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Compounding these issues is that generalist tendencies may hinder the efficiency of AI models when applied to specific tasks. For instance, in practical applications, the classification of all kinds of object classes is rarely required, explains Associate Professor Go Irie, who led the research.
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.
The opportunities afforded by AI are truly significant – but can we trust blackboxAI to produce the right results? Instead of utilizing AI systems that they cannot explain – blackboxAI systems – they could utilize AI platforms that use transparent techniques , explaining how they arrive at their conclusions.
Similarly, what if a drug diagnosis algorithm recommends the wrong medication for a patient and they suffer a negative side effect? 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.
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.
AI is today’s most advanced form of predictive maintenance, using algorithms to automate performance and sensor data analysis. Aircraft owners or technicians set up the algorithm with airplane data, including its key systems and typical performance metrics. Black-boxAI poses a serious concern in the aviation industry.
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 AI model 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.
Understanding the consequences of AI misuse and the challenges of unregulated systems is essential to realising its benefits without harm. Examples of AI Misuse and Consequences AI misuse has led to notable failures with far-reaching impacts. Explainability ensures stakeholders, including end-users, can trust AI outcomes.
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