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Researchers from the Tokyo University of Science (TUS) have developed a method to enable large-scale AImodels to selectively “forget” specific classes of data. Progress in AI has provided tools capable of revolutionising various domains, from healthcare to autonomous driving.
Thats why explainability is such a key issue. People want to know how AI systems work, why they make certain decisions, and what data they use. The more we can explainAI, the easier it is to trust and use it. Large Language Models (LLMs) are changing how we interact with AI. Thats where LLMs come in.
This success, however, has come at a cost, one that could have serious implications for the future of AI development. The Language Challenge DeepSeek R1 has introduced a novel training method which instead of explaining its reasoning in a way humans can understand, reward the models solely for providing correct answers.
At the root of AI mistakes like these is the nature of AImodels themselves. Most AI today use “blackbox” logic, meaning no one can see how the algorithm makes decisions. BlackboxAI lack transparency, leading to risks like logic bias , discrimination and inaccurate results.
This week, we are diving into some very interesting resources on the AI ‘blackbox problem’, interpretability, and AI decision-making. Parallely, we also dive into Anthropic’s new framework for assessing the risk of AImodels sabotaging human efforts to control and evaluate them. Enjoy the read!
The adoption of Artificial Intelligence (AI) has increased rapidly across domains such as healthcare, finance, and legal systems. However, this surge in AI usage has raised concerns about transparency and accountability. Composite AI is a cutting-edge approach to holistically tackling complex business problems.
It is very risky to apply these black-boxAI systems in real-life applications, especially in sectors like banking and healthcare. For example, a deep neural net used for a loan application scorecard might deny a customer, and we will not be able to explain why. arXiv: 2003.07132 where n is the sample size.
Principles of ExplainableAI( Source ) Imagine a world where artificial intelligence (AI) not only makes decisions but also explains them as clearly as a human expert. This isn’t a scene from a sci-fi movie; it’s the emerging reality of ExplainableAI (XAI). What is ExplainableAI?
Bias and Inequality AI can also introduce societal issues like exaggerating bias if corporations aren’t careful. Amazon’s scrapped hiring AImodel infamously penalized women’s resumes as the machine learning algorithm expanded on implicit biases within the training data. Thankfully, the world is moving in this direction.
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