AI is progressing rapidly, exceeding the pace of regulations and investor adjustments. Emerging applications span screening job candidates, aiding in medical diagnostics, assessing loan applications, and predicting crimes. These systems promise enhanced decision-making by analyzing vast data without emotional influence. However, a key drawback is the lack of transparency and explainability in AI operations, rendering them untrustworthy. As the focus shifts from mere scalability to understanding AI’s decision frameworks, Web3 infrastructure offers potential solutions for enhancing transparency and trustworthiness in AI systems.
Artificial intelligence is evolving rapidly, influencing multiple sectors not just by increasing productivity but also by making decisions on behalf of humans, such as screening job candidates and aiding in diagnostics.
Trust is emerging as a critical issue in AI's evolution, focusing not on the capabilities of models but on how these decisions are made, highlighting the importance of explainability.
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