The Impact of AI on Data Accuracy

AI is only as good as the data you train it on. But what happens when that data is flawed? 🤔 Think about it: ❌ A food delivery app sends orders to the wrong address because the system was trained on messy location data. 📍 ❌ A bank denies loans because AI was trained on biased financial history 📉 ❌ A chatbot gives wrong answers because it was trained on outdated information. 🤖🔄 These aren’t AI failures. They’re data failures. The problem is: 👉 If you train AI on biased data, you get biased decisions. 👉 If your data is messy, AI will fail, not because it's bad, but because it was set up to fail. 👉 If you feed AI garbage, it will give you garbage. So instead of fearing AI, we should fear poor data management. 💡 Fix the data, and AI will work for you How can organizations avoid feeding AI bad data? ✔ Regularly audit and clean data. ✔ Use diverse, high-quality data sources. ✔ Train AI with transparency and fairness in mind. What do you think? Are we blaming AI when the real issue is how we handle data? Share your thoughts in the comments! #AI #DataGovernance #AIEthics #MachineLearning -------------------------------------------------------------- 👋 Chris Hockey | Manager at Alvarez & Marsal 📌 Expert in Information and AI Governance, Risk, and Compliance 🔍 Reducing compliance and data breach risks by managing data volume and relevance 🔍 Aligning AI initiatives with the evolving AI regulatory landscape ✨ Insights on: • AI Governance • Information Governance • Data Risk • Information Management • Privacy Regulations & Compliance 🔔 Follow for strategic insights on advancing information and AI governance 🤝 Connect to explore tailored solutions that drive resilience and impact -------------------------------------------------------------- Opinions are my own and not the views of my employer.

Article from NY Times: More than two years after ChatGPT's introduction, organizations and individuals are using AI systems for an increasingly wide range of tasks. However, ensuring these systems provide accurate information remains an unsolved challenge. Surprisingly, the newest and most powerful "reasoning systems" from companies like OpenAI, Google, and Chinese startup DeepSeek are generating more errors rather than fewer. While their mathematical abilities have improved, their factual reliability has declined, with hallucination rates higher in certain tests. The root of this problem lies in how modern AI systems function. They learn by analyzing enormous amounts of digital data and use mathematical probabilities to predict the best response, rather than following strict human-defined rules about truth. As Amr Awadallah, CEO of Vectara and former Google executive, explained: "Despite our best efforts, they will always hallucinate. That will never go away." This persistent limitation raises concerns about reliability as these systems become increasingly integrated into business operations and everyday tasks. 6 Practical Tips for Ensuring AI Accuracy 1) Always cross-check every key fact, name, number, quote, and date from AI-generated content against multiple reliable sources before accepting it as true. 2) Be skeptical of implausible claims and consider switching tools if an AI consistently produces outlandish or suspicious information. 3) Use specialized fact-checking tools to efficiently verify claims without having to conduct extensive research yourself. 4) Consult subject matter experts for specialized topics where AI may lack nuanced understanding, especially in fields like medicine, law, or engineering. 5) Remember that AI tools cannot really distinguish truth from fiction and rely on training data that may be outdated or contain inaccuracies. 6)Always perform a final human review of AI-generated content to catch spelling errors, confusing wording, and any remaining factual inaccuracies. https://lnkd.in/gqrXWtQZ

Will bad data lead to AI model collapse? Researchers seem to think so. Believe it or not, there’s only so much real data in the world. And for AI to get better, it needs a lot of it. The picture represented (credit The New York Times) shows a series of “hand-written” AI generated numbers after just one model generation being trained on its own AI-generated data. After 30 generations? The output is unrecognizable. But, what would happen if that data was wrong to begin with? Those models wouldn’t break down in one generation—the outputs would be garbage on day one. Whether we’re talking about synthetic datasets or your own first-party data, broad data quality coverage and specialized machine learning monitors based on historic distribution data is and will forever be your best defense against inaccurate and anomalous production data. If you want to protect your data products—and the consumers that depend on them—you need to: - Set standards. - Profile your data. - Leverage scalable monitoring. - And measure performance. Trusting the data has never been easy. And in a world of AI-everything, solving that problem has never been more complicated—or necessary. What are your thoughts? Let me know in the comments! #genai #dataquality #dataobservability

𝗪𝗵𝗮𝘁 𝗽𝗶𝘇𝘇𝗮 𝗮𝗻𝗱 𝗰𝗵𝗲𝗲𝘀𝗲 𝘁𝗲𝗮𝗰𝗵 𝘂𝘀 𝗮𝗯𝗼𝘂𝘁 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: LLM providers have been training their models on public data, for example from Twitter and Reddit, leading to concerns over the contents they’ve learned from. So, they have been striking licensing deals with content providers to get access to their data — and that creates new challenges. Datasets obtained from the public Internet contain false information, sarcasm, and potentially harmful content. Given that Generative AI, unlike humans, has no understanding of common sense and nuance, this can backfire quickly. An AI-augmented Google search has recently recommended: adding non-toxic glue to your pizza to prevent the cheese from sliding off. (Don’t try this at home.) The Internet has traced the information back to a decade-old thread on Reddit that the model has presumably processed and incorporated into its AI-generated output. Think about autonomous agents that will book your travel, negotiate a contract with your supplier, or provide information about your products, parts, and warranties. Mishaps for any of these examples due to bad data can have a real impact on your business — from ending up in the wrong location at the wrong time to overpaying, causing damage to your customers’ assets, and more. Spending extra effort to review, clean, and correct your datasets remains key. So does attributing generated information to the exact source document or dataset. That way, your users have a reference point to verify if the generated output is actually correct. Otherwise, you might end up with the equivalent business outcome of suggesting to add glue to prevent cheese from sliding off of your pizza. A sticky situation. Read the article 👇🏻 for the full details and get the next one in your inbox tomorrow. 𝗜𝘀 𝘁𝗵𝗲 𝗼𝗹𝗱 𝘀𝗮𝘆𝗶𝗻𝗴 𝗲𝘃𝗲𝗿 𝗺𝗼𝗿𝗲 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁? —> “𝘋𝘰𝘯’𝘵 𝘵𝘳𝘶𝘴𝘵 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 𝘺𝘰𝘶 𝘳𝘦𝘢𝘥 𝘰𝘯 𝘵𝘩𝘦 𝘐𝘯𝘵𝘦𝘳𝘯𝘦𝘵.” #ArtificialIntelligence #GenerativeAI #IntelligenceBriefing