内容来源：https://aibusiness.com/generative-ai/how-payroll-giant-adp-practices-ai-governance-human-oversight

内容总结：

ADP：将负责任的AI融入人力资源与薪酬管理

在生成式AI日益普及的背景下，人力资源和薪酬管理公司ADP正致力于将AI嵌入其产品，同时坚持负责任的AI治理实践。ADP副总裁、管理法律顾问兼AI法律官海伦娜·阿尔梅达近日接受专访，介绍了公司在AI治理方面的原则与实践。

阿尔梅达表示，ADP多年来一直在使用不同形式的AI和机器学习技术，并自2018年起建立了一套ADP AI与数据伦理规范，指导AI的开发与部署。公司还成立了由内部专家和外部专家组成的AI与数据伦理委员会，涵盖法律、隐私、伦理、人力资源和产品等领域的专业知识。随着生成式AI的兴起，ADP持续审视和更新其治理流程，但核心原则始终未变：确保AI公平、透明、安全，并保持人类参与。

在应对美国各州及各国不同的法律法规时，阿尔梅达指出，这些法律之间的共同原则是一致的，包括对用户的透明度、可解释性、风险管理、隐私与安全以及持续监控。ADP长期坚持这些原则，并将其融入产品设计之中。她强调，即使没有法律要求，ADP也不会涉足某些对终端用户不安全的AI用途。由于ADP在美国每六名员工中就有一人的薪酬由其处理，公司对AI的使用始终保持高度谨慎。

关于“人类参与”原则，阿尔梅达强调，这不能流于形式上的勾选，而必须为人类提供有意义的参与机会。人类审核并非所有AI支持的任务都需要，低风险的行政任务可以安全地自动化，而涉及薪酬、就业或法律权利的设计则需要设置人工交互环节。她还特别提到“自动化偏差”问题——人们倾向于盲目相信AI给出的答案——并表示ADP会通过内部试点和观察客户使用情况来调整设计，确保用户将时间花在真正重要的事情上。

中文翻译：

赞助内容由谷歌云提供
选择你的首批生成式人工智能用例
要开始使用生成式人工智能，首先应关注那些能够改善人们信息体验的领域。
治理方法侧重于透明度、可解释性、风险管理、隐私和安全等关键原则。
对于这家人力资本管理和薪酬服务公司而言，将人工智能嵌入其产品与保持强有力的AI治理实践并非相互排斥。
虽然ADP在其IT团队内部使用人工智能进行编码以及实现AI图像和视频生成等任务的自动化，但它也在其ADP Assist工具中加入了生成式人工智能功能。ADP Assist是一套AI智能体，可跨人力资源和薪酬工作流程运行。这些智能体有助于减轻人力资源和薪酬团队的压力。
作为一家在企业人力资源和薪酬领域无处不在的组织，ADP已将负责任的人工智能作为其向客户发布的工具中的关键要素。
在这次问答中，ADP副总裁、管理法律顾问兼人工智能法律官海伦娜·阿尔梅达讨论了公司的负责任人工智能实践和原则，当前公众对人工智能风险和安全的关注度很高。
随着生成式人工智能日益主流化，ADP的负责任人工智能方法发生了怎样的变化？
海伦娜·阿尔梅达：ADP多年来一直在使用不同版本的人工智能和机器学习。我们长期以来一直设有负责任人工智能项目。自2018年起，我们就制定了一套ADP人工智能和数据伦理规范，指导我们如何开发和部署人工智能。
大约在同一时期，我们成立了人工智能和数据伦理委员会，由内部主题专家和外部专家组成，涵盖法律、隐私、伦理、人力资源和产品方面的专业知识，成员来自内部和外部服务提供商。我们有一个团队始终关注技术和人工智能的应用，以确保我们拥有正确的视角。
我们一直在思考负责任的人工智能。随着人工智能技术的发展，包括生成式人工智能的应用，我们持续审视我们的流程，持续审视ADP内部跨职能团队的人工智能治理，并不断更新。基本前提是确保公平、透明、安全，并有人的参与。影响人们的关键决策始终如一。
鉴于美国各州和各国拥有不同的法律和法规，ADP如何应对不同层面的治理要求？
阿尔梅达：所有这些法律的共同原则是相同的。这些原则包括对用户而言可识别的透明度、可解释性、风险管理、隐私和安全，以及维护监控程序。所有这些要素都出现在不同的监管框架中。
我们认为这些原则我们早已落实到位，我们已经经受住了许多变化的考验。
我们有指导原则来帮助我们设计产品。我们始终关注变化和法规。但我们注意到了这一主题，我们对我们的项目正在考虑相关事项以确保这些法规得到覆盖感到放心。
法规的可能出台是否决定了你们作为组织使用哪些人工智能产品，或者如何将人工智能嵌入客户使用的工具中？例如，如果美国建立联邦人工智能监管框架或机构，会有影响吗？
阿尔梅达：世界上有些地区已经禁止了某些对终端用户不安全的人工智能用途。无论是否有法律，这些都不是ADP会涉足的领域。法律对我们产生影响的地方在于，我们如何确保产品具有特定用例所需的透明度和可解释性水平。
我们始终非常谨慎地对待我们所做的事情，因为至少在美国，我们为六分之一的劳动者发放薪酬。这些劳动者与ADP有亲身体验，从ADP领取工资。
你们的原则之一是确保有人的参与。你们如何确保人类不仅仅是自动批准人工智能所做的决定？
阿尔梅达：我们不能让它成为一种仪式性的勾选，让人只是走个过场来表明我们有人的参与。我们确实需要为人提供有意义的参与机会。我们需要确保这个人理解人工智能的规则，并拥有足够的背景信息来判断何时出现异常或可疑的结果。
人工审核不应成为每项人工智能支持任务的反射性要求，因为并非所有人工智能支持的任务都承载相同的风险。因此，影响某人薪酬、就业或法律权利的设计，值得采用能够创造人机交互时刻的设计。低风险的行政任务可以安全地自动化，不需要人工干预。这就是我每天工作的一部分：思考哪里需要人的参与，然后与产品团队合作进行设计。
有些是显而易见的，有些则更难预先预料。
例如，存在一种叫做自动化偏差的现象。如果人工智能告诉你有一个正确答案，人们往往倾向于相信它并继续行事。我们要确保这种情况不会发生。我们始终先在内部试用我们使用的东西。我们观察客户的使用情况，有时我们会发现人们在某些更偏行政性、更安全的环节上花费的时间远远超出我们的预期，而在我们认为他们会花更多时间的事情上却花费较少。然后我们调整设计，确保我们有正确的激励措施将时间花在重要的事情上，因为为客户提供让他们更高效的工具非常重要。
编者按：本次问答经过编辑以确保清晰和简洁。

