人工智能策略 (AI Strategy)

AI is incredibly useful, but it’s so complicated that it’s challenging to get people to adopt it, at least consciously. We may like that our phones can automatically tag our photos but often forget that it’s AI powering the tool. People are hesitant to trust a technology they don’t understand well, especially if it is, well, a bit creepy.

A I是非常有用的，但它非常复杂，极具挑战性的让人们去接受它，至少自觉。 我们可能喜欢我们的手机可以自动标记我们的照片，但经常忘记它是由AI驱动的工具。 人们不愿信任他们不太了解的技术，尤其是如果它有点令人毛骨悚然 。

“I have exposure to the most cutting-edge AI, and I think people should be really concerned by it. AI is a fundamental risk to the existence of human civilisation.” Elon Musk at the National Governors Association, 2017

“我接触了最前沿的人工智能，我认为人们应该对此感到真正的关注。 人工智能是人类文明生存的根本风险。” 伊隆·马斯克(Elon Musk)，美国全国州长协会，2017年

AI can sometimes predict our own behaviour more accurately than the real people we know. We don’t like feeling that we can’t control how our data is collected and used, especially when AI uses that data to get us to take a particular action. It is well known that UX is an important factor to consider when creating a model, but a lot of data scientists fail to forget a major part of a positive UX: minimising the creep-factor of AI.

人工智能有时可以比我们认识的真实人更准确地预测我们的行为。 我们不喜欢无法控制数据的收集和使用方式，特别是当AI使用该数据使我们采取特定行动时。 众所周知，UX是创建模型时要考虑的重要因素，但是许多数据科学家未能忘记积极UX的主要部分：最小化AI的蠕变因子。

AI使“我们的手机在监视我们”和其他阴谋 (“Our Phones Are Spying On Us” and Other Conspiracies Made Possible by AI)

Thanks to advances in technology and the exponential availability of data, AI has gotten much more powerful — and accurate. That’s exciting for those of us passionate about technology, but it’s also part of the creepiness problem. Algorithms today can predict so much more about an individual with less information (or at least information knowingly provided). Sometimes AI can seem to read our thoughts — or at least must be secretly listening to us. Right?

得益于技术的进步和指数级的数据可用性，人工智能已变得更加强大，准确。 对于我们中那些热衷于技术的人来说，这令人兴奋，但这也是令人毛骨悚然的问题的一部分。 如今的算法可以用更少的信息(或至少是已知提供的信息)对个体进行更多预测。 有时，AI似乎可以读懂我们的想法，或者至少必须秘密地听我们的话。 对？

That our phones are secretly recording and spying on us for marketing purposes has been a top conspiracy theory on the internet for a few years — one that even some respected tech experts and journalists think is true. It’s been endlessly debated everywhere from the BBC to Esquire to Vox. There was even a /reply-all/ podcast about your phone listening on you as early as 2017. Nowadays, almost half of Americans and increasing numbers of Europeans are concerned their phones are recording private conversations without their permission for ad targeting.

几年来，我们的手机正在秘密地记录和监视我们用于营销目的，一直是互联网上的一种极高的阴谋论，甚至一些受人尊敬的技术专家和记者也认为这是真的 。 从BBC到Esquire到Vox，到处都有无休止的辩论。 甚至有一个/ reply-all /播客，关于您的手机早在2017年就在听着您的声音。如今， 几乎有一半的美国人和越来越多的欧洲人担心他们的手机在未经许可进行广告定位的情况下录制私人对话。

There’s no way to 100% know how much audio our phones capture, but that’s really beside the point: most experts have determined that most apps don’t use recordings for ad targeting. Not because they can’t, but because there’s no need. AI tracking is so sophisticated that it’s already this accurate without the hassle of technological and storage demands of audio collection. We inadvertently provide so much information about ourselves that we can be eerily well-targeted regardless.

