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How AI with Design is empowering people to tackle complexity.
人工智能与设计如何使人们能够解决复杂性。
By James O’Neill, Service and Systems Design Lead, Fjord at The Dock, and Connor Upton, Group Design Director, Fjord at The Dock.
Dock峡湾服务和系统设计负责人James O'Neill和Dock峡湾组设计总监Connor Upton。
“Everything should be made as simple as possible, but no simpler.” — attributed to Albert Einstein.
“一切都应该尽可能简单，但不要简单。” -归因于爱因斯坦。
Designed Intelligence is Fjord and Accenture’s approach to unlocking the full potential of human collaboration with AI. In our previous articles we talk about how AI technologies can help augment strategic decision making and build better experiences. Empowerment is the third pillar of Designed Intelligence and focusses on how design can make intelligent systems more transparent, more adaptable and ultimately more resilient.
“设计智能”是峡湾和埃森哲的方法，旨在发掘人类与AI合作的全部潜力。 在之前的文章中，我们讨论了AI技术如何帮助增强战略决策并建立更好的体验。 授权是“设计智能”的第三大Struts，重点在于设计如何使智能系统更透明，更具适应性并最终更具弹性。
We live in a world of increasing complexity. Physical objects are becoming ‘smart’ and ‘connected’ with cars, toys, medical devices and even toothbrushes getting an AI flair. These products come with a myriad of digital services that plug into larger networks of people, organisations, and infrastructure. These services, in turn, are just one part of a broader digital economy. The end result is unprecedented levels of customer choice; Amazon currently lists 3 billion products, Airbnb has over 5 million property listing, YouTube is expanding with three hundred hours of new content every minute. These are inhuman scales of information, so we increasingly depend on algorithms to present the relevant information to us. Businesses also find themselves increasingly interconnected in terms of supply chains and digital infrastructures. A MIT Sloan article states that it “has become too complex and is moving too rapidly for boards and CEOs to make good decisions without intelligent systems”. As complexity increases, it seems that AI powered recommendation systems and algorithmic decision support are not just helpful, they are becoming essential.
我们生活在一个日益复杂的世界中。 物理对象正变得与汽车，玩具，医疗设备甚至牙刷变得“智能”和“连接”， 甚至变得具有AI才能。 这些产品带有无数的数字服务，可插入更大的人员，组织和基础架构网络。 反过来，这些服务只是更广泛的数字经济的一部分。 最终结果是前所未有的客户选择水平； 亚马逊目前列出30亿种产品，爱彼迎(Airbnb)拥有超过500万个房地产清单，YouTube每分钟增加300个小时的新内容。 这些是非人类的信息规模，因此我们越来越依赖算法向我们提供相关信息。 企业还发现自己在供应链和数字基础设施方面越来越相互联系。 麻省理工学院斯隆 ( MIT Sloan)的一篇文章指出，“它变得过于复杂，并且为董事会和首席执行官提供的速度太快，以至于没有智能系统就无法做出明智的决定”。 随着复杂性的增加，人工智能支持的推荐系统和算法决策支持似乎不仅有用，而且变得至关重要。
At the same time there is wariness about the role that AI will play in relation to the future of work. Much of the commercial narrative to date has focused on smart automation that results in improved quality and reduced labour costs. Critics of this approach point to examples where over-dependency on AI in can result in ethical issues, fragile systems and even legal challenges. In reality AI enabled products and services exist as part of larger technical, social, and economic systems and can have both positive and negative effects. If we really want to get the best of AI (and avoid the worst) then we will need to understand the role it plays in these systems, and how to design for them.
同时，人们对于AI将在未来工作中扮演的角色持谨慎态度。 迄今为止，许多商业叙述都集中在智能自动化上，从而提高了质量并降低了人工成本。 对这种方法的批评者指出了这样的例子：对AI的过度依赖会导致道德问题 ， 脆弱的系统甚至法律挑战 。 实际上，支持AI的产品和服务作为较大的技术，社会和经济系统的一部分存在，并且可能具有正面和负面影响。 如果我们真的想获得最好的AI(并避免最糟糕的AI)，那么我们将需要了解AI在这些系统中的作用，以及如何为它们设计。
To do this in our projects, we’ve had to shift our design focus from the level of user centricity to the level of systems. At the systems level, design is about balancing the automation of tasks while maintaining the value of human problem solving. Design is also about mapping the complex relationships and interdependencies involved in modern businesses and digital platforms so that they can be built, maintained and improved. Here are a couple of the things we’ve learned.
