管理沟通 移情原则
by Arathi Mani
通过Arathi Mani
I sat in a wind tunnel for the first time while attending a summer camp as a middle schooler. Twelve-year-old me thought it was the coolest thing ever, sitting there as the wind whooshed across my face, feeling like I was nearly levitating in the same air used to test aircraft levitation. That experience single-handedly convinced me, at that time, that I was going to be an aerospace engineer when I grew up.
我作为一名中学生参加夏令营时第一次坐在风洞中。 12岁的我认为那是有史以来最酷的事情，坐在那里，风在我的脸上消散，感觉就像我几乎在用来测试飞机悬浮力的同一空中悬浮。 那段经验单据使我相信，我长大后将成为一名航空工程师。
Well, you know what they say about the best laid plans, especially those laid out by 12-year-olds…Five years later I chose to pursue computer science. My plan on becoming an aerospace engineer was derailed by my great enjoyment of taking my first computer science class in high school. Then, trying to decide between the two areas, I figured I wouldn’t be shut out of the world of aerospace if I pursued my love of computers, but the reverse might not be true. It was a solid (and rather astute) bet for a 17-year-old to make, even though I still have not yet sat in another wind tunnel (the dream lives on!). I have been extremely fortunate to be able to apply my computer science knowledge to a vast array of applications — from mental health, to natural language, and now, in my current role (Senior Software Engineer at the Chan Zuckerberg Initiative) to single-cell biology.
好吧，您知道他们对最佳计划的看法，尤其是对12岁以下儿童制定的计划。五年后，我选择了计算机科学。 我上高中的第一门计算机科学课的乐趣使我成为航空工程师的计划陷于混乱。 然后，尝试在这两个区域之间做出决定，我认为，如果我追求计算机的热爱，我将不会被排除在航空航天领域之外，但事实并非如此。 即使我还没有坐在另一个风洞中(梦想依然存在！)，对于一个17岁的年轻人来说，这是一个可靠的选择(而且相当明智)。 我非常幸运能够将我的计算机科学知识应用到从心理健康到自然语言的众多应用程序中，现在，我以目前的职务(Chan Zuckerberg Initiative的高级软件工程师)应用于单细胞生物学。
不断增长的工作差距 (The Job Gap That Keeps Growing)
My senior year in high school marked an interesting inflection point for computer science in the U.S. — it was 2010 and the start of a sharp growth in the number of students who graduated with a degree in Computer Science. In parallel, an external narrative seemed to be mirroring this growth: there are more jobs available in CS than there are qualified workers.
我高中的高三是美国计算机科学的一个有趣转折点-那是2010年，计算机科学学位的学生人数急剧增加的开始。 同时，外部叙事似乎反映了这种增长： CS中提供的工作多于合格的工人 。
This line was inculcated in me not only by my school counselors, but also generally by the news. For me, it was probably some combination of this external narrative as well as that one computer science class I had the privilege of taking that spurred my interest and, in a happy collision, deviated my path away from aerospace engineering. Interestingly, a decade later, the external narrative to convince more folks to pursue computer science jobs has continued to remain roughly the same. The gap has been projected to be quite dramatic in the upcoming decade, with the U.S. Bureau of Labor Statistics projecting a 21% increase in demand for Software Engineers between 2018 and 2028 (given 1,365,500 jobs in 2018) and only 284,100 people available with the skills necessary to fulfill these jobs.
我的学校顾问不仅向我灌输了这句话，而且新闻也向我灌输了这句话。 对我而言，这可能是这种外部叙述的结合，以及我荣幸地参加的一门计算机科学课激发了我的兴趣，并在一次快乐的碰撞中偏离了我从航空工程学的道路。 有趣的是，十年后，说服更多人从事计算机科学工作的外部叙述仍然大致保持不变。 预计在未来十年中，这种差距将非常显着， 美国劳工统计局预计，在2018年至2028年之间，对软件工程师的需求将增长21％(2018年提供了1,365,500个工作岗位)，只有284,100人拥有该技能完成这些工作所必需的。
These statistics as well as the allure of cushy, well-paying jobs at companies like Google, Amazon, and Facebook continue to fuel an exponential growth in the conferral of computer science degrees. For example, of the undergraduate degrees conferred at Stanford during the 2018–2019 academic year, a full 17% were degrees in computer science. The pattern continues even beyond Silicon Valley with the number of conferred degrees in computer science-related fields growing steadily upwards from around 43,000 in 2010 to over 71,000 in 2017.
