由Google云公司赞助
选择您的第一个基因AI 使用案例
要从基因AI开始，首先关注能够改善人类信息体验的领域。
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。
5 小读
特设工作组
对于人力资本管理和工资公司来说，将AI嵌入其产品中并保持强大的AI治理做法并不是相互排斥的。
虽然ADP在其IT团队内内部使用AI进行编码，并实现AI图像和视频生成等任务的自动化，但它也在其ADP Assistance工具中纳入了基因AI功能。 特设工作组协助是一套AI agents软件，在人力资源和发薪工作流程中工作。 agents帮助减少对HR和薪工单团队的压力。
作为一个在企业人力资源和工资单中广泛存在的组织，它使负责任的AI成为它对其客户的工具中的一个关键成分。
在ADP的副总裁、管理律师和AI法律干事Helena Almeida(Helena Almeida)讨论公司负责任的AI做法和原则
ADP负责的AI是如何随着基因化AI越来越主流而转变的？
Helena Almeida:ADP多年使用不同版本的AI和机器学习。 我们有一个负责的AI计划很长时间。 自2018年以来，我们拥有一套ADPAI和数据伦理，指导我们如何发展和部署AI。
大约在同一时间，我们成立了AI和数据伦理委员会，由内部主题专家和外部专家组成，涉及法律、隐私、道德、人力资源以及内部和外部提供者的产品专门知识。 我们有一个团队总是在研究技术和AI的应用以确保我们有正确的观点。
我们一直在考虑负责任的AI。 随着AI技术转移到包括GenAI的用途，我们不断审视我们的进程，我们不断审视ADP内部的跨功能团队来审视AI治理，并不断更新。 基本前提正在确保公平、透明、安全，并确保人的参与。 影响人民的关键决定保持不变。
特别是美国各州和国家有不同的法律法规，ADP如何处理各州和各国不同的治理层？
阿尔梅达：所有这些法律的共同原则是相同的。 这些原则对用户来说是公认的透明度、可解释性、风险管理、隐私和安全以及维持监测方案。 所有这些都出现在不同的监管计划中。
我们觉得这些都是我们长期存在的原则，我们经历了许多变化。
我们有帮助我们设计产品的指导原则。 我们正在适应变化和规定。 但是我们已经注意到这个主题，我们感到自在的是，我们的方案正在考虑一些事情，以便涵盖这些条例。
监管的到来是否决定了你作为一个组织使用的AI产品，或者你如何将AI嵌入你的客户使用的工具中？例如，如果美国要建立一个联邦AI监管框架或机构，那会有什么不同吗？
阿尔梅达：世界上有些地方禁止使用某些对最终用户不安全的AI。 这些不是阿塞拜疆民主党参与的事情，无论是否有法律。 当我们看法律时，影响我们的问题是，我们如何确保这一产品具有特定用途所需的透明度和可解释性。
我们总是非常小心地对待我们的工作，因为至少在美国，我们支付六分之一的工人。 这些工人在阿塞拜疆民主党有个人经验，从阿塞拜疆民主党领取工资。
你们的原则之一是确保人类参与其中。 你如何确保人类不只是自动认可AI的行为？
阿尔梅达：我们不能把它当作一个礼仪式的复选框；人类采取特定步骤只是为了说我们有一个人类的循环。 我们确实需要一个有意义的机会让人类参与。 我们需要确保此人了解大赦国际的规则，并有足够的背景来了解何时会产生不寻常或可疑的结果。
人文审查不应是大赦国际支持的每一项任务的反射要求，因为并非所有大赦国际支持的任务都具有同样的风险。 因此，一个影响一个人的薪酬，他们的就业或他们的合法权利的设计，应该有一个创造人类互动的时刻的设计。 低风险的行政任务可以安全自动化，不需要人干预。 这就是我每天工作的一部分：想想我们需要一个人类参与到哪里，然后与产品人合作来设计。
有些是显而易见的，有些是难以预先预料的。
例如，有些东西叫做自动化偏差。 如果AI告诉你，有一个正确的答案，人们倾向于相信它并前进。 我们希望确保这种情况不会发生。 我们总是驾驶我们内部使用的东西。 我们审视客户使用它们的情况，有时我们看到人们花更多的时间在行政上和更安全的地方，比我们想象中他们会花更多的时间。 然后我们调整设计，以确保我们得到适当的奖励，在重要的问题上花时间，因为重要的是要给我们的客户工具，使他们更有成效。
编辑说明：本QQA经过编辑，内容清晰简洁。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
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.
5 Min Read
ADP
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.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.