those who are done to). They found that boys are ‘doers’ while girls are the ‘done-tos.’ Perhaps most shockingly, when the researchers examined how this language has changed over time, they found that it hadn’t. The gender gap in who takes action in these programs hasn’t improved in six decades. Given the amount of time children spend watching television, the study authors suggest that those who watch these programs will develop biased ideas about how women and men behave in the real world. ‘These biases aren’t just about who gets more lines; they’re about who gets to act, lead, and shape the story. Over time, such patterns can quietly teach children that agency belongs more naturally to boys than to girls, even when no one intends that message,’ professor of psychology at NYU and an author on the paper Andrei Cimpian explained in a press release. AI learning models that train on program scripts pose an additional threat of perpetuating the gender bias. The study authors explain in their paper, “The rising popularity of script-writing programs powered by artificial intelligence (AI), which are trained on language from pre-existing screenplays, adds urgency to the goal of uncovering social biases in the language in children’s media.’ As technology continues to evolve, it becomes increasingly important to understand the messages we’re sending.” Read more 👉 https://lnkd.in/e5g6Z8WF ✍️ Article by Kim Elsesser #WomenInSTEM #GirlsInSTEM #STEMGems #GiveGirlsRoleModels

AI+ War? This is not old tech. It is old intent moving at terrifying new speed ⤵ This is not really about a new military tool. It is about a new pace of war. For years people treated systems like Project Maven as just another defense program. A technical upgrade. A niche intelligence tool. That view misses what is actually changing. The real shift is speed. When software compresses military decisions from hours into minutes, warfare itself begins to change. 1/ ➠ what changed ➝ project maven began as software analyzing drone footage ↳ now it aggregates data from satellites, drones, radar, social media, and intelligence feeds ↳ unified battlespace view ↳ faster awareness ↳ faster action What began as simple image recognition quietly evolved into something far larger. Not just a tool. A decision system. 2/ ➠ why that matters ➝ speed used to be a support advantage ↳ speed is now the advantage ↳ hours become minutes ↳ debate shrinks ↳ action grows This is not a small software upgrade. It is a power shift. The side that sees faster and decides faster reshapes the battlefield before the opponent reacts. 3/ ➠ the real story ➝ people say Maven is old because it started in 2017 ↳ that argument misses the point ↳ technologies often start small ↳ scale changes everything ↳ adoption rewrites impact Today the system has moved far beyond an experimental AI project. It now operates across military branches, combat commands, and allied forces. Old foundation. New consequences. 4/ ➠ what should make people uneasy ➝ the promise is better intelligence, faster targeting, and human oversight ↳ recommendations arrive instantly ↳ approvals must keep pace ↳ oversight shrinks ↳ rubber stamp risk Human in the loop sounds responsible. Until speed quietly turns the loop into a formality. 5/ ➠ the bigger question ➝ this is bigger than one company ↳ bigger than one contract ↳ war becomes a data problem ↳ systems prioritize ↳ humans approve Once warfare becomes a data problem, advantage no longer comes from weapons alone. It comes from systems that decide when to use them. We are not watching AI enter war. We are watching war learn to think like software. ▶ what happens when speed outruns judgment? ▶ who owns mistakes then? ▶ who says no? ▶ who can? ▶ why? // Repost this ⇄ // Source: Project Maven overview https://lnkd.in/d45eMWX9 Palantir Maven Smart System deployments and contracts https://lnkd.in/denba4Am // Follow me for daily posts on emerging tech and growth: https://lnkd.in/gqzS_9Tf //

💡 From Steel to Software: How Weapons Have Become Code-Driven Modern missile systems are no longer defined primarily by propulsion or aerodynamics — but by code. What was once a mechanical or chemical challenge has evolved into a software-defined system, where autonomy, guidance, and decision-making are increasingly driven by embedded algorithms. A “self-controlled” missile today integrates several layers of computational intelligence: - Inertial Navigation and Kalman Filtering for sensor fusion and drift correction. - Computer Vision and Target Recognition using convolutional or transformer-based neural networks. - Adaptive Guidance Laws that use reinforcement learning or real-time optimization to adjust trajectories dynamically. - Mission Management Software that executes conditional logic — deciding, for example, when to re-target, abort, or engage under uncertain data. These systems blur the line between mechanical engineering and autonomous robotics — and between civil and military innovation. The same AI models that enable autonomous vehicles, satellite tracking, or industrial inspection can be repurposed for target identification and dynamic flight control. This is the essence of dual-use technology: innovations born in commercial domains that can rapidly migrate into military contexts through software transfer, not physical manufacturing. This shift transforms defense R&D itself. The critical advantage is no longer only in materials or payloads, but in algorithmic superiority — speed of adaptation, data integration, and software reliability under extreme conditions. As weapons systems become code-centric, the challenge for policymakers, engineers, and ethicists alike is ensuring responsible autonomy — where control, accountability, and safety are not lost in the abstraction of software. In the age of algorithmic warfare, the sharpest edge is no longer steel — it’s software. #Defence #Miltech #Defense #DefenseTechnology #AutonomousSystems #DualUse #AIinWarfare #GuidanceSystems #SoftwareDefinedWeapons #EthicalAI #InnovationSecurity

