全球视野 | 从纸尿裤配送到救灾的思考：目前非营利组织到底需不需要人工智能？

In 2021, in the midst of a major pandemic-induced diaper crisis, the Greater DC Diaper Bank turned to a then-fledgling tool to cope with surging demand: artificial intelligence. 

2021年，在一场由新冠疫情引发的纸尿裤危机中，大特区纸尿裤银行（Greater DC Diaper Bank）求助于一种当时刚刚起步的工具来应对激增的需求：人工智能。 

At first “I wasn’t even sure we really needed A.I.,” says Cassie Fassett, director of partnerships and impact at the Greater DC Diaper Bank, which distributes millions of diapers to low-income families every year. Fassett first applied to IBM’s A.I. incubator for social impact “on a whim” at a time when supply-chain issues and rising prices had left many American families struggling to stock up on diapers. 

大特区纸尿裤银行的合作与影响力总监卡西·法赛特（Cassie Fassett）表示，起初“我甚至不确定我们是否真的需要人工智能”。法赛特最初是“一时兴起”向IBM的人工智能社会影响力孵化器提出申请的。当时，供应链问题和价格上涨，让许多美国家庭都在为纸尿裤而苦苦挣扎。 

The Greater DC Diaper Bank used a machine-learning model, a subset of A.I., designed by IBM to scrape and organize anonymous data from government benefit rosters, local tax codes, internal distribution data, and census demographics to predict areas with the highest diaper need. The model has helped the nonprofit identify neighborhoods where it’s fallen short, find new distribution partners, and bring more attention to the shortage in ways that Fassett hopes could one day become a nationwide standard. 

大特区纸尿裤银行使用了IBM设计的机器学习模型（人工智能的一个子集），从政府福利名册、地方税号、内部分发数据和人口普查数据中收集和整理匿名数据，预测纸尿裤需求量最大的地区。该模型帮助该非营利组织确定了供应不足的地区，找到了新的配送合作伙伴，并通过法赛特希望有朝一日能成为全国性标准的方式，让更多人关注纸尿裤短缺问题。

“People will be able to see and understand the issue in a way that we haven’t been able to before,” she says. 

法赛特说：“人们将能够以一种我们以前无法做到的方式，来看待和理解这个问题。” 

Public interest in A.I. has exploded in recent months, thanks to the power — and potential — of expansive new tools like ChatGPT, which can write and process commands in a way that mimics the complexity of human thought. Yet A.I. has been quietly transforming nonprofit operations for years, driven largely by an influx of corporate philanthropy from major tech companies. As more nonprofits of all sizes seek to use the technology, they’re considering both the benefits and the risks of an A.I.-driven future. 

近几个月来，公众对人工智能的兴趣呈爆炸式增长。这要归功于像ChatGPT这样的扩展性新工具的力量和潜力，它可以模仿人类思维的复杂性，来编写和处理命令。然而，人工智能多年来一直在悄然改变着非营利组织的运作。这主要得益于有大型科技公司大量涌入的企业慈善事业。随着越来越多大大小小的非营利组织寻求使用这种技术，他们正在考虑人工智能驱动的未来的好处和风险。 

At the Greater DC Diaper Bank, A.I. has been both a boon for expanding its reach and a challenge for the organization’s small staff. 

在大特区纸尿裤银行，人工智能既是扩大其影响力的利器，也是对该组织小规模员工的挑战。 

As part of the incubator, experts from IBM worked pro bono alongside the diaper bank’s staff to create a machine-learning model that can give a hyper-local look at diaper needs in the D.C. metro area. While the tool “really helped us to start getting very targeted about our services,” it ultimately became too unwieldy for the diaper bank’s 10-person team to maintain once the experts were gone, says Fassett. At her request, IBM has since created a simplified, yet still sophisticated, version of the original model that’s been easier for the team to manage on their own. 

