Federal taxes are due this week in the US. But what about our synthetic coworkers? Should they cough up, too? Jordan Harrod, a Harvard-MIT PhD student, has something to say on the subject.

联邦税将于本周在美国到期。 但是我们的合成同事呢？ 他们也应该咳嗽吗？ 哈佛MIT博士生约旦·哈罗德(Jordan Harrod)在这个问题上有话要说。

By S.C. Stuart

由 SC Stuart

COVID-19 means the US delayed this year’s federal tax deadline to July 15; if you’ve procrastinated, it’s time to get to it. But while tax collectors will only accept payment from humans in 2020, will we soon be sending tax bills to robots, too?

COVID-19表示美国将今年的联邦税收截止日期延迟到7月15日； 如果您拖延了， 该该值了 。 但是，尽管收税员只会在2020年接受人类的付款，但我们是否也会很快将税单发送给机器人？

That’s the question posed by Jordan Harrod, a medical engineering and neurobiology PhD student at Harvard by day and YouTube creator by night. On her channel, she digs into geeky topics like whether it’s possible to make artificial intelligence speak and the aforementioned android taxation.

这就是白天在哈佛大学医学工程和神经生物学博士学位的约旦·哈罗德 ( Jordan Harrod)以及晚上在YouTube上创作的乔丹·哈罗德 ( Jordan Harrod)提出的问题。 在她的频道上，她深入研究了令人讨厌的话题，例如是否有可能让人工智能说话以及上述机器人征税。

As she explains, US companies are taxed based on how many employees they have. More machines and fewer people mean less money paid in taxes. So the argument is that companies that lay off human workers and move to automation should not necessarily get a big tax break, and the taxes they do pay should go to retrain or support people who are now out of a job.

正如她所解释的，美国公司是根据拥有多少员工来征税的。 更多的机器和更少的人意味着更少的税金。 因此，有理由认为，解雇人力工人并转向自动化的公司不一定必须获得大额税收减免，而他们所缴纳的税金应该用于再培训或支持现在失业的人。

“I’m certainly not an economist or tax professional, so I spent a lot of time digging into research and current policy proposals on this topic to make that video,” Harrod tells PCMag. “Personally, I like the idea of an automation tax, where the money is redirected to retraining programs that help people re-integrate into the modern workforce and unemployment payments to support them through their training.”

“我当然不是经济学家或税务专业人士，所以我花了很多时间来研究有关该主题的研究和当前的政策建议，以制作该视频，” Harrod告诉PCMag。 “个人而言，我喜欢自动化税的想法，其中的钱被用于培训计划，以帮助人们重新融入现代劳动力和失业金，以通过培训来支持他们。”

She acknowledges reports that say automation won’t have as dire an effect on the US employment rate as some predict, thanks in part to the emergence of new industries and technology. And even if a robot tax is put in place, it “wouldn’t necessarily solve the income inequality issue without other policies implemented in parallel to make sure that money gets to the people who need it,” she says. “You can take money from the rich, but if you don’t give it to the poor, it doesn’t really solve that problem.”

她承认有报道称，自动化不会像某些人所预测的那样严重影响美国的就业率，这在一定程度上要归功于新兴产业和技术的出现。 她说，即使实行了机器人税收，“如果没有并行实施其他政策来确保钱能送到需要的人手中，它也不一定能解决收入不平等问题。” “你可以从富人那里得到钱，但是如果不把钱交给穷人，那将无法真正解决这个问题。”

Still, it’s an “interesting question” to ponder, she says. How did Harrod get thinking about these and other complex issues? We spoke to her recently about her neurobiology studies, distinguishing AI hype from reality, algorithm bias, and why she started a YouTube channel.

她说，这仍然是一个“有趣的问题”。 哈罗德如何思考这些以及其他复杂的问题？ 最近，我们向她介绍了她的神经生物学研究，从而将AI炒作与现实，算法偏见以及她为什么开设YouTube频道区分开来。

PCMag: You’re scheduled to complete a joint PhD from Harvard Medical School and MIT in 2023. What brought you to this field? Jordan Harrod: I came to neuroscience somewhat by accident. I’ve done research in a few different fields since starting college, and used each past experience as a way of narrowing down what I wanted to do next. By the time I started my PhD, I’d narrowed my research interests down to something that would let me use machine learning and build devices for medicine, which is obviously still pretty broad.