这些统计数据以及诸如Google，Amazon和Facebook这样的公司提供的高薪工作的诱人魅力，继续推动着计算机科学学位的授予呈指数级增长。 例如，在2018-2019学年期间，在斯坦福大学授予的本科学位中，有17％是计算机科学学位。 这种模式甚至持续到硅谷以外，与计算机科学相关领域的授予学位的数量从2010年的约43,000个稳定增长到2017年的71,000个以上。
While this growth is to be celebrated, I see two big problems. One is that the gap between jobs and jobseekers still isn’t closing and instead growing larger as time passes. This is unsurprising as more of our lives become entrenched in technology, generating even more technology-based jobs.
尽管这种增长值得庆祝，但我看到了两个大问题。 一个是，工作与求职者之间的差距仍然没有消除，而是随着时间的流逝而扩大。 这不足为奇，因为我们越来越多的人根深蒂固地使用技术，创造了更多基于技术的工作。
The second is a growing subplot around diversity in more than the demographic-sense, threatening the technological advances that we so eagerly want in our world:
二是围绕多样性越来越多的次要情节比人口义多 ，威胁着技术的进步，我们那么热情希望在我们的世界：
Computer scientists desperately need people with deep domain knowledge about all the other fields of study.
迫切需要计算机科学家的人对其他所有研究领域都拥有深入的知识。
And I really do mean all. We need people who have studied African American Studies and Computer Science, we need folks who have pursued Social Work and Computer Science. We need people who are experts in Agriculture, in Politics, in Law, in Microbiology, in Philosophy and Computer Science. We are badly lacking diversity of knowledge in technology, I believe, partially perpetuated by the external narrative pushing CS studies and not multidisciplinary studies.
而且我确实是全部 。 我们需要研究过非裔美国人研究和计算机科学的人，我们需要从事社会工作和计算机科学的人。 我们需要在农业，政治，法律，微生物学，哲学和计算机科学领域的专家。 我认为，我们非常缺乏技术知识的多样性，部分原因是外部叙事推动了CS研究而不是多学科研究。
Diversity of knowledge, stemming from diversity of study, background and perspective, is not only beneficial but paramount to the success of our communities and our world.
源于研究，背景和观点的多样性所带来的知识多样性不仅对我们的社区和世界的成功有益，而且至关重要。
代表性不足导致不平等 (Lack of Representation Leads to Inequity)
This past April, I attended the International Conference for Learning Representation, which was the first major machine learning conference ever to be held in Africa. It was ultimately held virtually rather than in Ethiopia due to the coronavirus pandemic. One of the most impactful and powerful keynotes I heard during the conference was delivered by Professor Ruha Benjamin, who described the way in which many current technological advances — data science in particular — hide modern-day social injustices by failing to address historical biases. Oftentimes, these technological advances misguidedly celebrate automation as progress while creating a society that unconsciously reinforces past prejudices by ignoring lack of diversity in the data itself, in our methods, and more broadly, in ideation.
今年四月，我参加了国际学习代表大会 ，这是有史以来在非洲举行的第一场大型机器学习大会。 由于冠状病毒大流行，它最终被虚拟保存，而不是在埃塞俄比亚。 我在会议上听到的最有影响力和最有力的主题演讲之一是Ruha Benjamin教授发表的演讲 ，他描述了当前许多技术进步(尤其是数据科学)如何通过未能解决历史偏见来掩盖现代社会不公正现象的方式。 通常，这些技术进步误导了自动化作为进步，同时又创造了一个无意识地通过忽略数据本身，我们的方法以及更广泛的思想上缺乏多样性的社会，从而无情地增强了过去的偏见。
One life-altering example is a recent effort to predict recidivism, the likelihood that a prisoner might reoffend. Without accounting for the fact that historical human-generated data reflects many of the human rights atrocities of our generations’ pasts, we end up amplifying the biases into the future in ways that clearly discriminate against underrepresented groups, especially Black people. Another example is lack of representation in state-of-the-art machine learning research; 30% of all living languages today are languages spoken in Africa, but the majority of machine translation research has traditionally focused on translations of Indo-European languages (i.e. French, German, Spanish, etc.). Eventually, successful research will find its way into products, and so lack of representation early in the pipeline bodes of unequal opportunities later down the line. In fact, — not one of the languages available for Siri real-time translation is a language spoken in Africa.