英文来源：

Sponsored by Google Cloud
Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
The governance approach focuses on key principles of transparency, explainability, risk management, privacy and security.
For the human capital management and payroll company, embedding AI in its products and maintaining strong AI governance practices are not mutually exclusive.
While ADP uses AI internally within its IT team for coding and to automate tasks such as AI image and video generation, it also has included generative AI features in its ADP Assist tool. ADP Assist is a suite of AI agents that work across HR and payroll workstreams. The agents help reduce pressure on HR and payroll teams.
As an organization with a pervasive presence in enterprise HR and payroll, it has made responsible AI a key ingredient in the tools it releases to its clients.
In this Q&A, Helena Almeida, vice president, managing counsel and AI legal officer at ADP, discusses the company’s responsible AI practices and principles, at a time when much public attention is focused on AI risk and safety.
How has ADP’s responsible AI shifted as generative AI has become more mainstream?
Helena Almeida: ADP's been using different versions of AI and machine learning for years. We have had a responsible AI program for a long time. We have had a set of ADP AI and data ethics since 2018 that govern how we develop and deploy AI.
Around the same time, we formed our AI and data ethics council, comprising internal subject matter experts and external experts, spanning legal, privacy, ethics, HR, and product expertise from both internal and external providers. We have a team that is always looking at uses of technology and AI to make sure we have the right perspective.
We have always been thinking about responsible AI. As AI technology has shifted to include GenAI uses, we keep looking at our processes, we keep looking at our cross-functional teams within ADP to look at AI governance, and we keep updating them. The essential premises are making sure it is fair, transparent, secure and that a human is involved. The key decisions that affect people have stayed the same.
How does ADP handle varying governance layers across states and countries, especially given that U.S. states and countries have different laws and regulations?
Almeida: The common principles among all these laws are the same. The principles are recognizable transparency for users, explainability, risk management, privacy and security, and maintaining monitoring programs. All these things together appear in different regulatory schemes.
We feel these are principles that we have had in place for a long time, and we've weathered a lot of change.
We have guiding principles that help us design products. We are staying attuned to changes and regulations. But we have noticed that theme, and we feel comfortable that our program is considering things so that these regulations are covered.
Does the possible arrival of regulation determine what AI products you use as an organization or how you embed AI within the tools that your customers use? For example, if the U.S. were to create a federal AI regulation framework or agency, would that make a difference?
Almeida: There are parts of the world that have prohibited certain uses of AI that would be unsafe for end users. Those are not things that ADP would be involved in, whether there were laws or not. What impacts us when we look at the laws is how we can ensure that this product has the levels of transparency and explainability required for a particular use case.
We are always very careful with what we do because, at least in the US, we pay one out of six workers. These workers have personal experience with ADP and receive their paychecks from ADP.
One of your principles is to ensure that a human is involved. How do you ensure that humans are not just automatically approving what the AI is doing?
Almeida: We cannot have it be a ceremonial checkbox; the human takes a particular step just to say we have a human loop. We really need a meaningful opportunity for a human to participate. We need to make sure that the person understands the rules of AI and has enough context to figure out when there are unusual or questionable results.
Human review should not be a reflexive requirement for every AI-supported task, because not all AI-supported tasks carry the same risk. So, a design that affects somebody's pay, their employment or their legal rights deserves a design that creates moments for human interaction. Low-risk administrative tasks can be safely automated and do not require human intervention. That is part of what I do every day: think about where we need a human to be involved and then work with the product people to design.
Some are obvious, some are harder to anticipate upfront.
For example, there is something called automation bias. If the AI tells you that there is a right answer, people tend to believe it and move on. We want to make sure that is not happening. We always pilot things we use internally. We look at our clients’ use of them, and sometimes we see that people are spending way more time on something a little more administrative and safer than something we thought they would spend more time on. Then we adjust the design to ensure we are getting the right incentives to spend time on what matters, because it is important to give our clients tools that make them more productive.
Editor’s note: This Q&A has been edited for clarity and conciseness.