PCMag：您计划在2023年获得哈佛医学院和麻省理工学院的联合博士学位。是什么使您进入这一领域的？ Jordan Harrod：我有些偶然地进入了神经科学领域。 自从开始上大学以来，我已经在几个不同的领域进行了研究，并将过去的每一次经验用作缩小我接下来要做的事情的一种方式。 当我开始攻读博士学位时，我将研究兴趣缩小到可以让我使用机器学习和制造医学设备的领域，这显然仍然很广泛。

And, interestingly, you’re working with not one but two labs. At the end of the day, I settled on a project that I both found interesting and that was in a lab run by faculty who I felt matched my mentorship preferences, which actually happened to be two labs: the Neuroscience Statistics Research Lab and the Synthetic Neurobiology group.

而且，有趣的是，您不是在与两个实验室一起工作。 归根结底，我完成了一个我都觉得很有趣的项目，该项目是由我认为与我的指导偏好相匹配的教职员工经营的，而该实验室实际上恰好是两个实验室：神经科学统计研究实验室和合成实验室神经生物学小组。

Tell us the story behind your YouTube channel. I was about to start my PhD, and knew I wanted machine learning to be a significant part of my research. However, given that my program focuses on translational and clinical research, I wanted to learn more about how people interact with algorithms, and I wasn’t able to find a lot of resources that were geared towards the average person. A lot of people supplement their education in one way or another by watching Crash Course or Khan Academy videos, so YouTube seemed like the place to be. And I’ve been involved in science communication since high school as both student, and teacher. So I put my plan to do science communication on YouTube together with my interest in AI, and the channel was born.

告诉我们您 YouTube频道 背后的故事 。 我正准备开始攻读博士学位，并且知道我希望机器学习成为我研究的重要组成部分。 但是，鉴于我的程序专注于转化和临床研究，所以我想了解更多有关人们如何与算法交互的信息，而我却找不到很多针对普通人的资源。 许多人通过观看速成班或可汗学院的视频以一种或另一种方式补充他们的教育，因此YouTube似乎是一个好地方。 从高中开始，我就一直作为学生和老师参与科学交流。 因此，我将自己的计划与对AI的兴趣一起在YouTube上进行了科学交流，因此该频道诞生了。

How do you come up with your sometimes strange and philosophical subject ideas? They’re often random ideas that pop into my head when I walk down the street and see a piece of technology that I hadn’t really considered before. My goal is to give people the tools and information they need to interact with the algorithms that often govern significant aspects of our lives, even if we don’t always know it.

您如何提出有时是奇怪的哲学主题思想？ 当我走在街上，看到以前从未真正考虑过的技术时，它们常常是随机的想法突然浮现在我的脑海。 我的目标是为人们提供与经常支配我们生活中重要方面的算法进行交互所需的工具和信息，即使我们并不总是知道这些。

In your TedX talk you say that few humans have ‘AI literacy’ and, as such, are at a disadvantage when interacting with expert systems. It’s true. Many people can’t distinguish AI hype from reality, and I don’t think you need to be an expert researcher in order to do so. You also may not realize that you are interacting with an AI system in the first place, which can become problematic when these systems are making life-or-death decisions. More broadly, a lack of AI literacy can lead to policies and regulations that don’t effectively govern new technologies, and individuals engaging with biased systems without understanding the risks associated with that.

在 您的TedX谈话中， 您说很少有人具有'AI素养'，因此与专家系统进行交互时处于不利地位。 这是真的。 许多人无法将AI的炒作与现实区分开，而且我认为您不需要成为专家研究人员就可以做到。 您可能还没有意识到自己最初是与AI系统进行交互的，当这些系统做出生死攸关的决定时，这可能会成为问题。 更广泛地讲，缺乏人工智能素养会导致无法有效管理新技术的政策和法规，以及个人在不了解与之相关的风险的情况下使用有偏见的系统。

You also point out that AI isn’t just handling the big stuff. It also affects your day-to-day experiences in small ways online. You may be missing content from friends and family members because an algorithm is prioritizing content that you react to instead, which, in turn, causes stress and negatively impacts your mental health.

