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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. Imagine an AI predicting home prices.
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 AI models 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.
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.
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?
When developers and users can’t see how AI connects data points, it is more challenging to notice flawed conclusions. Black-boxAI poses a serious concern in the aviation industry. In fact, explainability is a top priority laid out in the European Union Aviation Safety Administration’s first-ever AI roadmap.
Challenges in Unregulated AI Systems Unregulated AI systems operate without ethical boundaries, often resulting in biased outcomes, data breaches, and manipulation. The lack of transparency in AI decision-making (“black-boxAI”) makes accountability difficult.
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