改变生活的一个例子是最近的一项努力来预测再犯 ，即犯人可能再次犯罪的可能性。 没有考虑到人类产生的历史数据反映了我们这一代人过去的许多人权暴行这一事实，我们最终以明显歧视代表性不足的群体(尤其是黑人)的方式扩大了对未来的偏见。 另一个例子是最新的机器学习研究缺乏代表性。 当今，所有生活语言中有30％是在非洲使用的语言，但是传统上，大多数机器翻译研究都集中在印欧语系(例如，法语，德语，西班牙语等)的翻译上。 最终，成功的研究将进入到产品中，因此初期缺乏代表性的机会在后期一直存在机会不平等的现象。 实际上，在非洲使用的不是一种Siri实时翻译可用的语言。
Dr. Benjamin said during her talk “computational depth without historical and sociological depth is superficial.”
本杰明博士在演讲中说：“没有历史和社会学深度的计算深度是肤浅的。”
We can all do better to understand the ways in which we perpetuate unconscious bias but we also need to take action to address this superficiality.
我们所有人都可以做得更好，以了解我们保持无意识偏见的方式，但我们也需要采取行动来解决这种肤浅的问题。
One way we can do this is by championing the recruitment of a workforce that is not only demographically diverse, but also multidisciplinary.
我们做到这一点的一种方法是拥护不仅在人口统计学上多样化，而且也是跨学科的劳动力。
多学科方法 (A Multidisciplinary Approach)
To ensure we are creating a world that deeply values the health of our communities, we need a set of tenets that emphasize learning from our past and curiosity into the nuances of the problems we are trying to solve. Both of these principles rely on diversity of knowledge, which point to a strong need for diversity in cultural background, in race, in domain knowledge, in gender, and in numerous other ways. The conjecture becomes nearly a tautology: if you have diversity in people, then you will likely also have diversity in thought.
为了确保我们创建一个深深珍视社区健康的世界，我们需要一系列原则，强调从过去的经验中学习，并好奇地了解我们要解决的问题的细微差别。 这两个原则都依赖于知识的多样性，这表明强烈需要在文化背景，种族，领域知识，性别，以及许多其他方面具有多样性。 这个猜想几乎变成了重言式：如果您在人中有多种多样的话，那么您可能也会在思想上也多种多样。
It falls out naturally then that pursuing a multidisciplinary background is invaluable, and the cultivation of cross disciplinary teams across all work sectors is vital.
因此，自然而然的是，追求跨学科的背景非常宝贵，在所有工作领域培养跨学科团队至关重要。
But how do we encourage this? One possible solution is to shift the way we teach computer science in grade school; instead of presenting CS as a class to take in high school to vet the major for collegiate study, we can start earlier and begin teaching basic concepts in elementary and middle school through programs like Scratch and Alice.
但是我们如何鼓励呢？ 一种可能的解决方案是改变我们在小学阶段教授计算机科学的方式。 与其将CS作为上高中阶段的课程来审查大学学习的专业，我们可以更早地开始并通过Scratch和Alice等程序在中小学教授基本概念。
Another important shift that I think needs further emphasis is the inclusion of CS in general education courses. Instead of presenting CS as one of the few majors with extremely high job prospects, we should encourage students to pursue any major, knowing that a CS class or two will be included. By doing this, we ensure that students are better prepared to address the technological shifts in any industry. Who knows, perhaps if this had been the case back in 2010, I would be working with wind tunnels today! The good news is that I think there are signs that both shifts are already beginning to occur; in fact, regarding the second shift, I believe think we are at the cusp of a new wave of multidisciplinary tertiary studies, called informatics.
我认为需要进一步强调的另一个重要转变是将CS纳入通识教育课程。 我们不应该将CS视为具有极高工作前景的少数专业之一，而应该鼓励学生学习任何专业，因为他们知道将包括一两个CS班。 通过这样做，我们确保学生为应对任何行业的技术变化做好了更充分的准备。 谁知道，也许如果是2010年的情况，我今天将在风洞中工作！ 好消息是，我认为有迹象表明这两种转变已经开始发生。 实际上，关于第二个转变，我认为我们正处于一门称为信息学的新的跨学科高等教育研究的风口浪尖。
定义信息学 (Defining Informatics)
Looking back in history on the rise and fall of various studies, it was only in 1958 that the first definition of aerospace engineering as a discipline appeared; the term ‘engineering’ emphasized the application of mathematics in a particular field, in this case aerospace.
回顾各种研究的兴衰史，直到1958年，航空航天工程学科才首次出现。 术语“工程”强调数学的一个特定领域的应用 ，在这种情况下，航空业。
In many ways, I see the future of computer science study mimicking the way mathematics is taught today; as a general purpose tool that serves nearly all disciplines in an applied sense.
