Understanding Diverse Viewpoints on AI Technology

As AI weaves itself into the fabric of our lives, we have a tendency to assume that all of us want the same things from AI. A recent study from Stanford HAI reveals that our cultural background significantly influences our desires and expectations from AI technologies. European Americans, deeply rooted in an independent cultural model, tend to seek control over AI. They want systems that empower individual autonomy and decision-making. In contrast, Chinese participants, influenced by an interdependent cultural model, favour a connection with AI, valuing harmony and collective well-being over individual control. Interestingly, African Americans navigate both these cultural models, reflecting a nuanced balance between control and connection in their AI preferences. The importance of embracing cultural diversity in AI development cannot be understated. As we build technologies that are increasingly global, understanding and integrating these diverse cultural perspectives is essential. The AI we create today will shape the world of tomorrow, and ensuring that it resonates with the values and needs of a global population is the key to its success. When designing technology solutions, we must think beyond our immediate cultural contexts and strive to create systems that are inclusive, adaptable, and culturally aware. If OpenAI wants to benefit humanity, then that needs to be humanity with all our different world views. The key takeaways from the study can apply to all kinds of product development: 1. Cultural Awareness: recognise that preferences vary across cultures, and these differences should inform design and implementation strategies. 2. Inclusive Design: incorporate diverse perspectives from the outset to create products that resonate globally. 3. Global Leadership: lead with an understanding that what works in one cultural context might not in another—adaptability is key. By embedding these principles into our product development efforts, we can ensure that the technology and products we develop are culturally attuned to the needs of a diverse world. I would love to see deeper analysis of this cultural lens as it should inform the way we work with technology for good. There is always a danger that as we seek to break one set of biases, we introduce our own. How do you think leaders should adapt their AI approaches or precut development on the basis of this research? #AI #product #research #techforgood #responsibleAI Enjoy this? ♻️ Repost it to your network and follow me Holly Joint 🙌🏻 I write about navigating a tech-driven future: how it impacts strategy, leadership, culture and women 🙌🏻 All views are my own.

My "AI is not cheating" post hit a nerve this week. After 148 comments (and counting), fascinating debates, and a lot of emotion, here's what I learned from analyzing every single comment: 🔹 52% of you generally agreed that AI should be embraced in education (with guardrails) 🔹 30% strongly disagreed 🔹 18% were skeptical or neutral But here's the fascinating thing: The camps weren't just divided, they were emotionally polarized. 🔹 Pro-AI commenters (mostly tech professionals, founders, AI specialists) were calm, business-focused, and shared practical examples. 🔹 Anti-AI commenters (mostly educators, creatives, linguists) were passionate and protective, clearly driven by deep concern for students' cognitive development. This revealed something important: This isn't just about technology adoption. It's about fundamentally different beliefs about how humans learn. 🔹 One camp sees AI as a learning accelerator similar to calculators that freed us from arithmetic to tackle complex math. 🔹 The other sees it as a learning disruptor, undermining the cognitive struggle that IS learning. Both sides care deeply about students. Both have valid concerns. My takeaway: We're not going to solve this with more studies or better AI tools. We need to: → Acknowledge the fundamental disagreement about what constitutes learning → Design education systems that preserve cognitive development while preparing students for an AI world → Stop treating this as a simple adoption challenge and start treating it as a values conversation The passion in yesterday's comments tells me we're dealing with something much deeper than technology. We're grappling with what it means to think, learn, and grow as humans. Thank you to everyone who shared their perspective. Even (especially) those who disagreed with me. What do you think? Can we find common ground, or are these worldviews fundamentally incompatible? Barry O'Sullivan, Jim Amos, Christopher Johnson, Dr. Sabba Quidwai, James Moed, Danielle Favreau, Yuriy B., Kay Dawson, and Phil Woodford You all brought such thoughtful and passionate perspectives to the original discussion, representing the full spectrum of viewpoints on this complex issue. Would love to hear your reflections.