没有办法让100％知道我们的手机捕获了多少音频，但这实际上是无关紧要的：大多数专家确定大多数应用程序不使用录音来进行广告定位。 不是因为他们做不到，而是因为没有必要 。 AI跟踪是如此复杂，以至于它已经如此精确，而无需担心音频采集的技术和存储需求。 我们无意间提供了太多关于自己的信息，因此无论如何我们都可以精准地锁定目标。

“We tend to think about surveillance in the way that humans do. That if somebody learned about something that you talked about with a friend that meant that they were listening to you, but I think it’s harder for most people to make the connection between how much they give away in their online activities to these companies that can target advertisements to them”. -David Choffnes, Northeastern University Professor and researcher on the topic of mobile privacy and security

“我们倾向于以人类的方式来考虑监视。 如果有人了解了您与朋友谈论的内容，这意味着他们在听您讲话，但我认为，对于大多数人而言，很难将他们在在线活动中所付出的钱与这些可以针对的公司联系起来给他们的广告”。 -David Choffnes，东北大学教授，研究员，研究移动隐私和安全性

Newer conspiracies swirl around the many virus-tracking apps released by governments around the world. People worry that downloading these apps will allow the government to track them forever, even though it is illegal. Many have compared these apps to the type of surveillance seen more commonly in China. Here in Italy, the Immuni app has failed to gain much traction due to some of these conspiracies, as well as very real concerns about privacy and security. More fears about creepy, always-watching AI is yet another consequence of Covid-19.

世界各国政府发布的许多病毒跟踪应用程序都围绕着新的阴谋。 人们担心下载这些应用程序将使政府永远跟踪它们，即使这是非法的。 许多人将这些应用程序与在中国更常见的监视类型进行了比较。 在意大利，由于其中一些阴谋以及对隐私和安全性的真正关注， Immuni应用未能获得太多关注。 Covid-19的另一个后果是，人们对令人毛骨悚然，时刻关注的AI感到更加担忧。

All-seeing, all-knowing AI may not be even close to reality, but that doesn’t mean we’re less wary about current AI capabilities. Forget Skynet; it’s Facebook’s data collection and tracking that we’re worried about. When the most visible examples of AI in our lives seems to be powered by spying on us, it’s no wonder so many people find AI so freaky.

无所不知，无所不知的AI可能甚至还无法接近现实，但这并不意味着我们对当前的AI功能没有那么警惕。 忘记天网； 我们担心的是Facebook的数据收集和跟踪。 当我们生活中最明显的AI例子似乎受到监视我们的推动时，难怪有这么多人发现AI如此怪异。

可以使用AI来提供个性化建议而不会导致我们毛骨悚然吗？ (Can AI Ever Be Used to Provide Personalised Recommendations without Creeping Us Out?)

Personalisation is a good goal. Getting the right offer at the right time helps us as much as it helps the company making that offer. Often, even people who claim to feel concerned about AI’s spread often have no problem using it if the AI helps them solve a problem more efficiently.

个性化是一个好目标。 在正确的时间获得正确的报价对我们的帮助与对提供报价的公司的帮助一样。 通常，即使声称自称担心AI传播的人，使用AI也会毫无问题 帮助他们解决问题 更有效率。

In reality, most AI doesn’t creep us out. Most AI we interact with every day goes completely unnoticed. Sometimes that’s because the AI isn’t sophisticated enough to bother us. It is pretty difficult to find a customer service chatbot that can only respond to precise input more menacing than irritating. Other AI fits so seamlessly into the processes that we used before that all we notice is an improvement in service. When Apple created Face ID to unlock iPhones, most people were excited by the convenience, not worried about the implications of the AI.

实际上，大多数AI不会使我们失望。 我们每天与之交互的大多数AI完全没有被注意到。 有时是因为AI不够复杂，无法打扰我们。 很难找到一个仅能响应精确输入而不是令人讨厌的客户服务聊天机器人。 其他AI与我们以前使用的流程非常无缝地融合在一起，而我们注意到的只是服务方面的改进。 当苹果公司创建Face ID来解锁iPhone时，大多数人对这种便利感到兴奋，而不必担心AI的含义。

Most often, however, AI goes unnoticed because we only see the results. We have no direct interaction with the AI itself; we only see how it makes our lives easier. For example, no customer will likely ever know that they can purchase their desired item in the right size because the Evo Replenish algorithm used predictive supply chain to prevent a stockout of a newly popular shirt. Customers have no way of knowing how the store makes inventory decisions, and they probably wouldn’t care. The lack of empty shelves is convenient, and the AI that prevents the problem isn’t creepy because its predictions and recommendations aren’t made on the individual level. If no one is saying I personally will buy a particular product, just that 15 people will that week, I am not concerned about my privacy.