为此，我们必须将设计重点从以用户为中心的层次转移到系统的层次。 在系统级别，设计要平衡任务的自动化，同时保持解决人类问题的价值。 设计还涉及映射现代业务和数字平台中涉及的复杂关系和相互依赖性，以便可以对其进行构建，维护和改进。 这是我们学到的一些知识。
The world changes fast these days. 2020 alone seems to have cycled through a bewildering series of world changing events. So how do you design with AI, when the models you build today might not be able to cope with the reality of tomorrow? Even in times of stability, AI models that interact with the real world degrade in accuracy over time, which can lead to undesired or even dangerous outcomes.
这些天世界瞬息万变。 仅2020年似乎就经历了一系列令人眼花wil乱的世界变化事件。 那么，当您今天构建的模型可能无法应对明天的现实时，如何使用AI进行设计？ 即使在稳定时期， 与现实世界交互的AI模型也会随着时间的流逝而降低准确性，这可能会导致不良后果甚至是危险后果。
One way of coping with this is to think about AI solutions as parts of wider systems. These involve people, AI, and other technologies working together to create a more resilient system, one that is able to adapt to change rather than break under stress. We took this approach when designing the Accenture Logistics Platform, an AI driven demand prediction, scheduling and route planning tool that empowers postal services to support same day delivery. By paying careful attention to how the people in the system operate, we were able to design an AI solution that advises postal workers but also allows them to override suggestions. This way, the workers can react to on-the-ground situations and unexpected events without being slaves to the algorithm. This level of user control maintained the workers autonomy and was critical to drive adoption of the solution. But just as important, through their deviations and workarounds, users become trainers of the AI, providing it with feedback and new information that allows it to learn and improve. If you design your AI solution to interact with its surrounding ecosystem you can ensure it supports meaningful actions even in the most changeable environment.
解决此问题的一种方法是将AI解决方案视为更广泛系统的一部分。 其中涉及人员，人工智能和其他技术的共同努力，以创建一个更具弹性的系统，该系统能够适应变化而不是在压力下破裂。 我们在设计埃森哲物流平台时采用了这种方法，该平台是一个由AI驱动的需求预测，日程安排和路线规划工具，可以使邮政服务支持当天交付。 通过仔细关注系统中人员的操作方式，我们能够设计出一个人工智能解决方案，为邮政人员提供建议，但也允许他们忽略建议。 这样，工作人员可以对实际情况和意外事件做出React，而不必成为算法的奴隶。 这种级别的用户控制保持了工人的自主权，对于推动解决方案的采用至关重要。 但同样重要的是，通过他们的偏差和变通方法，用户成为了AI的培训者，为其提供了反馈和新信息，从而使它能够学习和改进。 如果您将AI解决方案设计为与其周围的生态系统进行交互，则即使在多变的环境中也可以确保它支持有意义的动作。
Click to watch video. 单击以观看视频 。Building resilience is about feedback and communication. It’s about collaboration between people and AI, and this requires trust. However there is evidence that the majority of people (at least in the USA) do not trust algorithms to make decisions that will affect their lives. Building trust in AI is a multifaceted problem but transparency and fairness are two of the biggest challenges we face.
建立弹性是关于反馈和沟通。 这是关于人与AI之间的协作，这需要信任。 但是， 有证据表明 ，大多数人(至少在美国)不信任算法来做出会影响他们生活的决策。 建立对AI的信任是一个多方面的问题，但是透明度和公平性是我们面临的两个最大挑战。
When we talk about transparency we mean the ability for an AI’s logic to be understood by a person. In principle this would seem like a sensible thing to do, so much so that it has been enshrined in European Legislation. In practice, however, some of the most effective forms of AI involve levels of mathematical complexity that defy simple explanation. This raises technical challenges in terms of the types of algorithms we use and communications challenges for how we represent models and their outputs to people.