作为孵化器的一部分，IBM的专家与纸尿裤银行的员工一起无偿工作，创建了一个机器学习模型，该模型可以提供华盛顿特区纸尿裤需求的超本地化信息。虽然这个工具“确实帮助我们开始非常有针对性地提供服务”，但法赛特说，一旦专家离开，这个工具最终变得过于笨重，纸尿裤银行的10人团队根本无法维护。在她的要求下，IBM创建了一个简化但仍然复杂的原始模型版本，使团队更容易独立管理。 

“These types of custom tools are just not something that small nonprofits will be able to sustain” on their own “if they have access to them at all,” says Fassett, who also stressed the importance of “careful data governance and guidance” that takes into account people’s privacy and consent when building out new A.I. projects. For example, the diaper bank opted to use largely public and anonymous geographic data to build out its A.I., rather than personal data from its partners or beneficiaries, to avoid privacy violations. 

法赛特还强调了“谨慎的数据管理和指导”的重要性，在建立新的人工智能项目时，要考虑到人们的隐私和同意权。例如，纸尿裤银行选择使用大部分是公开和匿名的地理数据来构建人工智能，而不是合作伙伴或受益人的个人数据，以避免侵犯隐私。 

For these concerns and others, it’s important for nonprofits of all sizes to have a seat at the table as A.I. tools become more mainstream, says Michael Jacobs, Sustainability and Social Innovation Leader at IBM, where he leads a $30 million initiative for A.I.-powered philanthropy projects. While IBM provides the technical expertise, tools, and occasional cash grants, he says, nonprofits are the experts in creating accessible and equitable solutions for their community. 

IBM公司可持续发展与社会创新领导者迈克尔·雅各布斯（Michael Jacobs）说，出于这些考虑和其他，在人工智能工具成为主流的同时，各种规模的非营利组织都有必要参与其中。他说，虽然IBM提供了专业技术、工具和不定期的现金资助，但非营利组织才是为社区创造无障碍和公平解决方案的专家。 

“Tech companies have a lot to learn from these organizations too,” says Jacobs. 

雅各布斯说：“科技公司也可以从这些组织中学到很多东西。”

While not all nonprofits have been as eager to adopt the new technology, experts agree that A.I. is here to stay — and that organizations ought to start thinking about their next steps. 

虽然并非所有非营利组织都急于采用新技术，但专家们一致认为：人工智能将继续存在，各组织应开始考虑下一步行动。 

“The barriers to access are coming down and will continue to come down” for A.I. tools, says Brigitte Gosselink, director of product impact at Google.org, the philanthropic arm of tech giant Google, which has given over $100 million in cash grants and 160,000 hours in pro bono consulting to a total of more than 150 organizations for A.I.-related projects over the past several years. 

科技巨头谷歌的慈善机构Google.org的产品影响力总监布里吉特·戈斯林克（Brigitte Gosselink）说：“人工智能工具的使用门槛正在降低，并将继续降低。”在过去几年中，Google.org为150多个组织的人工智能相关项目，提供了超过1亿美元的现金资助和16万小时的公益咨询。 

The organizations that Google.org supports say their A.I. projects have helped achieve their goals in a third of the time and half the cost, according to surveys Google has conducted. That claim is echoed in research about A.I.'s impact on productivity. A study by Stanford University and the National Bureau of Economic Research found that A.I. increased workers’ productivity by 14 percent; another released by MIT researchers in March found that ChatGPT improved workers’ efficiency by 37 percent. 

根据谷歌进行的调查，Google.org支持的组织表示，他们的人工智能项目帮助他们以三分之一的时间和一半的成本实现了目标。关于人工智能对生产力影响的研究也证实了这一点。斯坦福大学和美国国家经济研究局（National Bureau of Economic Research）的一项研究发现，人工智能将工作人员的生产率提高了14%；麻省理工学院的研究人员于今年3月发布的另一项研究发现，ChatGPT将工作人员的工作效率提高了37%。 

Most of the A.I.-driven tools used by nonprofits bear little resemblance to more advanced (and expensive) A.I. like Google’s Bard or DALL-E, which can generate their own text and images. Simpler forms of A.I., like Apple’s Siri or even an automatic spam filter, focus instead on analyzing and making predictions based on existing data. 