但是，大多数情况下，由于我们只看到结果，所以人们往往不会注意到AI。 我们与AI本身没有直接互动； 我们只看到它如何使我们的生活更轻松。 例如，由于Evo Replenish算法使用预测性供应链来防止新近流行的衬衫缺货，因此没有客户会知道他们可以购买合适尺寸的所需商品。 客户无法知道商店如何做出库存决策，他们可能不在乎。 缺少空架子很方便，并且防止出现问题的AI并不是令人毛骨悚然的，因为它的预测和建议不是在个人层面上做出的。 如果没有人说我个人会购买一种特定的产品，那么那周只有15个人会购买，我并不担心自己的隐私。

AI forecasts and personalisation unsettle us most when we can’t understand how it came to a conclusion about us personally or when the AI is too believably human but not disclosed as AI. As such, merely embracing transparency and human-centric design will go a long way towards making AI less ominous.

当我们无法理解如何得出关于我们的个人结论时，或者当AI过于人性化却未被披露为AI时，AI的预测和个性化将使我们最不满意。 因此，仅拥抱透明性和以人为本的设计将大大降低AI的不祥性。

非蠕动的AI是透明的 (Non-Creepy AI Is Transparent)

The more transparent you are about how your AI works, the less creepy it seems. We tend to look sceptically at things we don’t understand. When you face something that you don’t understand that seems to understand you, it’s not just suspicious; it’s creepy. Popular culture has shown us plenty of ways AI can potentially run amok. If we don’t understand how the technology works now, how could we possibly see disaster coming? Without transparency, AI feels like more of a threat — which ultimately makes AI so creepy.

您对AI的工作方式越透明，看起来越不令人毛骨悚然。 我们倾向于怀疑地看待我们不了解的事情。 当您面对一些您不了解的东西似乎可以理解您时，这不仅是可疑的。 好诡异。 流行文化向我们展示了AI可以潜在运行的多种方式。 如果我们现在不了解这项技术的运作方式，我们怎么可能看到灾难的来临？ 没有透明性，AI会更像是一种威胁-最终使AI变得如此令人毛骨悚然。

Consider the theory that Facebook is listening to us through our phones again. People are so suspicious because Facebook refuses to disclose what information they collect on us and how they use that data to target ads.

考虑一下Facebook通过电话再次听我们的理论。 人们如此可疑，是因为Facebook拒绝透露他们收集到的关于我们的信息以及他们如何使用这些数据来定位广告。

“[Facebook has] created this problem because they’re really good at collecting information about us. They won’t be very transparent about what they collect or how, and so, you’re basically forcing people to come up with the simplest possible solution for how Facebook knows stuff about them — and that’s that they’re listening in.” -PJ Vogt, Tech Journalist and Co-host of Reply All

“ [Facebook]造成了这个问题，因为他们非常善于收集有关我们的信息。 他们对收集的内容或方式不会很透明，因此，您基本上是在迫使人们想出最简单的解决方案，以了解Facebook如何了解他们的东西，那就是他们在听。 -PJ Vogt，技术记者，全部答复的共同主持人

Transparency also means being clear about how you collected data and what you did with that information. It’s always going to seem creepy to gather tons of identifiable information on individuals if you can’t explain why you need it. That’s why we should be upfront about why particular information is requested and anonymise data whenever possible.

透明度还意味着要清楚如何收集数据以及如何处理这些信息。 如果您无法解释为什么需要收集大量可识别的个人信息，那总是令人毛骨悚然。 这就是为什么我们应该提前了解为什么要求特定信息并尽可能对数据进行匿名处理。

This data transparency doesn’t hurt AI models. At Evo, our algorithms still process data from over 1.2 billion people to make supply chain and pricing forecasts; we just scrape data in such a way that we entirely exclude any identifying information from our databases. The resulting AI recommendations are not affected, but the models become more transparent. It’s possible to be honest about what data you are using and how you sourced it without hurting the accuracy of outcomes, so long as you plan for transparency from day one.

这种数据透明性不会损害AI模型。 在Evo ，我们的算法仍然处理超过12亿人的数据，以进行供应链和价格预测； 我们只是以这样一种方式抓取数据，即完全从数据库中排除所有识别信息。 由此产生的AI建议不会受到影响，但是模型变得更加透明。 只要您计划从第一天开始就保持透明，就可以诚实地使用所使用的数据以及如何获取数据，而不会损害结果的准确性。

以人为本的AI设计可帮助我们建立有用的模型，而这些模型不会让用户感到惊讶 (Human-Centric AI Design Helps Us Build Useful Models That Don’t Creep Out Users)

While transparency is essential, a majority of people will still forgive AI for being a little creepy if it gets them results. Surveys show that almost 75% of us don’t mind intrusive AI if it helps avoid an issue before it develops, solves a problem quickly or minimises complexity. While it may be creepy to know that you’ve been tracked if all it leads to is more spam about a product you considered buying once, we worry less about tracking that warns us of a data breach. We are willing to make compromises when the problem the AI solves helps us, not a company trying to recruit us as customers.

