Language is deeply rooted in culture, reflecting speakers’ values, beliefs, and experiences. As such, cultural differences significantly influence language use, leading to variations in expressions, idioms, and contextual meanings. Robust AI models must be adequately trained to interpret diverse user input and give responses that are not just accurate but also culturally appropriate.

语言深根于文化之中，反映了言语者的价值观、信仰和经历。因此，文化差异深刻影响着语言的使用，致使表达方式、习语和语境含义上有所变化。强大的AI模型必须经过充分训练，以理解多样化的用户输入，并提供既准确又符合文化背景的答案。

Two of the primary issues with AI models are the presence of bias and underrepresentation in data sets.

AI模型的两个主要问题是数据集中的偏见和代表性不足。（注：数据集，又称为资料集、数据集合或资料集合，是一种由数据所组成的集合。）

AI models trained on large data sets of content can inadvertently learn and perpetuate societal biases, leading to unfair or inaccurate responses. These biases could be stereotypes related to race, gender, or age.

在大规模内容数据集上训练的AI模型可能会无意中学习并延续社会偏见，从而生成不公平或不准确的答案。这些偏见可能是跟种族、性别或年龄相关的刻板印象。

For instance, an  IBM survey of 8,500 global IT professionals  showed that 42% of companies use AI screening, but numerous experiments have shown that some of these tools favor parameters that introduce bias, such as younger age candidates or males more often than females. Recognizing and mitigating these biases is critical to ensuring fair and accurate AI interactions across cultures.

例如，IBM对8500名全球IT专业人士的调查显示，42%的公司使用AI筛选工具，但许多实验表明，这些工具倾向于使用引入偏见的参数，比如更青睐年轻候选人或男性，而不是女性。 识别和减轻这些偏见对于确保跨文化的公平和准确的AI互动至关重要。

Collecting and representing diverse cultural data presents another significant challenge. Underrepresented groups such as minorities may be excluded from or underrepresented in training data sets, leading to biased outcomes. This data gap can result in AI models that perform well for majority groups but fail to understand or appropriately respond to input from minority cultures.

采集和呈现不同的文化数据则是另一重大挑战。诸如少数群体等代表性不足的群体可能会在训练数据集中被排除或呈现不足，导致结果产生偏见。这种数据空缺可能会导致AI模型在大多数群体中表现良好，但无法理解或恰当回应少数群体的信息。

What should be done to ensure AI models capture cultural nuance and mitigate social biases? It starts with the source: the training data must address these problems. Developers can use three techniques——Data Relevance and Diversity, Localization and Contextualization, and   Bias Mitigation Strategies.

应该做些什么来确保AI模型用于捕捉文化细微差别并减轻社会偏见呢？首先是源头：训练数据一定要解决以上问题。开发人员要掌握三个要领——数据相关性和多样性，本地化和情境化，还有减轻偏见策略。

Finally, fostering diversity within development teams is essential. Human-in-loop oversight and collaboration will remain vital. Intentionally bringing together individuals from various cultural backgrounds leverages a broader range of perspectives to identify and address potential biases before they become ingrained in the system.

最后，培养开发团队的人才多样性是必不可少的。人机交互监督和合作仍将是至关重要的。有意识地汇集来自不同文化背景的人才，可以借助更广泛的视角，在偏见固化之前识别并解决潜在问题。

Several promising directions emerge as we look to the future of culturally aware AI. One is dynamic cultural adaptation, with AI models that dynamically adjust their responses based on real-time cultural cues and user feedback.

展望未来，文化敏感型AI展现出多个令人期待的发展方向。其一为动态文化适应性， AI模型基于实时文化线索及用户反馈，对自身反应进行实时调整的过程。

Another is cross-cultural AI mediators facilitating communication between individuals from different cultural backgrounds, bridging linguistic and cultural gaps. Progress has been made in advancing AI’ s ability to recognize  and respond appropriately to emotions expressed in culturally diverse ways.

其二则是跨文化AI调解者旨在促进不同文化背景下个体间的交流互动，有效消除语言及文化差异所造成的障碍。在能力提升方面，AI对于多样化文化情绪表达的识别与灵活应对能力已逐步增强。

Lastly, global governments and large enterprises are moving toward establishing robust ethical guidelines for developing and deploying culturally aware AI systems, ensuring respect for cultural diversity and privacy.

最后，全球范围内的政府及大型企业正在积极制定严格的伦理规范，以指导具有文化敏感性的AI系统的研发与应用，确保对文化多样性的尊重与个人隐私的保护。