Can Artificial Intelligence be racist? Rona Wang, an inspiring MIT graduate, recently shared her thought-provoking experience with artificial intelligence (AI). In her encounter with an AI image creator, Rona, an Asian-American, sought to generate a "professional" headshot for her LinkedIn profile. However, she was taken aback when the AI-generated image presented her with features that resembled those of a Caucasian individual, with lighter skin and blue eyes. This incident has prompted quite a few conversations about whether the AI was racist... However, I think we need to realise that AI today is merely a reflection of the biases of its human creators! AI is not intelligent on its own, rather, it operates as an aggregator of the data it is given by humans. Currently, most AI algorithms and models are built on datasets from majority white countries, which inadvertently perpetuate their biases. This leads to the biased outcomes shown in Roma's experience, where "professionalism" is associated with specific skin and eye colours. I don't think that technology is inherently evil. Instead, I believe that Rona's experience highlights the need for increased diversity and inclusion in the development of AI and future technologies. All humans have biases and it's only by working and learning together that we can effectively address them. Especially as AI is becoming more and more mainstream, we need to recognise incidents like Rona's as cautionary tales, as biases like these can lead to far greater consequences as AI grows. By centring inclusion and diversity, we can mitigate the risk of perpetuating unconscious biases and ensure that technology serves everyone equitably. I believe that together we have the power to shape AI into a force for positive change. #AI #Diversity #Inclusion #Equity #Equality #Intersectional #Culture #Technology #Discrimination #Race #Racism #ArtificialIntelligence #ChatGPT #OpenAI #Midjourney #StableDiffusion #Tech #Ethics #Bias [Image Description] A square image with text and two photos beneath the text. The text reads "An MIT student asked an AI to make her LinkedIn headshot more "professional". It gave her lighter skin and blue eyes." The two photos are of Rona Wang, an Asian-American girl wearing a maroon t-shirt. The left photo is her original photo where she has dark brown eyes and hair. The right the AI-generated photo where she is edited to have lighter skin and blue eyes.

This summer, I had the opportunity to interview more than 40 talent and learning leaders from Fortune 1000 companies. While much of our conversations centered on their role in shaping the future of AI in the workplace, I also wanted to explore broader, more provocative questions. One of my favorites was this: “What are we not talking about with respect to AI, that we should be talking about?” Leaders shared a wide spectrum of perspectives, from overlooked risks to untapped opportunities. A few of their responses stood out to me: ➡️ Preparing For New Roles: While job displacement often dominates the narrative, there has been less attention paid to the new roles that AI will create. What will these roles look like, and how can we begin preparing employees today? ➡️ The Human vs Machine Speed Gap: Several leaders cautioned that traditional approaches to upskilling and reskilling might be important but ultimately, too slow to keep up with the pace of AI advancement. As one leader asked, “what happens when through reinforcement learning and other techniques, machines can upskill faster than humans. Then what do we do?” ➡️ Governance and Trust: Multiple leaders voiced concern around the intersection of governance, ethics, regulation, and how they are lagging far behind innovation, and while this might be outside of the sphere of influence of the workplace, it still matters. “Our workplaces are a reflection of our society, so it’s one and the same” a leader shared ➡️ Big Player/Market Fragmentation: A fragmented vendor landscape risks inflating hype cycles and creating confusion for buyers. Leaders warned that if there is a dip or macro funding squeeze, rapid consolidation could slow innovation and a few dominant players could make choices that hinder innovation. “If AI evolves too fast for organizations to capture its benefits, a few players may eventually dominate and slow innovation.” ➡️ The Employee Lens: Too often, AI conversations are framed almost exclusively around business outcomes, efficiency, productivity, cost savings. While those are important, far fewer discussions focus on what employees need to adapt, grow, and thrive in this new reality. As one leader put it: “Businesses exist because they create value exchanges between humans. If we accelerate technology at the expense of people, what are we really building—and who is it for?” These are just a few of the many  responses I heard, but I’d love to hear from you. What do you think we’re not talking about in the AI conversation that we should be? P.S. If these insights sparked your interest and you’d like a full copy of the report, drop me a comment or send me a message

