微软AI部门的首席产品负责人纳维里娜·辛格说，她在一个电子商务网站项目中遇到过一个完美的以同理心思维开发技术的例子。这个网站想让印度消费者更方便地购买他们的商品。由于印度的识字率较低，这家公司就为不识字的用户提供了语音转文字功能。他们事先统一行动，用印度各地的方言和文化对AI进行了训练，原因是在不同背景下，用户说话时表达的意图和内容都不一样。IBM沃森部门的客户关系总经理Inhi Cho Suh认为，意图识别是目前AI面临的最主要挑战和机遇之一。
辛格提出用F.A.T.E.这个缩略语来代表开发和监管此类技术时应注意的关键问题。它们是公平（fairness）、负责（accountability）、透明（transparency）和伦理（ethics）。反恐技术公司Moonshot CVE的创始人维迪亚·拉玛林汉姆说，虽然有很多关于AI技术的负面新闻，比如英国政治咨询机构Cambridge Analytica非法获取8700万Facebook用户数据的丑闻，但我们不能让恐惧主导舆论。
The AI revolution is upon us.
Machine learning, one of key artificial intelligence technologies, has already been deployed within more companies than you would expect. As it gains even greater adoption, regulation and empathy should be at the forefront.
Rana el Kaliouby, co-founder and CEO of emotional AI company Affectiva, said at Fortune’s Most Powerful Women Next Gen 2018 in Laguna Niguel, Calif. on last Wednesday that EQ is just as important in technology as IQ. Because of the frequency with which people interact with technology and its growing impact on our lives, it’s important that empathy be built into it, she said.
One way to do that, el Kaliouby said, is to have diverse teams work on the technology. In a example of the problem, she said that middle-aged white men usually create and train face recognition AI using images of people who look like themselves, which means the technology often doesn’t work as well, if at all, on women of color.
“It goes back to the teams designing these algorithms, and if your team isn’t diverse they aren’t going to be thinking about how this will work on a woman wearing hijab,” she said. “You solve for the problems you know.”
Navrina Singh, principal product lead of Microsoft AI, said that a perfect example of building technology with empathy in mind came to her during a project with an e-commerce site that trying to make it easier for customers in India to buy it products. Due to the low literacy rate in the country, the company built speech-to-text functionality for users who couldn’t read. Beforehand, the company made a concerted effort to train its AI in dialects and cultures from all around India, because the intent and meaning of speech varies based on background. Deciphering intent is one of the greatest challenges and opportunities in AI right now, Inhi Cho Suh, general manager of customer engagement at IBM Watson, said.
Regulation is another big topic in machine learning at the moment. With bots and other related technology becoming more sophisticated, laws are necessary to check that power, the panelists agreed. Suh said that technology and regulation should be used to prevent misuse, while el Kaliouby stressed the need for mandatory ethics training for college computer science and engineering majors.
Singh shared the acronym F.A.T.E., which stands for fairness, accountability, transparency and ethics, to sum up the key ideas to keep in mind when creating and regulating this technology. Although there is a lot of bad news about technology, like the Cambridge Analytica scandal, in which a British political firm accessed personal data on up to 87 million Facebook users, we must not let fear guide the debate, said Vidhya Ramalingham, founder of counter-terrorism technology company Moonshot CVE.
“Policy should not be written out of fear, it should be written in an educated and informed manner,” she said.