The promise of artificial intelligence is compelling: Faster computations, improved data analytics, and solutions to previously intractable global problems. But serious challenges with these technologies remain, including possible built-in biases against women and people of color. In the first part of a series on the pitfalls and promises of AI, we examine bias in AI systems and workplaces, how women are addressing inequities, and the growing role of regulations. Learn more in the latest issue of SWE Magazine: https://bit.ly/3RK57Fe #SWEMagazine #ArtificialIntelligence #AIBias #AIChallenges #AI #WomenInSTEM
Society of Women Engineers’ Post
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🧠 Decoding AI Bias: Unraveling the Challenge and Solutions 🤖 Understanding AI Bias: Ever wondered why AI systems sometimes exhibit bias, leading to discriminatory outcomes? Let's unravel this complexity. Consider the example where an AI, trained on internet text, inadvertently learns and reinforces stereotypes. 📚 The Analogy Dilemma: Man to Woman vs. Computer Programmer to Homemaker: An eye-opening example showcases how an AI, asked to reason analogically, reflects biases present in its training data. When prompted with analogies like "man is to computer programmer as woman is to what?," the AI's response exposes gender bias, associating women with roles like homemaker. 🔍 Technical Underpinnings of Bias: AI stores words as numerical representations derived from statistical analysis of internet data. The bias emerges when certain numbers are associated with stereotypes, affecting the outcomes of analogical reasoning. 🌐 Real-world Impact of Bias: Biased AI systems can have far-reaching consequences, from discriminatory hiring tools to facial recognition systems favoring specific ethnicities. Companies have a responsibility to address these issues to ensure fairness and equality. 💡 Mitigating Bias in AI: Efforts to reduce bias in AI are underway. Researchers propose technical solutions, such as zeroing out numbers associated with bias, and using more inclusive and diverse datasets. Systematic auditing and transparency in AI processes are critical to identifying and addressing biases. 👩💻 The Role of Diversity in Diminishing Bias: Building a diverse workforce is a key strategy to reduce bias. Diverse perspectives contribute to spotting problems, making data more inclusive from the outset, and fostering less biased applications. 🌐 Optimism in Bias Reduction: While the challenge of eliminating bias in AI persists, there's optimism. AI systems offer better prospects for bias reduction than human systems. Leveraging technical solutions and continuous improvement, society can strive for fair and unbiased decisions through both human and AI processes. #AI #BiasInAI #TechEthics #DiversityInTech #Innovation #FutureTech
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Experienced Executive in AI & Software | Sales, Growth & Business Development | CBM (Certified Board Member)
The Future of AI at Singularity University: “Every company is an AI company” What an inspiring week it has been at Singularity University in Silicon Valley! I had the privilege to participate in 'The Future of Artificial Intelligence' Executive Program alongside 100 fellow AI innovators representing over 30 countries. Here are some key takeaways Agenda and Program: The event spanned three full days, each packed with insightful presentations and workshops structured around a well-thought-out agenda: - Understanding the Current State of AI - Identifying AI Opportunities - Planning Beyond the Horizon Discussions on AI's exponential growth was a central theme: - Computational capacity doubles every 18-24 months - Data volume doubles every 12 months - AI model intelligence advances every 3-8 months - Recognizing data as the most valuable asset, while acknowledging associated risks Key conclusions: Inevitability of AI's integration into society and job markets: The best positioned organizations for the future of work are the companies adopting AI that supports and augments their workforce GenAI is in its early development stages There is an urgent need to enhance AI literacy across all levels Emphasis on effective human-AI collaboration for optimal performance and innovation, along with the critical role of data quality, risks of biases and ethics. And finally, as the esteemed AI visionary Ray Kurzweil said it: “Every company is an AI company”. Fantastic hosts and speakers from Singularity University, along with visiting business, scientist and academic speakers. The Computer History Museum in the heart of Silicon Valley provided an inspiring environment. Thank you for the organizers, I can highly recommend this to other AI innovators Alix Rübsaam Aaron Frank Venus Ranieri
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Thanks for sharing your takeaways, Timo Heikkinen - agree with your conclusion that the Future of Work will be won by organizations adopting AI in a manner that augments their workforce. Requires intentional planning to help employees to develop their skills, language and mindset around AI!
