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Explainable AI: Why is It the Talk of the Town Right Now?
- Explainable AI (XAI) aims to shed light on the inner workings of AI models, enabling users to understand and trust the output and outcomes generated by machine learning models.
- Machine learning algorithms can become black boxes, leading to a lack of transparency and making the reliability, fairness, and credibility of AI systems questionable.
- XAI offers comprehensible justifications for the decisions AI systems make, promoting credibility, accountability, and acceptance.
- Explanations from XAI can enable us to understand and address biases, detect and mitigate errors, provide audit-ready explanations, and empower humans to optimize models effectively.
- Lack of understandability is one of the primary issues facing XAI researchers, along with difficulties in achieving balance between performance and clarity.
- Explanations must be tailored to user-specific needs, promoting a broader understanding of AI’s inner workings.
- Future XAI developments may involve ‘XAI-by-Design,’ which embeds explication techniques directly into AI model architectures, making models more inherently transparent.
- Counterfactual explanations will provide insights into the causal structure underlying the model’s decisions, allowing XAI systems to not only answer ‘what’ but also ‘why.’
- Regulations requiring explainability for certain AI applications will help drive the development of effective XAI techniques, and integrating AI with human expertise is a promising area for the future.
- Kanerika is a leading technology consulting firm specializing in AI, Machine Learning, and Generative AI, with expertise in delivering high-quality, value-driven AI solutions.
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