The UK Research and Innovation's new policy document on how to use AI tools responsibly is probably much more widely appropriate than the intended research funding application process. Some highlights: - Never input sensitive or personal information (without consent) - Consider the risk of bias - Ensure outputs are not falsified, fabricated, plagiarised, misrepresented I also find the assessor angle on not using AI tools to 'outsource' personal evaluation and judgement very relevant but likely hard to apply in practice in many scenarios. One of the core use cases at the moment for LLMs like ChatGPT is summarising meetings, long documents, etc. Without validating the accurateness and completeness of the summary, "AI judgement" is very quickly baked into any subsequent human decision making. #AI #Policy #Ethics #HumanFactors #DecisionMaking The full policy is here: https://lnkd.in/dDD6B3mC
Sven Laqua, PhD FRSA’s Post
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Our Partner, Sajai Singh, has authored an article for CNBC-TV18 titled, “AI's Global Surge — how to navigate the balance between innovation and regulation”. AI is transforming businesses, but its rapid growth has sparked concerns. Debates rage on how to regulate AI ethically. The EU leads the charge with its AI Act, classifying AI risks and setting compliance levels. This trend is likely to be followed globally, with challenges remaining but focusing on balancing innovation with consumer safety. Please click here to read the article: https://lnkd.in/drDPAKvA #jsa #leadinglawfirm #leadinglawyers #ai
AI's Global Surge — how to navigate the balance between innovation and regulation - CNBC TV18
cnbctv18.com
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Human oversight in AI is essential for ensuring trustworthy and reliable outcomes – see how tasq.ai leading the way.
#AI systems are advancing rapidly, but one thing remains clear: #human oversight is key to ensuring that these systems are accurate, fair, and reliable. Human-in-the-loop models are essential for preventing errors, especially in high-stakes industries like finance, healthcare, and data science. At tasq.ai, we combine AI with human expertise to refine outputs, handle edge cases, and ensure trustworthiness, reinforcing the critical role that humans play in the AI development process. This collaboration will continue to shape the future of responsible AI. Ready to take the next step? Contact us! Learn more about why human-in-the-loop is critical to AI development : https://lnkd.in/dHmUJdWT
Why Keeping Humans in the Loop Is Critical for Trustworthy AI
datanami.com
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This release marks a significant shift in the AI development landscape, emphasizing transparency and regulatory compliance alongside performance. Aleph Alpha's decision to open-source models positions them as a pioneer in EU-compliant AI development, inviting scrutiny and collaboration. By releasing both a standard and an "aligned" model, Aleph Alpha showcases its commitment to responsible AI development. The aligned model, having undergone additional training to reduce harmful outputs and biases, highlights the company's dedication to ethical practices. This dual-release strategy not only allows for the study of alignment techniques on model behavior but also has the potential to advance the field of AI safety. Aleph Alpha's approach could prove strategically advantageous amidst growing regulatory pressure and public demand for ethical AI practices. https://lnkd.in/gc8XSW85 #AITransparency #EUAI #AICompliance #AIDevelopment #RegTech
Aleph Alpha unveils EU-compliant AI: A new era for transparent machine learning
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AI, a double-edged sword, the vast potential it offers alongside notable pitfalls like the proliferation of misinformation. To steer AI towards positive outcomes, key steps include emphasizing digital literacy, fostering ethical AI practices, ensuring platform responsibility, and fostering collaborative problem-solving. #AI #DigitalLiteracy #EthicalAI
How to Prepare for AI-Generated Misinformation
insight.kellogg.northwestern.edu
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Regulating AI alone won’t solve the misinformation problem. As Yaniv Makover argues, 'The focus should be on regulating misinformation itself, whether it comes from an AI platform or a human source.' 🔍🤖 Read more below on how orgs can stay ahead of mis-information while embracing AI's evolution! #AI #Misinformation #TechEthics https://lnkd.in/eiWHtfBw
