As more and more businesses start using their own Large Langue Models (LLMs), responsible AI becomes a critical concern. A key part of responsible AI is using your AI training and data validation to prevent any hateful, intolerant, or biased AI content generation. This kind of content can be harmful and contribute to overall social problems, including (but not limited to):
Spreading hate speech
Marginalizing certain groups or communities
Causing emotional distress
Biased or intolerant content also has severe business consequences. Read on to learn why businesses should use AI training to ensure responsible AI usage and implement our recommended action items.
Why is Responsible AI Crucial for Business Content?
When a business’s LLM neglects responsible AI by creating intolerant, hateful, or biased content, it doesn’t just create the social issues mentioned above. The business may also suffer some consequences. Negative repercussions could occur with any public-facing content, including:
Print marketing materials
Official website chatbots
Social media posts
Sales emails
Website copy
A company’s LLM is likelier to create offensive multilingual content if a human expert in the loop is absent from the process. In some cases, a human expert is essential to review and perfect AI translation or localization. These are the potential consequences a business may face:
Potential Consequences of Neglecting Responsible AI
Legal, including lawsuits for defamation, discrimination, or harassment
Regulatory penalties, fines, restrictions, etc.
Reputation damage with stakeholders, customers, etc.
Loss of customers and business partnerships
Loss of revenue
Expenses for damage mitigation, including new publicity to restore trust, more AI training and development, etc.
Lowered employee morale, loyalty, and productivity
Businesses may experience just one, or a combination, of these consequences. Taking the right steps to avoid these ramifications is crucial. Read our recommendations below.
5 Tactics for Ensuring Responsible AI Usage and Preventing Harmful Content
Consider implementing all, or at least some, of these tactics to ensure your AI output isn’t unintentionally biased, racist, misogynistic, or simply offensive or culturally taboo. For optimal results, work with a diverse group of people throughout the AI training and monitoring process. They will bring a wider and stronger base of knowledge. Consider working with AI training experts, like the ones at Lionbridge, who combine expertise in AI, sociocultural norms, industries, and linguistics. Lastly, some companies may set policies for AI developers and users. These policies articulate consequences for misuse of an AI system. They motivate everyone to help ensure an AI never creates harmful or offensive content.
Tactic #1 Data Curation
When conducting AI training, proper data collection is crucial for teaching an LLM to create content free from bias, racism, misogyny, etc. Companies should take a two-pronged approach. Firstly, filter out data from sources that may include problematic viewpoints. The second step is ensuring the training data for an LLM represents a diverse array of voices and perspectives. If the content is multilingual or comes from differing locations or cultures, it may help to have local or linguistic experts assist with these tasks. Lionbridge has a solid foundation in linguistics and language. This expertise uniquely positions us to support the Natural Language Process required in machine learning.
Tactic #2 Establish an Ethical Framework
When training AI for ethical output, building an ethical framework is essential. Much like creating a style guide or translation glossary, a company should develop a series of rules and guidelines that it would want all its content to abide by. Use industry standards to help develop the framework, ensuring compliance and better results. These frameworks may need to be expanded and varied for multilingual or cultural work to include new language and social norms or taboos. Companies should also set up protocols and structures for continuous ethical deployment of the AI model.
Tactic #3 Ethical and Bias Pre-Training
During the pre-training and fine-tuning phases, companies should prioritize bias mitigation techniques. Using the ethical framework mentioned above, the LLM should be taught to identify and avoid creating and consuming biased or offensive content. When testing The LLM during pre-training, it’s essential to use data validation to update data sets with a foundational understanding of ethics and biases. The ethical framework is helpful for this step, as well.
During training, consider creating mechanisms that showcase the AI model’s decision-making when identifying and rejecting offensive content. This transparency will help later if there are issues.
Tactic #4: Continually Monitor Output
After training their AI, a company must still continue reviewing output. For mission-critical content, a human reviewer may be worth considering. This is particularly helpful for content designed for customers who speak different languages and come from other cultures. Companies may also want to use a human reviewer for regularly scheduled material audits to ensure quality and compliance with their ethical framework. You may also consider creating opportunities for customers to report offensive content, and incorporate this feedback into continuous fine-tuning efforts.
Tactic #5: Retrain as Needed
Companies should build retraining into their protocols for a couple of reasons. Firstly, the AI model may not thoroughly “learn” how to correctly apply the ethical framework initially. It may erroneously create offensive content, or the ethical framework itself might be lacking. A second reason for continued retraining is because cultural norms change constantly. While content isn’t offensive today, it could be tomorrow — especially if it's developed for customers who speak multiple languages or come from other cultures. The more cultures and languages, the more nuances required for an ethical framework.
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随着越来越多的企业开始使用自己的大型语言模型(LLM),负责任的人工智能成为一个关键问题。负责任的人工智能的一个关键部分是使用你的人工智能训练和数据验证来防止任何仇恨、不宽容或有偏见的人工智能内容的生成。这类内容可能是有害的,并会导致整体社会问题,包括(但不限于):
散布仇恨言论
边缘化某些群体或社区
造成情感抑郁
有偏见或不宽容的内容也会带来严重的商业后果。继续阅读,了解为什么企业应该使用人工智能培训来确保负责任的人工智能使用,并实施我们建议的行动项目。
为什么负责任的AI对业务内容至关重要?
