2022年10月21日 专家：人类参与有助于防止人工智能错误
Security experts say artificial intelligence (AI) systems used by businesses can make serious, costly mistakes. But one way to avoid such mistakes is for companies to employ humans to closely watch the AI.
安全专家表示，企业使用的人工智能(AI) 系统可能会犯严重且代价高昂的错误。但避免此类错误的一种方法是让公司雇佣人类密切关注人工智能。
One example of AI problems that can affect businesses happened early in the COVID-19 pandemic. It involved the credit scoring company Fair Isaac Corporation, which is known as FICO.
可能影响企业的 AI 问题的一个例子发生在 COVID-19 大流行初期。它涉及信用评分公司 Fair Isaac Corporation，该公司被称为 FICO。
FICO is used by about two-thirds of the world's largest banks to help make lending decisions. The company's systems are also used to identify possible cases of credit fraud.
世界上大约三分之二的最大银行使用 FICO 来帮助做出贷款决策。该公司的系统还用于识别可能的信用欺诈案件。
FICO officials recently told Reuters news agency that one of the company's AI systems misidentified a large number of credit card fraud cases. At the time, the pandemic had caused a large increase in online shopping. The AI tool considered the rise in online shopping to be the result of fraudulent activity.
FICO 官员最近告诉路透社，该公司的一个人工智能系统误判了大量信用卡欺诈案件。当时，大流行导致网上购物大量增加。人工智能工具认为网上购物的兴起是欺诈活动的结果。
As a result, the AI system told banks to deny millions of purchase attempts from online buyers. The incident happened just as people were hurrying to buy products that were in short supply in stores.
结果，人工智能系统告诉银行拒绝在线买家的数百万次购买尝试。事情发生在人们争相抢购店内供不应求的商品之际。
But FICO told Reuters that in the end, very few buyers had their purchase requests denied. This is because a group of experts the company employs to observe, or monitor, its AI systems recognized the false fraud identifications. The workers made temporary adjustments to avoid an AI-ordered block on spending.
但 FICO 告诉路透社，最终很少有买家的购买请求被拒绝。这是因为该公司雇用的一组专家来观察或监控其人工智能系统识别出虚假的欺诈识别。工人们进行了临时调整，以避免人工智能命令的支出限制。
FICO says the expert team is quickly informed about any unusual buying activity that the AI systems might misidentify.
FICO 表示，专家团队会迅速获悉人工智能系统可能误认的任何异常购买活动。
But these kinds of corporate teams are not that common, Reuters reports. Last year, FICO and the business advisory company McKinsey & Company carried out separate studies on the subject. They found that most organizations involved in the study were not closely watching their AI-based programs.
但据路透社报道，这类公司团队并不常见。去年，FICO 和商业咨询公司麦肯锡公司就该主题进行了单独研究。他们发现参与这项研究的大多数组织都没有密切关注他们基于人工智能的程序。
Experts say AI systems mainly make mistakes when real-world situations differ from the situations used in creating the intelligence. In FICO's case, it said its software expected more in-person than online shopping. This led the system to identify a greater share of financial activity as problematic.
专家表示，当现实世界的情况与创建智能所使用的情况不同时，人工智能系统主要会犯错误。在 FICO 的案例中，它表示其软件预计比在线购物更多。这导致系统将更大比例的金融活动识别为有问题的。
Seasonal differences, data-quality changes or extremely unusual events – such as the pandemic – can lead to a series of bad AI predictions.
季节性差异、数据质量变化或极不寻常的事件（例如大流行）可能导致一系列糟糕的 AI 预测。
Aleksander Madry is the director of the Center for Deployable Machine Learning at the Massachusetts Institute of Technology. He told Reuters the pandemic must have been a "wake-up call" for businesses not closely monitoring their AI systems. This is because AI mistakes can cause huge problems for businesses that do not effectively manage the systems.
Aleksander Madry 是麻省理工学院可部署机器学习中心的主任。他告诉路透社，对于没有密切监控其人工智能系统的企业来说，这种流行病一定是一个“警钟”。这是因为人工智能错误可能会给无法有效管理系统的企业带来巨大的问题。
"That's what really stops us currently from this dream of AI revolutionizing everything," Madry said.
“这就是目前真正阻止我们实现人工智能彻底改变一切的梦想的原因，”Madry 说。
Urgency has been added to the issue since the European Union plans to pass a new AI law as soon as next year. The law requires companies to do some observation of their AI systems. Earlier this month, the U.S. administration also proposed new guidelines aimed at protecting citizens from the harmful effects of AI. In the guidelines, U.S. officials called for observers to ensure AI system "performance does not fall below an acceptable level over time."
