Explainable AI promises clarity in decision-making processes driven by machines, yet it often obscures the deeper issues such as bias and misuse instead of addressing them.
Current methods like SHAP and LIME for explainable AI focus on decoding influence rather than intention, leaving fundamental issues in AI systems unexamined.
Investments in explainable AI yield insights into input variables but fail to provide a meaningful understanding of underlying decision-making intentions and biases involved.
Despite the facade of transparency, explainable AI merely rebrands the chaos of algorithmic processes, failing to remedy the systemic problems of bias and overreach.
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