当一个企业的LLM通过创建不宽容,仇恨或有偏见的内容而忽视负责任的AI时,它不仅仅会产生上面提到的社会问题。企业也可能遭受一些后果。任何面向公众的内容都可能产生负面影响,包括:
打印营销材料
官方网站chatbots
社交媒体帖子
销售电子邮件
网站复制
一个公司的法学硕士是更有可能创造攻击性的多语言内容,如果人类专家在循环是缺席的过程。在某些情况下,人类专家对于审查和完善人工智能翻译或本地化至关重要。这些是企业可能面临的潜在后果:
忽视负责任的AI的潜在后果
法律,包括诽谤、歧视或骚扰诉讼
监管处罚、罚款、限制等
利益相关者、客户等的声誉受损。
客户和商业伙伴关系的损失
收入损失
减轻损害的费用,包括恢复信任的新宣传,更多的人工智能培训和开发等。
降低员工士气、忠诚度和生产力
企业可能只会经历其中一种后果,或者同时经历其中的几种后果。采取正确的措施避免这些后果至关重要。阅读下面我们的建议。
确保负责任的人工智能使用和防止有害内容的5种策略
考虑实施所有这些策略,或者至少实施其中的一部分,以确保您的AI输出不会无意中带有偏见,种族主义,厌恶女性,或者只是冒犯或文化禁忌。为了获得最佳结果,在整个人工智能培训和监控过程中与不同的人群合作。他们将带来更广泛和更强大的知识基础。请考虑与人工智能培训专家合作,例如Lionbridge的专家,他们将人工智能、社会文化规范、行业和语言学方面的专业知识结合在一起。最后,一些公司可能会为AI开发人员和用户制定政策。这些政策阐明了滥用人工智能系统的后果。他们激励每个人帮助确保人工智能永远不会创建有害或攻击性的内容。
策略#1数据固化
在进行人工智能培训时,适当的数据收集对于教授LLM创建没有偏见,种族主义,厌女症等内容至关重要。首先,从可能包含有问题的视点的源中过滤掉数据。第二步是确保LLM的训练数据代表各种各样的声音和观点。如果内容是多语言的,或者来自不同的地方或文化,请当地或语言专家协助完成这些任务可能会有所帮助。Lionbridge在语言学和语言方面有着坚实的基础。这种专业知识使我们能够支持机器学习所需的自然语言过程。
策略#2建立道德框架
在训练人工智能进行道德输出时,建立道德框架至关重要。就像创建风格指南或翻译词汇表一样,公司应该制定一系列规则和指导方针,希望所有内容都能遵守。使用行业标准来帮助开发框架,确保合规性和更好的结果。这些框架可能需要扩大和变化,以纳入新的语言和社会规范或禁忌。公司还应该建立协议和结构,以持续道德地部署人工智能模型。
策略#3道德和偏见预培训
在预培训和微调阶段,公司应该优先考虑偏见缓解技术。使用上面提到的道德框架,法学硕士应该被教导识别和避免创建和消费有偏见或冒犯性的内容。在预训练期间测试LLM时,必须使用数据验证来更新数据集,并对道德和偏见有基本的了解。道德框架也有助于这一步。
在训练过程中,考虑创建一些机制来展示AI模型在识别和拒绝攻击性内容时的决策。这种透明度将有助于以后如果有问题。
策略4:持续监控输出
在训练他们的人工智能之后,公司仍然必须继续审查输出。对于任务关键型内容,人工审核者可能值得考虑。这对于为讲不同语言和来自其他文化的客户设计的内容特别有帮助。公司可能还希望使用人工审核员进行定期的材料审核,以确保质量和遵守其道德框架。您还可以考虑为客户报告攻击性内容创造机会,并将此反馈纳入持续的微调工作中。
策略#5:根据需要重新培训
公司应该将再培训纳入其协议中,原因有几个。首先,人工智能模型可能无法彻底“学习”如何正确应用道德框架。它可能会错误地创造冒犯性的内容,或者道德框架本身可能缺乏。持续再培训的第二个原因是文化规范不断变化。虽然今天的内容并不令人反感,但明天可能会如此-特别是如果它是为讲多种语言或来自其他文化的客户开发的。文化和语言越多,道德框架所需的细微差别就越多。
取得联系
与Lionbridge专家一起开始您的AI培训。我们已经帮助许多客户从他们的LLM中获得最大的利益。我们认真对待负责任的人工智能使用和人工智能信任,我们有自己的信任框架。相信我们,以确保您的LLM帮助您的公司实现其目标,并带来投资回报率。让我们保持联系。
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以上中文文本为机器翻译,存在不同程度偏差和错误,请理解并参考英文原文阅读。
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