自欧盟计划最早于明年通过一项新的人工智能法以来，这一问题变得更加紧迫。法律要求公司对其人工智能系统进行一些观察。本月早些时候，美国政府还提出了新的指导方针，旨在保护公民免受人工智能的有害影响。在指导方针中，美国官员呼吁观察员确保人工智能系统“随着时间的推移，性能不会低于可接受的水平”。
安全专家表示，企业使用的人工智能(AI) 系统可能会犯严重且代价高昂的错误。但避免此类错误的一种方法是让公司雇佣人类密切关注人工智能。
One example of AI problems that can affect businesses happened early in the COVID-19 pandemic. It involved the credit scoring company Fair Isaac Corporation, which is known as FICO.
可能影响企业的 AI 问题的一个例子发生在 COVID-19 大流行初期。它涉及信用评分公司 Fair Isaac Corporation，该公司被称为 FICO。
FICO is used by about two-thirds of the world's largest banks to help make lending decisions. The company's systems are also used to identify possible cases of credit fraud.
世界上大约三分之二的最大银行使用 FICO 来帮助做出贷款决策。该公司的系统还用于识别可能的信用欺诈案件。
FICO officials recently told Reuters news agency that one of the company's AI systems misidentified a large number of credit card fraud cases. At the time, the pandemic had caused a large increase in online shopping. The AI tool considered the rise in online shopping to be the result of fraudulent activity.
FICO 官员最近告诉路透社，该公司的一个人工智能系统误判了大量信用卡欺诈案件。当时，大流行导致网上购物大量增加。人工智能工具认为网上购物的兴起是欺诈活动的结果。
As a result, the AI system told banks to deny millions of purchase attempts from online buyers. The incident happened just as people were hurrying to buy products that were in short supply in stores.
结果，人工智能系统告诉银行拒绝在线买家的数百万次购买尝试。事情发生在人们争相抢购店内供不应求的商品之际。
But FICO told Reuters that in the end, very few buyers had their purchase requests denied. This is because a group of experts the company employs to observe, or monitor, its AI systems recognized the false fraud identifications. The workers made temporary adjustments to avoid an AI-ordered block on spending.
但 FICO 告诉路透社，最终很少有买家的购买请求被拒绝。这是因为该公司雇用的一组专家来观察或监控其人工智能系统识别出虚假的欺诈识别。工人们进行了临时调整，以避免人工智能命令的支出限制。
FICO says the expert team is quickly informed about any unusual buying activity that the AI systems might misidentify.
FICO 表示，专家团队会迅速获悉人工智能系统可能误认的任何异常购买活动。
But these kinds of corporate teams are not that common, Reuters reports. Last year, FICO and the business advisory company McKinsey & Company carried out separate studies on the subject. They found that most organizations involved in the study were not closely watching their AI-based programs.
但据路透社报道，这类公司团队并不常见。去年，FICO 和商业咨询公司麦肯锡公司就该主题进行了单独研究。他们发现参与这项研究的大多数组织都没有密切关注他们基于人工智能的程序。
Experts say AI systems mainly make mistakes when real-world situations differ from the situations used in creating the intelligence. In FICO's case, it said its software expected more in-person than online shopping. This led the system to identify a greater share of financial activity as problematic.
专家表示，当现实世界的情况与创建智能所使用的情况不同时，人工智能系统主要会犯错误。在 FICO 的案例中，它表示其软件预计比在线购物更多。这导致系统将更大比例的金融活动识别为有问题的。
Seasonal differences, data-quality changes or extremely unusual events – such as the pandemic – can lead to a series of bad AI predictions.
季节性差异、数据质量变化或极不寻常的事件（例如大流行）可能导致一系列糟糕的 AI 预测。
Aleksander Madry is the director of the Center for Deployable Machine Learning at the Massachusetts Institute of Technology. He told Reuters the pandemic must have been a "wake-up call" for businesses not closely monitoring their AI systems. This is because AI mistakes can cause huge problems for businesses that do not effectively manage the systems.
Aleksander Madry 是麻省理工学院可部署机器学习中心的主任。他告诉路透社，对于没有密切监控其人工智能系统的企业来说，这种流行病一定是一个“警钟”。这是因为人工智能错误可能会给无法有效管理系统的企业带来巨大的问题。
"That's what really stops us currently from this dream of AI revolutionizing everything," Madry said.
“这就是目前真正阻止我们实现人工智能彻底改变一切的梦想的原因，”Madry 说。
Urgency has been added to the issue since the European Union plans to pass a new AI law as soon as next year. The law requires companies to do some observation of their AI systems. Earlier this month, the U.S. administration also proposed new guidelines aimed at protecting citizens from the harmful effects of AI. In the guidelines, U.S. officials called for observers to ensure AI system "performance does not fall below an acceptable level over time."
自欧盟计划最早于明年通过一项新的人工智能法以来，这一问题变得更加紧迫。法律要求公司对其人工智能系统进行一些观察。本月早些时候，美国政府还提出了新的指导方针，旨在保护公民免受人工智能的有害影响。在指导方针中，美国官员呼吁观察员确保人工智能系统“随着时间的推移，性能不会低于可接受的水平”。
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