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职位：可解释性研究必须优先考虑基础而非临时方法Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods

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以下内容为 AI 自动翻译，英文原文附后供参考。

摘要

一篇最近的论文强调了在可解释人工智能（XAI）中基础研究的重要性，而不是依赖临时方法。作者认为，许多现有的解释未能有效融入现实工作流程，从而降低了它们的实际效用。他们呼吁明确的问题表述和评估目标，以确保解释在人工智能系统中是可操作和有益的。

A recent paper emphasizes the importance of foundational research in Explainable AI (XAI) rather than relying on ad-hoc methods. The authors argue that many existing explanations do not effectively integrate into real-world workflows, which diminishes their practical utility. They call for clearer problem formulations and evaluation objectives to ensure that explanations can be actionable and beneficial in AI systems.

要点

  1. XAI中的基础研究对于开发有效的解释至关重要。

    Foundational research in XAI is crucial for developing effective explanations.

  2. 当前的临时方法往往无法在现实应用中提供可操作的见解。

    Current ad-hoc methods often fail to provide actionable insights in real-world applications.

  3. 明确的问题表述和评估标准对于解释的有意义整合是必要的。

    Clear problem formulations and evaluation criteria are necessary for meaningful integration of explanations.

为何重要

在XAI中关注基础研究对于创建不仅可解释而且在各个行业中实际有用的人工智能系统至关重要。通过优先解决基础问题，该领域可以朝着更稳健和可靠的人工智能解决方案迈进。

This focus on foundational research in XAI is essential for creating AI systems that are not only interpretable but also practically useful in various industries. By prioritizing foundational issues, the field can move towards more robust and reliable AI solutions.

按谁的角度看

产品经理

产品经理应意识到，投资于基础XAI研究可以导致更有效的产品特性，从而增强用户信任和参与度，最终推动产品成功。

Product managers should be aware that investing in foundational XAI research can lead to more effective product features that enhance user trust and engagement, ultimately driving product success.

开发者

对于开发人员和工程师来说，理解对基础研究的重视可以指导他们构建不仅在技术上可靠而且提供有意义解释的人工智能系统，这些解释可以在现实应用中使用。

For developers and engineers, understanding the emphasis on foundational research can guide them in building AI systems that are not only technically sound but also provide meaningful explanations that can be utilized in real-world applications.

学生与求职者

学生和求职者应认识到可解释性在人工智能中的日益重要性，因为在基础XAI研究方面的专业知识将成为就业市场中的宝贵资产，特别是在关注人工智能伦理和负责任的人工智能开发的角色中。

Students and job-seekers should recognize the growing importance of explainability in AI, as expertise in foundational XAI research will be a valuable asset in the job market, particularly in roles focused on AI ethics and responsible AI development.

覆盖来源

来源标题与摘录保留原文，以便核对与追溯。

  • arXiv cs.LG

    Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods

    The paper argues that Explainable AI (XAI) research should focus on foundational issues rather than ad-hoc methods, as current explanations often fail to influence real-world workflows. It highlights the need for clearer problem formulations and evaluation objectives to integrate explanations into actionable AI systems.