当我们谈论透明度时，是指人们能够理解AI逻辑的能力。 原则上，这样做似乎是明智的选择，以至于它已被欧洲立法所确立。 然而，实际上，某些最有效的AI形式涉及到数学上的复杂程度，无法进行简单的解释。 在我们使用的算法类型以及如何表达模型及其对人的输出方面的交流挑战方面，这带来了技术挑战。
When we talk about fairness we mean the ability for an AI to generate results that align with our societal values and laws around discrimination and bias. This has proven to be a major challenge with examples of gender and racial bias in AI applications across finance, law and healthcare. AI models are only as good as the data they are trained upon. The problem is that this data may contain historic latent biases or be unrepresentative of a wider population. In high stakes applications, like facial recognition in law enforcement, some companies are choosing to back away from AI altogether.
当我们谈论公平时，是指AI能够产生与我们的社会价值观和围绕歧视和偏见的法律相一致的结果的能力。 在金融 ， 法律和医疗领域的 AI应用中，性别和种族偏见的例子已证明这是一个重大挑战。 AI模型仅取决于其训练的数据。 问题在于该数据可能包含历史性潜在偏差或无法代表更广泛的人群。 在高风险的应用中，例如执法中的面部识别，一些公司选择完全放弃使用AI 。
Explainable AI and algorithmic fairness are not technological challenges they are systems problems. The Algorithmic Fairness project at the Dock, Accenture’s Global Innovation Hub, examined fairness and transparency in the context of banking systems and credit risk. Mapping the AI model lifecycle from problem identification, through development process, launch and reviews revealed the points where human input was essential. Here, bias detection and mitigation strategies could be developed and assessed by data scientists and business domain experts together. To facilitate communication between these very different stakeholders we designed a set of visualisations and simulations that increased the transparency of the data and its effect on the models. This approach allowed them to understand, discuss and develop plans for tackling the complex problem of bias in machine learning.
可解释的AI和算法公平性不是技术挑战，它们是系统问题。 埃森哲全球创新中心Dock的算法公平项目研究了银行体系和信贷风险情况下的公平和透明度。 从问题识别到开发过程，启动和审查的AI模型生命周期图揭示了人工输入至关重要的要点。 在这里，偏差检测和缓解策略可以由数据科学家和业务领域专家共同开发和评估。 为了促进这些截然不同的利益相关者之间的交流，我们设计了一组可视化和模拟，以增加数据的透明度及其对模型的影响。 这种方法使他们能够理解，讨论和制定解决机器学习偏见复杂问题的计划。
Mapping the AI model lifecycle. 映射AI模型生命周期。AI has become a critical technology for coping with complexity in the modern world. It’s helping to tackle important problems like drug discovery and climate change but it can also result in unintended consequences for society and business. Taking a systems design approach shifts our focus from the particulars of AI algorithms to the people, organisations, and technologies that surround it. We’re expanding our design toolkit to meet this challenge. A systems view helps us define how an AI interacts with its wider ecosystem and help us articulate the consequences of these interactions so that we can make more conscientious design decisions.
人工智能已经成为应对现代世界复杂性的关键技术。 它有助于解决药物发现和气候变化等重要问题，但也可能给社会和企业带来意想不到的后果。 采用系统设计方法会将我们的注意力从AI算法的细节转移到围绕它的人员，组织和技术。 我们正在扩展设计工具包以应对这一挑战。 系统视图可帮助我们定义AI如何与其更广泛的生态系统交互，并帮助我们阐明这些交互的后果，以便我们可以做出更认真的设计决策。
No man is an island, and no technology is either. If we think of AI as part of a system we can use it to its best, and avoid its worst.
没有人是孤岛，也没有技术。 如果我们将AI视为系统的一部分，则可以最大程度地使用它，而避免最坏的情况。
Read more about Designed Intelligence and envisioning new strategies and enhancing the human experience.
了解更多关于 设计的智能 和 电子商务 nvisioning新战略 和 增强人类的经验 。
翻译自: https://medium.com/design-voices/designed-intelligence-empowering-people-within-systems-ec2c1d029ebe
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