尽管透明度是必不可少的，但大多数人仍会原谅AI令人毛骨悚然(如果获得结果)。 调查显示， 几乎有75％的人不介意侵入性AI，因为它有助于在问题发展之前避免问题，快速解决问题或将复杂性降至最低。 知道您是否曾经被跟踪过是否导致您打算一次购买的产品更多是垃圾邮件，这可能令人毛骨悚然，但对于跟踪警告我们发生数据泄露的跟踪，我们的担心就少了。 当AI解决的问题对我们有帮助时，而不是试图将我们招募为客户的公司，我们愿意做出让步。

Take Google Maps, for example. Few people found it creepy when Maps started automatically suggesting directions for events planned in Google Calendar; it was obvious why Google had that information. People found it much creepier when Maps began making suggestions for directions to meetings or events directly from Gmail that never made it into Calendar. Still, this functionality was useful enough that few people complained. Only once Maps started predicting where you may need to go based on time of day and your habits did any real backlash begin. This recommendation felt more intrusive in part because it made the tracking more visible, but in part because it wasn’t always helpful. If you didn’t need those directions, you were unlikely to feel grateful Google knew where you habitually went.

以Google Maps为例。 当地图自动开始提示在Google日历中计划的活动的方向时，很少有人发现它令人毛骨悚然； 很明显Google为什么拥有这些信息。 当Maps开始直接从Gmail提出会议或活动的方向建议时，人们发现这变得更加令人毛骨悚然，而从未将其纳入Calendar。 尽管如此，此功能仍然足够有用，以至于很少有人抱怨。 只有Maps开始根据一天中的时间和习惯来预测您可能需要去的地方时，任何真正的抵制才开始。 这项建议更具侵入性，部分是因为它使跟踪更加明显，但部分原因却并非总是有帮助。 如果您不需要这些指示，则不太可能会感激Google知道您惯常去了哪里。

That’s why it’s so important to solve the right problems with your AI. Human-centric design of AI prioritises the needs of the end-user and makes sure that AI does not feel intrusive. If you create an algorithm that will solve problems for everyone that interacts with the AI, you’re guaranteed to minimise negative responses. At the very least, AI should respect people’s need for privacy and a sense of control. Put the people who will interact with the AI first and empathise with their perspectives. The resulting human-centric AI feels less unsettling.

这就是用AI解决正确问题如此重要的原因。 以人为本的AI设计优先考虑最终用户的需求，并确保AI不会让人感到不适。 如果您创建的算法可以解决与AI交互的每个人的问题，那么可以确保最大程度地减少负面响应。 至少，AI应该尊重人们对隐私和控制感的需求。 首先将要与AI进行交互的人员并同情他们的观点。 由此产生的以人为中心的AI不会让人感到不安。

不要蠕变 (Don’t Be a Creep)

AI will continue to improve our lives greatly — but only if we can trust it. If we don’t minimise its creepiness, AI cannot reach its full potential. We will all lose out on the benefits while protecting us from the potential downsides less than we think.

人工智能将继续极大地改善我们的生活-但前提是我们能够信任它。 如果我们不将其蔓延程度降至最低，那么人工智能将无法发挥其全部潜力。 我们所有人都会失去收益，同时保护我们免受潜在不利影响的程度比我们想象的要少。

AI will always be complex, but it doesn’t have to be a total mystery. We can be honest about how our AI works and where it gets its data without giving up any commercial secrets. When we put people first and operate transparently, AI becomes less creepy — and our lives get easier.

人工智能将永远是复杂的，但并不一定是一个谜。 我们可以诚实地说出我们的AI如何工作以及如何在不丢失任何商业秘密的情况下获取数据。 当我们把人们放在第一位并透明地进行操作时，人工智能就不再那么令人毛骨悚然了，我们的生活变得更加轻松。

Special thanks to Kaitlin Goodrich for contributing to this article.

特别感谢Kaitlin Goodrich对本文的贡献。

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