非营利组织使用的大多数人工智能驱动工具，与谷歌的Bard或DALL-E等更先进（也更昂贵）的人工智能几乎没有什么相似之处。后者可以生成自己的文本和图像。更简单的人工智能，就像是苹果公司的Siri，甚至是垃圾邮件自动过滤器，都是基于现有数据进行分析和预测。 

For example, the Trevor Project, a nonprofit that provides crisis support to LGBTQ youths, worked with Google.org to build a chatbot to train volunteers and A.I. that identifies the highest-risk young people through chat and puts them in touch with a volunteer. 

例如，为LGBTQ青年提供危机支持的非营利组织特雷弗项目（Trevor Project）与Google.org合作建立了一个聊天机器人，用于培训志愿者和人工智能，通过聊天识别风险最高的年轻人，并让他们与志愿者取得联系。

In the past five years, the American Red Cross has launched more than 20 A.I.-powered projects, including disaster-response chatbots that can help people find the nearest shelter and algorithms that can predict levels of attendance — and anticipate staffing needs — at future blood drives. 

在过去五年中，美国红十字会启动了20多个人工智能项目，其中包括可以帮助人们找到最近避难所的灾难响应聊天机器人，以及可以预测未来献血活动的参加人数和人员需求的算法。

One project uses a tool similar to the Greater DC Diaper Bank’s machine-learning model to determine which areas of the country have the highest risk of fire. Using that data, a campaign to install free smoke alarms around the country has been able to target the communities most at risk. 

其中一个项目使用与大特区纸尿裤银行的机器学习模型类似的工具，来确定全美国哪些地区火灾风险最高。利用这些数据，在全美国范围内开展的免费安装烟雾报警器的活动，就能够锁定那些风险最高的社区。 

More recently, the group has begun exploring more advanced deep-learning models, which rely on much larger datasets than other forms of A.I. and can produce more complicated predictions and analyses. Two new tools, which are nearly ready for pilot testing, will allow the group to automatically assess the damage level of disaster-stricken communities using drone footage and a set of GoPro video cameras affixed to a car. 

最近，该组织开始探索更先进的深度学习模型。这种模型依赖于比其他形式人工智能大得多的数据集，可以进行更复杂的预测和分析。该组织的两款新工具已准备就绪，即将进行试点测试。这两款新工具将使该组织能够利用无人机拍摄的画面和安装在汽车上的GoPro相机，来自动评估受灾社区的受破坏程度。 

“Identifying damage takes a lot of time because you need to have a lot of people on the ground going door-to-door,” says Sajit Joseph, chief innovation officer at the American Red Cross. “The process could take weeks — and technology’s changing that to hours, or maybe days.” 

美国红十字会的首席创新官萨吉特·约瑟夫（Sajit Joseph）说：“确定损失需要大量时间，因为你需要派很多人在现场挨家挨户地查看。这个过程可能需要数周时间，而该技术正在将这个时间缩短为数小时，或者数天。”

While most of the American Red Cross’s A.I. tools are developed in-house through a dedicated innovation team, the newer and more technically advanced projects have been built with the support of Microsoft and Amazon Web Services. 

虽然美国红十字会的大部分人工智能工具，都是通过专门的创新团队在内部开发的。但更新的、技术更先进的项目，则是在微软和亚马逊网络服务的支持下建立的。 

The disaster nonprofit has also begun thinking about how it might use generative A.I., the technology behind ChatGPT, for internal processes. A new volunteer, for example, might soon be able to ask a chatbot for a bite-size explanation of how to conduct shelter counts without sifting through thousands of internal Red Cross documents. 

这家非营利性救灾组织也开始考虑如何在内部流程中使用生成式人工智能（ChatGPT背后的技术）。例如，一名新来的志愿者可能很快就能向聊天机器人询问，关于如何进行避难所清点的简单解释，而无需翻阅成千上万的红十字会内部文件。 

Still, the group is in no rush to deploy the technology, which has been plagued by bias and privacy concerns, to external users, says Joseph. Critics of A.I. contend that the technology often replicates and scales up the racial and gender biases embedded in its algorithms, while exposing user information or data. It’s important to choose the right projects for such advanced tools, he says, and to make sure that employees and the public alike understand them before they’re deployed. 