Experienced Executive in AI & Software | Sales, Growth & Business Development | CBM (Certified Board Member)
The Future of AI at Singularity University: “Every company is an AI company” What an inspiring week it has been at Singularity University in Silicon Valley! I had the privilege to participate in 'The Future of Artificial Intelligence' Executive Program alongside 100 fellow AI innovators representing over 30 countries. Here are some key takeaways Agenda and Program: The event spanned three full days, each packed with insightful presentations and workshops structured around a well-thought-out agenda: - Understanding the Current State of AI - Identifying AI Opportunities - Planning Beyond the Horizon Discussions on AI's exponential growth was a central theme: - Computational capacity doubles every 18-24 months - Data volume doubles every 12 months - AI model intelligence advances every 3-8 months - Recognizing data as the most valuable asset, while acknowledging associated risks Key conclusions: Inevitability of AI's integration into society and job markets: The best positioned organizations for the future of work are the companies adopting AI that supports and augments their workforce GenAI is in its early development stages There is an urgent need to enhance AI literacy across all levels Emphasis on effective human-AI collaboration for optimal performance and innovation, along with the critical role of data quality, risks of biases and ethics. And finally, as the esteemed AI visionary Ray Kurzweil said it: “Every company is an AI company”. Fantastic hosts and speakers from Singularity University, along with visiting business, scientist and academic speakers. The Computer History Museum in the heart of Silicon Valley provided an inspiring environment. Thank you for the organizers, I can highly recommend this to other AI innovators Alix Rübsaam Aaron Frank Venus Ranieri
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As a discipline and as a practice, #datascience has historically been led by a narrow cohort of people, representing a homogeneous demographic. This lack of diversity has contributed to the (often unconscious) perpetration of #biases and stereotypes, which pose significant risks to society. Increasing #diversity in data science and AI not only mitigates these risks, but also fosters creativity and produces better outcomes. 🌍🤝 ♀️ #Feminist AI can transform the way we approach the #AI industry, by challenging the notion that a few, typically affluent, Western, male individuals should have control over the way AI is developed, designed, and deployed. In honor of #InternationalWomensDay2024, the AI Initiative interviewed Sara Colombo, who leads the Feminist Generative AI Lab at TU Delft and advocates for other voices in data science and AI. “Only by making other voices heard, can we ensure a more fair and equitable development of data science and AI, and the products and services that are built on it.” 🗣️ Read the story here: https://lnkd.in/dn45QekZ
Feminist AI: transforming and challenging the current Artificial Intelligence (AI) Industry
tudelft.nl
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Sales Director | People Leader Supporting a team that drives digital transformation in healthcare | Driving IT automation with AI | Improving customer experience and engagement | Women in tech and ESG advocate
For the more visual among us, here is an insightful collection of charts from the past year showing the development and impact of generative AI. Here are a few things that stood out to me: 🔲 New AI developments could achieve human-level performance in some capabilities decades sooner than previously thought 🔲 Nearly all industries will see the most significant gains from gen AI in their marketing and sales functions 🔲 In terms of healthcare-related advantages, Gen AI has high-value potential in accelerating research and drug discovery 🔲 Though Gen AI can increase the speed of software engineering and development, humans will still be needed to deal with complicated tasks 🔲 When filtering by gender identity, women are using gen AI more than men 🔲 Gen AI-literate people are in high demand, and organizations will have to develop excellent talent management capabilities to attract and retain them 🔲 Gen AI is just a piece of the AI revolution Those are just a few takeaways that caught my eye. I’m interested to hear what stood out to others. #GenerativeAI #Technology #ArtificialIntelligence #AITechnology https://ow.ly/HV4850PLHM0
What’s the future of generative AI? An early view in 15 charts
mckinsey.com
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Founder & Chief Data Scientist @ Zeitios | Harnessing AI for Smarter Decisions? 🧠 | Discover Data-Driven Strategies | AI Decision-Making Expert |
Understanding Implicit Biases in Responsible AI Implicit biases are unconscious attitudes or stereotypes that influence our decisions and actions. In AI, these biases can have significant implications, leading to unfair or discriminatory outcomes. Sources of Implicit Bias in AI Training Data: AI systems learn from historical data, which may contain societal biases. Algorithm Design: Developer choices can unintentionally introduce biases. Deployment Context: The environment where AI is deployed affects its performance across different groups. Implications of Implicit Bias Discrimination: Biased AI systems can lead to unfair practices in hiring, lending, law enforcement, and healthcare. Loss of Trust: Perceived bias can erode trust in AI technology. Inequitable Outcomes: Biases can reinforce societal inequalities. Strategies for Mitigating Implicit Bias Diverse and Representative Data: Ensure training data represents all relevant groups. Bias Detection and Monitoring: Implement tools to detect and monitor bias early. Inclusive Design Practices: Involve diverse teams in AI development. Fairness-Aware Algorithms: Use algorithms designed to be fair and unbiased. Transparency and Accountability: Make AI systems transparent and hold developers accountable. Addressing implicit biases is crucial for developing responsible AI systems that are fair, equitable, and trustworthy. By proactively mitigating biases, we can create AI technologies that benefit all members of society and foster trust and reliability in their applications. Together, let's build a future where AI serves everyone fairly. #ResponsibleAI #EthicalAI #BiasInAI #AIethics #TechForGood #Inclusion #DiversityInTech