您还指出，人工智能不只是处理大问题。 它也会以细微的在线方式影响您的日常体验。 您可能会缺少朋友和家人的内容，因为算法会优先处理您要对之做出React的内容，这反过来会造成压力并给您的心理健康带来负面影响。

What do you want people to do after watching your videos?I think it depends on the video. Most of my videos are purely for education and awareness, so that people can walk away from it having learned something new that might help them the next time they encounter a similar system. On the other hand, I hope that people watching my AI 101 series might continue to improve their programming skills and look for additional resources to do so if they found the tutorial interesting.

您希望人们观看您的视频后做什么？ 我认为这取决于视频。 我的大部分视频纯粹是为了教育和提高认识，因此人们可以从中学到的新知识中摆脱出来，这可能会在下次遇到类似系统时为他们提供帮助。 另一方面，我希望观看我的AI 101系列的人们可以继续提高他们的编程技能，并在发现有趣的教程的同时寻找其他资源来提高他们的编程技能。

On Juneteenth, you did a video explaining how AI preserves systemic racism through social systems like education, healthcare and the law. How can we make AI explicitly anti-racist to rectify this? That’s the goal, isn’t it? Unfortunately, one of the many things that I’ve realized in making videos on AI fairness and bias research is that there’s no one way to ‘fix’ it, because the fix will always be predicated on your definition of fairness, which comes with your own personal biases.

在6月14日，您制作了一个视频， 解释 了AI如何通过教育，医疗保健和法律等社会系统维护系统种族主义。 我们如何才能使AI明确反对种族主义以纠正这一问题？ 这是目标，不是吗？ 不幸的是，我在制作有关AI公平性和偏见研究的视频时意识到的许多事情之一是，没有一种“修复”方法，因为修复总是基于您对公平性的定义，自己的个人偏见。

Which is why diversity in AI is so important, to have checks and balances on so-called ‘hidden biases.’ Right. Balancing datasets and regularizing distributions during training are also steps you can take, but what a lot of the research has shown me is that community engagement is both extremely important, and often skipped, while developing AI systems. By involving the communities that will be affected by these technologies in every stage of the design and development process, you’re more likely to tailor your solution to the needs of the community and consider on- or off-target effects that you may not have been aware of otherwise.

这就是为什么人工智能的多样性如此重要，以制衡所谓的“隐性偏见”如此重要。 对。 在培训期间平衡数据集和调整分布也是您可以采取的步骤，但是很多研究表明，在开发AI系统时，社区参与既非常重要，而且经常被跳过。 通过将受这些技术影响的社区纳入设计和开发过程的每个阶段，您更有可能根据社区的需求量身定制解决方案，并考虑可能没有的目标或目标外影响知道否则。

Good point. What are your plans for post-doctoral life?In terms of post-doctoral life, I haven’t settled on a path quite yet. Luckily, I just finished my second year of my PhD, so I have a good amount of time before I have to decide. I’d like to continue working on high-risk/high-reward projects, likely in industry, so places like Google X are interesting to me. But, I also wouldn’t be surprised if my dream job hasn’t been created yet. Some of these technologies move so quickly that there might be a whole new field for me to consider by the time I finish my PhD.

好点子。 您的博士后生活计划是什么？ 就博士后生活而言，我还没有定下一条路。 幸运的是，我刚完成博士学位的第二年，所以我有很多时间可以决定。 我想继续从事高风险/高回报的项目(可能在行业中)，因此Google X这样的地方对我来说很有趣。 但是，如果我的梦想工作还没有创建，我也不会感到惊讶。 其中一些技术发展如此之快，以至于我完成博士学位时可能需要一个全新的领域来考虑。

Finally, you hint that sometimes you use AI to curate YouTube videos for you. Tell us more. Well, if I told you that secret, everyone would be able to do it!

最后，您暗示有时您会使用AI为您整理YouTube视频。 请告诉我们更多。 好吧，如果我告诉你这个秘密，每个人都可以做到！

Originally published at https://www.pcmag.com.

最初发布在 https://www.pcmag.com 。