约瑟夫说，尽管如此，该组织并不急于向外部用户部署这项饱受偏见和隐私问题困扰的技术。人工智能的批评者认为，该技术经常复制和扩大算法中的种族和性别偏见，同时暴露用户信息或数据。他说，为这种先进工具选择合适的项目非常重要，在部署之前要确保员工和公众都能理解这些工具。 

“The opportunity with these A.I. models is to change the way that work is done,” says Joseph. “It takes a little time to make sure that change is really well understood.” 

约瑟夫说：“这些人工智能模型带来的机会在于改变工作方式。这需要一点时间来确保人们真正理解这种改变。”

Ensuring that projects are properly planned and targeted is key to using an A.I. program that genuinely advances nonprofits’ missions, says Jacob Metcalf, program director at Data & Society, where he leads an initiative researching the impact of A.I. 

数据与社会（Data & Society）项目总监雅各布·梅特卡夫（Jacob Metcalf）说，确保项目规划合理、目标明确，是利用人工智能项目来真正推进非营利组织使命的关键。 

Some nonprofits and government agencies have already generated controversy for biting off more A.I. than they could chew. In Pittsburgh, a child-welfare tool, built to lighten the load for overwhelmed city social workers, has been accused of discrimination against families with disabilities. The mental health hotline Crisis Text Line came under fire for sharing user data with its for-profit A.I.-driven customer service spinoff, and the National Eating Disorders Association was criticized last year for replacing its hotline staff with a problem-plagued chatbot. 

一些非营利组织和政府机构已经因过度利用人工智能而引发争议。在匹兹堡，一个为减轻城市社会工作者负担而开发的儿童福利工具，被指控歧视残疾家庭。心理健康热线Crisis Text Line，因与其营利性人工智能客户服务子公司共享用户数据而受到抨击。美国全国饮食失调协会（National Eating Disorders Association）去年也因用问题聊天机器人取代热线工作人员，而受到批评。 

“If all you have is a hammer, everything looks like a nail,” says Metcalf, warning against an overly zealous approach that ignores existing biases or expects that the “solution is A.I. before you even figure out the problem.” 

梅特卡夫说：“如果你有的只是一把锤子，那么一切看起来都像钉子。”他警告说，不要过分热衷于那些忽视现有偏见的方法，或者“在你弄清楚问题之前”，期望“人工智能就是解决方案”。 

It’s the biggest lesson Gosselink herself has learned while leading Google’s A.I. for social-good initiatives. Not everything needs to be A.I., she says. 

这也是布里吉特·戈斯林克本人在领导谷歌人工智能社会公益项目时，学到的最大一课。她说，并非所有事情都需要人工智能。 

“I worry about people thinking it’s an inaccessible opportunity for them,” she says. “Or getting so distracted by the hype that it becomes something they’re investing in before they should be.” 

戈斯林克说：“我担心人们认为，这对他们来说是一个可望而不可及的机会。或者被炒作搞得心烦意乱，以至于在他们应该投资之前，就已经投资了。” 

Instead, before every project, she recommends that nonprofits ask themselves if A.I. will advance the organization’s mission. Many nonprofits could benefit from starting with smaller-scale data projects to improve their operations, she says. 

相反，戈斯林克建议非营利组织在开展每个项目之前，先问问自己人工智能是否能推动组织的使命。她说，许多非营利组织可以从较小规模的数据项目入手，改善他们的运营。 

“You don’t need to be all the way there. You don’t need to be programming a robot or developing some profound new algorithm,” says Gosselink. “Most of what we’re doing here is really thinking about how to have more data-driven insights.” 

戈斯林克说：“你不需要完全做到这一点。你不需要给机器人编程，也不需要开发什么高深的新算法。我们在这里所做的大部分工作都是在真正思考，如何获得更多数据驱动的洞察力。”