I recently shared insights with SPH Media's Lianhe Zaobao 联合早报 on OpenAI's decision to restrict API access in China. As an MBA graduate from Guanghua School of Management, Peking University, with experience in both Western and Chinese tech landscapes, I'd like to offer a comprehensive perspective on this development and its global implications. Key takeaways and deeper insights: 1) 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞𝐝 𝐝𝐨𝐦𝐞𝐬𝐭𝐢𝐜 𝐀𝐈 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: OpenAI's restrictions may catalyze China's AI industry rather than hinder it. Chinese tech giants and emerging AI companies are already capitalizing on this opportunity to attract displaced OpenAI users. 2) 𝐋𝐨𝐜𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐬 𝐚 𝐤𝐞𝐲 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐭𝐨𝐫: While core LLM technology is similar globally, the real value lies in cultural adaptation: ● Western models align with Western perspectives ● Chinese models are tailored to local cultural norms and values. This trend extends beyond China, with projects like Singapore's SEA-LION developing AI models for Southeast Asian languages and cultures. 3) 𝐆𝐥𝐨𝐛𝐚𝐥 𝐀𝐈 𝐟𝐫𝐚𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: We may see an increasingly divided AI landscape reflecting geopolitical tensions, potentially leading to region-specific AI models for global enterprises. This could result in distinct AI ecosystems tailored to local markets, values, and regulations. 4) 𝐃𝐚𝐭𝐚 𝐬𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧𝐭𝐲 𝐚𝐧𝐝 𝐀𝐈 𝐥𝐨𝐜𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: The restrictions highlight the growing importance of data sovereignty, with countries prioritizing AI models trained on local data for cultural relevance and regulatory compliance. 5) 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐠𝐥𝐨𝐛𝐚𝐥 𝐭𝐞𝐜𝐡 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: Multinational corporations may need to adapt their AI approaches, potentially using different models for different markets. This could increase complexity in global tech operations and spur innovation through regional competition. These developments underscore the need for a nuanced understanding of the evolving global AI landscape. My experience in both Western and Chinese tech environments provides a unique lens through which to analyze these trends and their implications for businesses worldwide. I'm open to further discussions, keynote speaking opportunities, or interviews on this critical topic. Let's explore how these changes in the AI landscape will shape the future of technology and business globally. Link to full article: https://lnkd.in/g3h3U9Zc What are your thoughts on the future of AI development across different regions? How might this impact global innovation and cross-cultural collaboration in tech? #AI #GlobalTech #AIInnovation #TechStrategy #ChineseAI

Hi Alan - your insights into the transformative impact of #generativeAI on patient empowerment and the evolving role of healthcare providers are both timely and thought-provoking. As a radiologist with academic and industry experience in AI, I'd like to share some perspectives: 1️⃣ The Crucial Role of Regulation: While rapid innovation in consumer-driven AI is exciting, regulatory frameworks are vital for ensuring patient safety and data privacy. Medical imaging data, for instance, cannot be open-sourced due to ethical and legal constraints. Regulations aren't obstacles but safeguards that maintain trust in medical advancements. 2️⃣ Innovation Within Regulated Spaces: Significant advancements are happening within the regulated healthcare sector. AI technologies are improving diagnostics and treatment planning while adhering to safety and effectiveness standards. Large vision language models have immense potential but must be developed responsibly to ensure accuracy and reliability. 3️⃣ Balancing Speed with Responsibility: In healthcare, we cannot afford to compromise safety for speed. A measured approach allows for thorough validation of AI tools, ensuring they meet the rigorous standards required for clinical use. 4️⃣ The Evolving Role of Physicians: The shift from authoritative figures to collaborative partners is a positive evolution. Physicians bring invaluable expertise in interpreting complex medical data and understanding patient care nuances. Our role is increasingly about guiding patients through information to make informed decisions. 5️⃣ Synergy Between Communities: I envision a future where consumer-driven and regulated sectors collaborate closely. Combining the agility of the open source community with the rigor of the regulated community can yield AI solutions that are both innovative and trustworthy. 6️⃣ Ethical Considerations and Trust: Ethical development of AI must be at the forefront. Transparency in AI decision-making, respecting patient privacy, and ensuring data security are essential for building trust with patients and providers alike. Generative AI holds immense promise for transforming healthcare into a more personalized and patient-centered system. Realizing this potential requires a collaborative approach that values both innovation and responsibility. It's an exciting time in healthcare, and I am optimistic about the positive changes that thoughtful use of AI can bring. Let's continue the conversation to shape a future where technology enhances the human aspects of care. With my background in radiology and AI, I recognize the complexities of integrating #GenAI into healthcare. It's imperative that we, as professionals and innovators, work together to develop these technologies responsibly. By fostering open dialogue and collaboration, we can fully leverage the opportunities that GenAI presents while ensuring patient well-being remains the priority.

