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Senior Manager @ Deloitte | Engineering, AI & Data

𝗗𝗲𝗺𝘆𝘀𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗫𝗔𝗜 🌟🔍 Explainable AI (XAI) is a paradigm that focuses on making AI systems understandable and interpretable by humans, shedding light on the decision-making processes within AI models. It is designed to address the limitations of traditional AI models, which are often seen as "black boxes" that do not provide any insight into how they arrive at their conclusions. Let's unpack XAI through a testing lens 👇 🛡️ Trust: XAI builds trust by providing insights into why AI makes specific decisions during testing, fostering transparency and confidence in AI-driven processes. 🎨 Decoding AI: Techniques like Shapley values and integrated gradients reveal the factors influencing AI decisions, empowering testers to understand and interpret model outputs effectively. 🚀 Future Vision: XAI is reshaping testing by addressing challenges and refining definitions, paving the way for more transparent and efficient testing methodologies. 🤝 Human-AI Collaboration: Embrace the collaborative potential of XAI to strengthen partnerships between humans and AI in quality engineering, leading to improved testing strategies and outcomes. #ExplainableAI #SoftwareTesting #InnovationInTech

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