explainable

简明释义

英[ɪkˈspleɪnəbl]美[ɪkˈspleɪnəbl]

adj. 可辩解的；可说明的

英英释义

单词用法

同义词

反义词

例句

1.Contemplate the fact that you might have made a regrettable, but somewhat explainable decision then.

谨记这个事实：也许你做了一个让人懊悔但仍然可以解释的决定。

2.He believed in scientific method - phenomena were explainable.

他相信科学方法——现象都是可以解释的。

3.Much of the rise in the income share of the top one per cent is explainable by a tendency for some wages, such as those of asset managers and chief executives, to track stock prices closely.

前百分之一的收入增长大部分是由于某些薪酬的增长带来的，比如资产经理和首席官的工资，与股票价格密切相关。

4.So does the idea that decisions made by AI systems should be explainable, transparent, and fair.

人工智能系统做出的决策应该是可解释的、透明的和公平的，这一观点也是如此。

5.Much of the rise in the income share of the top one per cent is explainable by a tendency for some wages, such as those of asset managers and chief executives, to track stock prices closely.

前百分之一的收入增长大部分是由于某些薪酬的增长带来的，比如资产经理和首席官的工资，与股票价格密切相关。

6.Decisions made by AI systems should be explainable, transparent, and fair.

人工智能系统做出的决策应该是可解释的、透明的且公平的。

7.In the research of detection model generation, it is desirable that the detection model be explainable and have high detection rate, but the existing methods cannot achieve these two goals.

在入侵检测模型生成研究中，检测模型不仅应该具有高的检测率，而且也应该是易于理解的，但是现存的很多方法很难同时达到这两个目标。

8.They are explainable as a species of mental atavism.

这些都可以解释为一种精神的返祖现象。

9.This influence is termed "resistance" (Freud), or "defensiveness" (Rogers), or "security operation" (Sullivan), and a great deal of behavior is thereby explainable.

这种影响称为“阻抗resistance”(佛洛德)，或“防御defensiveness”(罗杰斯)，或“安全操作security operation”(沙利文)，以及用来解释大量的行为。

10.The results of the experiment were explainable, showing a clear correlation between the variables.

实验的结果是可解释的，显示了变量之间的明确相关性。

11.In the context of AI, explainable models are crucial for understanding decision-making processes.

在人工智能的背景下，可解释的模型对于理解决策过程至关重要。

12.The teacher provided explainable methods to solve the math problems, making it easier for students to grasp.

老师提供了可解释的方法来解决数学问题，使学生更容易理解。

13.His behavior was explainable given the stressful circumstances he was under.

考虑到他所处的压力环境，他的行为是可以解释的。

14.The software's error messages should be explainable to help users troubleshoot effectively.