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🚨 Can we really trust #AI to regulate itself? 🚨 With the explosion of AI models in healthcare and life sciences, the margin for error is razor thin. One misstep could have serious, even life-or-death, consequences. So how do we ensure these powerful, complex systems remain safe, ethical, and compliant with regulations? Human-driven oversight is costly, slow, and increasingly overwhelmed by the sheer volume of AI technologies. But what if AI could help regulate AI? In my latest article, I dive into how large language models (LLMs) like Meta's "LLM-as-a-Judge" could transform regulatory processes, especially within the FDA’s AI lifecycle. Imagine AI systems that continuously monitor and evaluate other AI models, flagging risks in real time, providing transparent reasoning, and evolving alongside regulations. Are we ready to let AI take a larger role in overseeing itself? Can this self-regulation model be trusted? 👉 Read the full article to explore this game-changing possibility and how it could reshape the future of AI #governance. #AI #AIRegulation #LLMs #AITrends #Healthcare #AIEthics #RegulatoryCompliance #LifeSciences #Innovation
Can AI Regulates Itself? How LLM-as-a-Judge Could Revolutionize FDA’s AI Lifecycle
medium.com
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AI It depends how it is used #AI
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Regulating AI can be straightforward, with eternal vigilance
weforum.org
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The #IntelligentAge demands a shift from isolated #AI models to #CompositeAI systems by combining specialized capabilities across industries and regions. Uljan Sharka, the Founder & CEO of iGenius outlines ways to make it happen. #WEF25 https://lnkd.in/gaMn4iay
Why composite AI in the Intelligent Age leads us to a people-centred future
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To anyone who prompts a chatbot, take an ethical "HUMAN" approach: H - Halt: Pause to critically evaluate the ethical implications of using Generative AI for the task at hand. U - Utilize: Effectively leverage AI tools to enhance productivity and creativity while ensuring responsible usage. M - Modify: Adapt prompts and approaches based on feedback and outcomes to improve the relevance and accuracy of AI-generated content. A - Assess: Continuously evaluate the effectiveness and ethical considerations of AI outputs to ensure they align with desired standards. N - Note: Document insights and lessons learned from AI interactions to foster transparency and accountability in future uses
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Did you know that a recent study by McKinsey & Company highlighted that 84% of organizations are concerned about bias in their AI algorithms? However, there's a solution to this problem. Upholding best practices can significantly mitigate biases in AI for enterprises, particularly given the challenges posed by compliance and the rapid dissemination of information through digital media. In this E42 Blog post, we delve into an array of best practices to mitigate bias and hallucinations in AI models. A few of these best practices include: 1️⃣ 𝐌𝐨𝐝𝐞𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: This practice focuses on enhancing model performance and reducing bias through various optimization techniques 2️⃣ 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: This involves a deep dive into the structure of AI models to identify and rectify biases 3️⃣ 𝐇𝐮𝐦𝐚𝐧 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬: This emphasizes on the critical role of human feedback in the training loop in ensuring unbiased AI outcomes 4️⃣ 𝐎𝐧-𝐏𝐫𝐞𝐦𝐢𝐬𝐞𝐬 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐋𝐋𝐌𝐬): This practice involves utilizing on-premises LLMs to maintain control over data and model training processes Read the full piece here: https://bitly.cx/folg #genai #generativeai #llms #aimodels #artificialintelligence #largelanguagemodels #genaipractices #ai #automation #E42
Best Practices to Implement when using Gen AI and LLMs
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Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
5moThe real challenge lies in defining "accuracy" and "completeness" for summaries, especially when dealing with nuanced or subjective content. LLMs often excel at capturing surface-level information but struggle with deeper contextual understanding. This can lead to biased or incomplete summaries that inadvertently influence human decision-making. You talked about the assessor angle on not using AI tools to 'outsource' personal evaluation and judgement very relevant but likely hard to apply in practice in many scenarios. Imagine a scenario where an LLM is tasked with summarizing legal documents for a court case, how would you technically use the concept of "accuracy" and "completeness" to ensure the generated summary captures all relevant legal nuances and avoids potential misrepresentation?