🔊 Pleased to share that the “Developing Critical AI Cultures" report is now out!” The online dialogue co-designed and hosted by Diverse AI and the “Patterns in Practice” team from the University of the West of England is now available. The dialogue was aimed at hearing the viewpoints of different AI practitioners representing various global communities on their perception of AI, and how it affects their communities - positively and / or negatively. Different communities were represented, ranging from race and nationalities, to the deaf and blind communites, nuerodivergent groups, Gen-Z, and so on. Based on this project, I'm happy to share that Diverse AI will continue with this important work by re-creating datasets through community participatory research to ensure the inclusion of different voices from vulnerable, underrepresented group in AI design and development. More on this to come soon! You can access the report here 👉🏽 https://lnkd.in/eb9dBFMr. 👏🏼 Huge shoutout to the team who made this happen! Samborne Bush Dr Erinma Ochu Jo Bates Steph Wright Chinonye Dianne Pat-Ekeji #ai #responsibleai #aicommunities #diversityinai

🤔Rethinking Intelligence in the Age of AI 👉 In a recently published Noema article, Blaise Aguera y Arcas and James Manyika capture something profound: We are not just building new technologies—we are confronting a new paradigm of what it means to be intelligent. They urge us to reimagine intelligence as emergent, relational, and distributed — not just artificial. 💻 Recommended Reading: "AI Is Evolving — And Changing Our Understanding of Intelligence": https://lnkd.in/ebAVdGrx 🤔 Similarly, we anticipated this shift in our paper “Emerging Uses of Technology for Development: A New Intelligence Paradigm”, where we argue that to truly harness the potential of today’s technologies for public good, we must move beyond artificial intelligence alone and recognize four forms of intelligence: 🔹 Data Intelligence — turning data and information into insight 🔹 Artificial Intelligence — advancing analysis and pattern recognition 🔹 Collective Intelligence — crowdsourcing ideas, wisdom, and decision-making 🔹 Embodied Intelligence — deploying smart tools in the physical world Together, these intelligences form the architecture of a new decision-making model—one that’s distributed, contextual, and participatory. 💻 Read: "Emerging Uses of Technology for Development: A New Intelligence Paradigm": https://lnkd.in/gPDtEx2e 👉 What does this mean for those working toward advancing the public interest? It means decision-makers must recognize the plurality of intelligences available to them — and intentionally and responsibly design systems that combine them for greater insight, legitimacy, and impact. 📌 It's not about replacing human intelligence. It’s about weaving together different types of intelligence to create new capabilities that can drive social progress. 📌It's about moving beyond “AI as automation” toward “intelligence as collaboration.” #AI #DataForGood #CollectiveIntelligence #DataGovernance #Tech4Good #IntelligenceParadigm #AIethics #SDGs #Intelligence

At a moment when the leaders of dominant AI firms are openly aligning with Trumpism, I'm proud to share a new report from Coding Rights & One Project. AI Commons: nourishing alternatives to Big Tech monoculture By Joana Varon, Sasha Costanza-Chock, Mariana Tamari, Berhan Taye, and Vanessa Koetz ‘Artificial Intelligence’ (AI) has become a buzzword, with tech companies, research institutions, and governments all vying to define and shape its future. How can we escape the current context of AI development where powerful forces are pushing for models that, ultimately, automate inequalities and threaten socio-environmental diversity? What if we could redefine AI? What if we could shift the production of AI systems away from the hegemonic capitalist model towards more disruptive, inclusive, and decentralized models? Can we imagine and foster an AI Commons ecosystem that challenges the dominant logic of an 'AI arms race?' An ecosystem that might encompass researchers, developers, and activists who are thinking about AI from decolonial, transfeminist, antiracist, indigenous, decentralized, post-capitalist and/or socio-environmental justice perspectives? This field scan, commissioned by One Project and conducted by Coding Rights, aims to understand the (possibly) emerging “AI Commons” ecosystem. The authors identify 234 key entities (organizations, cooperatives and collectives, networks, companies, projects, and others) from Africa, the Americas, and Europe that are building various components of alternative possible AI futures - potential seeds of an AI Commons ecosystem. The report identifies nascent but powerful communities of practice that already produce nuanced criticism of the Big Tech-driven AI development ecosystem, while they also imagine, develop, and, at times, deploy AI systems informed and guided by commitments to decoloniality, feminism, antiracism, and post-capitalism.