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🤖 Exciting News! Explore the World of Artificial Intelligence 🧠 Artificial Intelligence has been making waves, but guess what? We're just getting started! 🚀 Our latest blog post dives deep into the realm of AI, highlighting why we're still in the infancy of this transformative technology. 👉 Read the blog to discover: ✨ The challenges of replicating human intelligence. 🤯 How data bias impacts AI decisions. 🌍 The ethical dilemmas surrounding AI. 🔬 The importance of interdisciplinary collaboration. 🌱 Sustainable AI development. Join us on this journey into the future of AI and learn how it will shape our world. 🌐💡 Read the blog here ➡️ https://lnkd.in/dc9pzVug #ArtificialIntelligence #AI #FutureTech #EthicalAI #Innovation #TechJourney
AI: We're Not There Yet, We've Just Scratched the Surface
njoroge.tomorrow.co.ke
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CEO at CreativeGuru.ai | Revolutionizing Marketing with AI-Driven Engagement and Precision Targeting
Former President Barack Obama is actively advising the White House on their AI strategy, underscoring the technology's increasing importance in shaping the future. He's called for the coding community to join President Biden's AI talent surge, stressing the need for skilled individuals to advance AI and ensure its responsible, ethical use. Obama's involvement signifies the government's commitment to AI innovation, with a focus on diverse workforce involvement. This call to action presents a unique opportunity for personal growth and contribution to national technological advancements. As AI integration grows, clear strategies are paramount to harness its full potential. Let's join in this discussion and share our thoughts on the future of AI. Original article: https://lnkd.in/eUPtVwq2
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“Only by making other voices heard, can we ensure a more fair and equitable development of data science and AI, and the products and services that are built on it.” – Sara Colombo #Feminist AI can transform the way we approach the #AI industry, by challenging the notion that a few, typically affluent, Western, male individuals should have control over the way AI is developed, designed, and deployed. The TU Delft | AI Initiative interviewed Sara Colombo, the co-founder and co-director of the Feminist Generative AI Lab, which is a joint research lab between Delft University of Technology and Erasmus University Rotterdam. The lab serves as a pioneering hub where generative AI, design, and data feminism intersect. As a discipline and as a practice, #datascience has historically been led by a narrow cohort of people, representing a homogeneous demographic. This lack of diversity has contributed to the (often unconscious) perpetration of biases and stereotypes, which pose significant risks to society. 📢 Increasing #diversity in data science and AI not only mitigates these risks, but also fosters creativity and produces better outcomes. Read the story here: https://lnkd.in/dn45QekZ
As a discipline and as a practice, #datascience has historically been led by a narrow cohort of people, representing a homogeneous demographic. This lack of diversity has contributed to the (often unconscious) perpetration of #biases and stereotypes, which pose significant risks to society. Increasing #diversity in data science and AI not only mitigates these risks, but also fosters creativity and produces better outcomes. 🌍🤝 ♀️ #Feminist AI can transform the way we approach the #AI industry, by challenging the notion that a few, typically affluent, Western, male individuals should have control over the way AI is developed, designed, and deployed. In honor of #InternationalWomensDay2024, the AI Initiative interviewed Sara Colombo, who leads the Feminist Generative AI Lab at TU Delft and advocates for other voices in data science and AI. “Only by making other voices heard, can we ensure a more fair and equitable development of data science and AI, and the products and services that are built on it.” 🗣️ Read the story here: https://lnkd.in/dn45QekZ
Feminist AI: transforming and challenging the current Artificial Intelligence (AI) Industry
tudelft.nl
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Calvin Lawrence, a computer engineer with decades of AI experience, highlights the glaring lack of diversity in his field. Despite AI's potential to reshape society, it also perpetuates biases encoded in its data. Lawrence's book, "Hidden in White Sight," delves into AI's role in systemic racism 🤖 Cases like a Black mum's wrongful arrest due to facial recognition errors underscore the technology's flaws. Studies reveal AI's tendency to reinforce harmful stereotypes. OpenAI acknowledges industry-wide bias issues and pledges to mitigate them in model development. Lawrence emphasises the importance of diversity in AI teams to ensure fair representation and inclusive solutions (CNN) 💻 It seems to be an area where progress is being made but we still have a long way to go. The most effective way to improve this technology is via having a uniquely diverse team working on developing the very best technology 📲 #AIAdoption #AIRecruitment #AI
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Visit Leanreimagined.com. Promoter of diversity. Co-author of "Retaining Women in Engineering: The Empowerment of Lean Development". R&D Project Manager, Experience in Lean Development, Scrum, and Six Sigma,
3wI believe that addressing the significant gender disparity within engineering is critical to addressing AI concerns. Currently it will be 350 years to gender parity within engineering. To help address this, with my daughter Alissa Stavig MD we wrote at a book “Retaining Women in Engineering: The Empowerment of Lean Development”. In which we promote the need to change the way engineering is done. I have info at leanreimagined.com