Technology-facilitated gender-based violence is not a future risk. It is already shaping how women and girls participate in public life. A recent United Nations Population Fund (UNFPA) resource on law and policy reform makes this clear. Technology is now so embedded in daily life that the line between online and offline violence no longer holds. What happens online has real psychological, economic, and physical consequences. What stands out in this guidance is the shift toward accountability. Not only for individuals, but across systems: Laws that reflect the lived realities of survivors Platforms that are responsible for how harm is enabled and amplified Institutions that are equipped to respond with consistency and care The report also reinforces that effective responses must be: Survivor-centred, grounded in safety, dignity, and consent Intersectional, recognizing who is most at risk and why Evidence-based, informed by what is actually happening in digital spaces Perhaps most importantly, it positions TFGBV within a broader continuum of violence. Online harm is not separate. It is part of the same structures of power that shape violence in homes, workplaces, and communities. As digital spaces continue to expand, the question is becoming more urgent. What does accountability look like in a world where violence can be amplified at scale?

Higher education is increasingly adopting AI-driven tools: chatbots for applicant queries, systems for verifying qualifications, and algorithms for shortlisting candidates. While these technologies promise efficiency, they also carry the risk of embedding and obscuring systemic biases. Misogyny, racism, and classism are already embedded in our systems. Add AI to the mix, and those biases don’t disappear, they get scaled. In her latest book, The New Age of Sexism, Laura Bates exposes how emerging technologies, including AI, are reinforcing and amplifying existing gender inequalities. She delves into how AI systems, often trained on biased data, can perpetuate harmful stereotypes and discrimination against women and marginalised groups. Consider these scenarios: - A chatbot providing less comprehensive information to female or international applicants, reflecting historical underrepresentation in training data. - A document verification system disproportionately flagging certificates from certain countries as suspicious. - An admissions algorithm favouring candidates from traditionally privileged backgrounds (by something as simple as giving primacy to A-levels), inadvertently penalising those with non-linear educational paths. These are not hypothetical concerns. For instance: The UK government's AI system for detecting welfare fraud was found to exhibit bias against individuals based on age, disability, marital status, and nationality. Research by Joy Buolamwini revealed that facial recognition systems have higher error rates for darker-skinned women, highlighting the intersection of racial and gender biases in AI technologies. If your institution is integrating AI into recruitment or admissions, it's crucial to ask: Who developed and trained (and continues to train) the model? What data was used, and does it reflect diverse populations? How are biases identified and mitigated? Who is accountable for the decisions made by these systems? Automation doesn't eliminate bias; it often conceals it behind a façade of objectivity. We must critically assess and address the implications of AI in our institutions to ensure equity and fairness. As Laura Bates emphasises, it's not about fixing the individuals affected by these systems but about fixing the systems themselves. How is your institution approaching the integration of AI in a way that promotes inclusivity and mitigates bias? Or, has no one thought about it yet?

If you had an y doubt that gender bias still exists, consider this. Dr. Ben Barres, a renowned neurobiologist at Stanford University who transitioned from female to male. Before transitioning, Ben Barres lived as Barbara Barres, and after his transition, he overheard a colleague remark, “Ben Barres gave a great seminar today. His work is much better than his sister’s.” Of course, there was no sister — they were referring to the same person, before and after transition. The comment powerfully illustrates the unconscious gender bias in science and academia: the exact same work was judged as more competent when the researcher was perceived as male. Dr. Barres went on to speak and write extensively about sexism in science, including in his well-known essay “Does Gender Matter?” published in Nature (2006).