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Artificial intelligence (AI) is revolutionizing various sectors by enhancing data processing and decision-making capabilities beyond human limits. However, as AI systems grow more sophisticated, they become increasingly opaque, raising concerns about transparency, trust, and fairness.  The “black box” nature typical in most AI systems often leaves stakeholders questioning the origins and reliability of AI-generated outputs. In response, technologies like Explainable AI (XAI) have emerged looking to demystify AI operations, though they often fall short of fully clarifying its complexities. As AI’s intricacies continue to evolve, so too does the need for robust mechanisms to ensure these systems are not only effective but also trustworthy and fair. Enter blockchain technology, known for its pivotal role in enhancing security and transparency through decentralized record-keeping. Blockchain holds potential not just for securing financial transactions but for imbuing AI operations with a layer of verifiability that has previously been difficult to achieve. It has the potential to address some of AI’s most persistent challenges, such as data integrity and the traceability of decisions, making it a critical component in the quest for transparent and reliable AI systems. Chris Feng, COO of Chainbase, offered his insights on the subject in an interview with crypto.news. According to Feng, while blockchain integration may not directly resolve every facet of AI transparency, it enhances several critical areas. Can blockchain technology actually enhance transparency in AI systems? Blockchain technology does not solve the core problem of explainability in AI models. It’s crucial to differentiate between interpretability and transparency. The primary reason for the lack of explainability in AI models lies in the black-box nature of deep neural networks. Although we comprehend the inference process, we do not grasp the logical significance of each parameter involved. So, how does blockchain technology enhance transparency in ways that differ from the interpretability…

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Filed under: News - @ July 15, 2024 8:12 pm