软件的错误信息应该是可解释的，以帮助用户有效地排除故障。

作文

In today's rapidly advancing technological landscape, the concept of artificial intelligence (AI) has become increasingly prevalent. However, as AI systems grow more complex, the need for an explainable framework becomes crucial. The term explainable refers to the ability to clarify and interpret the decisions made by AI algorithms in a manner that is understandable to humans. This is particularly important because many AI systems operate as 'black boxes,' making it difficult for users to comprehend how decisions are reached. One of the primary concerns surrounding AI is the potential for bias in decision-making processes. If an AI system is trained on data that contains inherent biases, it may produce results that are unfair or discriminatory. Therefore, having an explainable AI model allows developers and users to identify and rectify these biases. By understanding the rationale behind AI decisions, stakeholders can ensure that the technology is used ethically and responsibly. Moreover, the significance of explainable AI extends beyond ethical considerations. In sectors such as healthcare, finance, and law enforcement, the consequences of AI-driven decisions can be profound. For instance, if an AI algorithm recommends a particular treatment for a patient, it is vital that medical professionals understand the reasoning behind this recommendation. An explainable model can provide insights into the factors that influenced the AI's decision, enabling doctors to make informed choices that prioritize patient welfare. In addition to enhancing trust and accountability, explainable AI fosters collaboration between humans and machines. As AI systems become more integrated into our daily lives, it is essential for users to feel confident in their interactions with these technologies. When users can comprehend how AI arrives at its conclusions, they are more likely to engage with the system and leverage its capabilities effectively. This synergy between human intuition and machine intelligence can lead to innovative solutions and improved outcomes across various domains. Despite the advantages of explainable AI, achieving it poses several challenges. Many advanced AI models, such as deep learning networks, excel in performance but lack transparency. Researchers are actively exploring methods to enhance the interpretability of these models without sacrificing their predictive power. Techniques such as feature importance analysis, local interpretable model-agnostic explanations (LIME), and SHAP (SHapley Additive exPlanations) have emerged as valuable tools in this pursuit. As we continue to navigate the complexities of AI, the demand for explainable systems will only intensify. Policymakers, technologists, and ethicists must collaborate to establish guidelines and standards that promote transparency in AI. By prioritizing explainable AI, we can harness the full potential of this transformative technology while safeguarding against its risks. Ultimately, the goal should be to create AI systems that not only perform effectively but also do so in a way that is understandable, trustworthy, and aligned with human values. In conclusion, the journey towards explainable AI is essential for fostering trust, accountability, and collaboration in our increasingly automated world. As we strive to develop AI technologies that are both powerful and comprehensible, we must remain committed to ensuring that these systems serve humanity in a fair and just manner. The future of AI lies not only in its capabilities but also in our ability to understand and explain its workings, paving the way for a more harmonious coexistence between humans and intelligent machines.

在当今快速发展的科技环境中，人工智能（AI）的概念变得越来越普遍。然而，随着AI系统的复杂性增加，对一个可解释的框架的需求变得至关重要。可解释的一词是指能够以人类可以理解的方式澄清和解释AI算法所做决策的能力。这一点尤其重要，因为许多AI系统作为“黑箱”运作，使用户难以理解如何得出决策。 围绕AI的主要担忧之一是决策过程中的潜在偏见。如果AI系统在包含固有偏见的数据上进行训练，它可能会产生不公平或歧视性的结果。因此，拥有一个可解释的AI模型使开发者和用户能够识别和纠正这些偏见。通过理解AI决策背后的理由，利益相关者可以确保技术的伦理和负责任使用。 此外，可解释的AI的重要性不仅限于伦理考量。在医疗、金融和执法等领域，AI驱动的决策的后果可能是深远的。例如，如果AI算法推荐某种治疗方案给患者，那么医疗专业人员理解这一推荐背后的推理至关重要。一个可解释的模型可以提供影响AI决策的因素的见解，使医生能够做出优先考虑患者福利的明智选择。 除了增强信任和问责制外，可解释的AI还促进了人类与机器之间的协作。随着AI系统越来越多地融入我们的日常生活，用户必须对与这些技术的互动感到自信。当用户能够理解AI如何得出结论时，他们更有可能与系统互动并有效利用其能力。这种人类直觉与机器智能之间的协同作用可以在各个领域带来创新解决方案和改善结果。 尽管可解释的AI的优势显而易见，但实现它面临着几个挑战。许多先进的AI模型，如深度学习网络，虽然在性能上表现出色，但缺乏透明度。研究人员正在积极探索方法，以提高这些模型的可解释性，而不牺牲它们的预测能力。特征重要性分析、局部可解释模型无关解释（LIME）和SHAP（SHapley加法解释）等技术已成为这一追求中的宝贵工具。 随着我们继续应对AI的复杂性，对可解释的系统的需求只会加剧。政策制定者、技术专家和伦理学家必须合作建立促进AI透明度的指导方针和标准。通过优先考虑可解释的AI，我们可以充分利用这一变革性技术，同时保护自己免受其风险。最终，目标应该是创建既高效又以可理解、可信赖和符合人类价值观的方式运行的AI系统。 总之，朝着可解释的AI的旅程对于在我们日益自动化的世界中促进信任、问责制和协作至关重要。在努力开发既强大又易于理解的AI技术时，我们必须始终致力于确保这些系统以公平和公正的方式服务于人类。AI的未来不仅在于其能力，也在于我们理解和解释其工作原理的能力，为人类与智能机器之间的和谐共存铺平道路。