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NIST AI RMF Map 3.2: What is the "human cost" of your AI system?
Today, I move to a sub-category that often separates the "compliant" from the truly "responsible": Map 3.2.
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NIST AI RMF Decoded: Map 3.1 – Assessing Intended AI Benefits
NIST AI RMF Map 3.1: "Potential benefits of intended AI system functionality and performance are examined and documented"
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Consent or manipulation?
A retail AI agent 🤖: "I've created a meal plan for the week and added 47 items to your cart totaling $287. Ready to checkout?"
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Risk Thresholds for Frontier AI: Paper review
Paper review: how to set risk thresholds in frontier AI development?
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Moral alignment for LLM agents: Paper review
Paper review: how to ensure AI objective match ours through fine-tuning on th Prisoners' Dilemma game?
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Data quality issues as an operational risk
When assessing risks stemming from AI use in your organisation, remember that one of the major operational risks is data quality issues. Why? Poor data quality can cause incorrect decisions, inefficient operations, compliance issues, or reduced productivity. This is why it is important to assess the likelihood and impact of
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Level zero: my thoughts on Global Responsible AI Initiative launch event
On 14 July 2023, I attended the launch of the Global Responsible AI Initiative that I attended last night at Queensland AI Hub, and here is my human-generated summary.
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AI-generated children's books, creepy and harmful
YouTube Kids, a platform designed to provide kids with age-appropriate video content, is known for feeding children some low-quality and even disturbing videos, thanks to its algorithms, when not continuously controlled by a parent (and sitting next to your child when they watch YouTube often negates the whole
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Exploring the future frontiers of NLP research (paper review)
This blog post is a summary of a paper on the future of Natural Language Processing (NLP) research.
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NLP project #1: All they need is a nap
What do moms really want as a gift? I ran a fun NLP experiment to find out.