在许多方面，我看到了计算机科学学习的未来，模仿了当今的数学教学方式。 作为一种通用工具，可在应用意义上为几乎所有学科提供服务。
Today, computer science has a strong theoretical component to it because of its siloed study. While theoretical study should always be strongly supported, applied study is also very valuable and helps unlock additional knowledge in the field in which it is applied.
如今，计算机科学由于其孤立的研究而具有强大的理论成分。 尽管理论研究应始终得到大力支持，但应用研究也非常有价值，有助于在应用领域中获得更多知识。
‘Applied CS’ generally takes on a different name in the list of pursuable studies: ‘Informatics.’ We are already seeing a trend towards this with the emergence of various Informatics fields — Bioinformatics, Business Informatics, and even Music Informatics. It makes sense: given that the term ‘Informatics’ has been loosely defined as “the science of processing data for storage and retrieval,” and given that data is now being produced everywhere, we need more multidisciplinary tools, that are also domain-driven, to process it.
“应用的CS”通常在可研究的列表中使用不同的名称：“信息学”。 随着各种信息学领域(生物信息学，商业信息学甚至音乐信息学)的出现，我们已经看到了朝着这一方向发展的趋势。 这是有道理的：鉴于“信息学”一词已被粗略地定义为“处理用于存储和检索数据的科学”，并且鉴于现在到处都有数据产生，我们需要更多的多学科工具，这些工具也是领域驱动的进行处理。
难题的最后一块：跨学科团队 (The Final Piece of the Puzzle: Cross Disciplinary Teams)
Beyond educational reform, companies need to start forming cross disciplinary teams with a strong focus on hiring a diverse workforce. Here as well, we are already beginning to see positive change in this direction. For example, the Stanford Institute for Human-Centered Artificial Intelligence (HAI) aims to build a multidisciplinary community involving AI researchers and domain experts to develop solutions that are human-centered at its core. Other Diversity, Equity, and Inclusion (DEI) efforts throughout the world, like SACNAS, make huge strides towards this goal by focusing broadly on diversity of thought. Make no mistake, we aren’t done yet and the road ahead is still long; there are going to be times where we as a society feel like we aren’t making progress. even though we’re investing time into having tough conversations or hammering down what seems like an indestructible wall. I can personally attest to having felt these feelings in my own role, working at the intersection of single-cell biology and technology.
除教育改革外，公司还需要开始组建跨学科团队，并着重于雇用多样化的劳动力。 同样在这里，我们已经开始看到朝着这个方向的积极变化。 例如， 斯坦福大学以人为本的人工智能研究所 (HAI)旨在建立一个由AI研究人员和领域专家组成的多学科社区，以开发以人为本的核心解决方案。 像SACNAS这样的全球其他多样性，公平和包容性(DEI)努力，通过广泛关注思想的多样性，朝着这一目标迈出了巨大的步伐。 没错，我们还没有完成，未来的路还很长。 在某些时候，我们这个社会会觉得自己没有进步。 即使我们正在花时间进行艰难的对话或推翻似乎坚不可摧的隔离墙。 我可以亲自证明自己在单细胞生物学和技术的交汇处感受到了这些感觉。
While my own background is in tech, tech, and some more tech, my work requires me to collaborate with scientists to ensure we build technology solutions that address the core concerns that scientists are facing and not make up a solution based on what I see at surface-level. I work on helping drive the creation of a comprehensive cell atlas; the vision is an amalgamation of many single-cell datasets generated by many different labs across the world, spanning donors of all ethnicities, genders, ages, and other demographics, integrated in such a way that we have a more holistic understanding of the human body that can accelerate cures for many diseases. Without the incredible support of the team of computational biologists that I am very lucky to work with, I would not be able to understand the nuances of the problems the tech needs to address. They help me answer questions like how does data need to be integrated such that biological variations are retained? What sort of metadata do I need to store such that meaningful downstream analyses can be performed on the corpus?
虽然我自己的背景是技术，技术以及更多技术，但我的工作要求我与科学家合作，以确保我们建立能够解决科学家所面临的核心问题的技术解决方案，而不是根据我的见解构成解决方案表面水平。 我致力于推动创建全面的单元图集； 该愿景是由世界各地许多不同实验室生成的许多单细胞数据集的合并，涵盖了各个种族，性别，年龄和其他人口统计的捐赠者，并且通过这种方式进行整合，使我们对人体有了更全面的了解可以加快治愈许多疾病的速度。 如果没有非常幸运的合作伙伴，计算生物学家团队的大力支持，我将无法理解技术需要解决的问题的细微差别。 它们帮助我回答诸如如何整合数据以保留生物学差异之类的问题？ 我需要存储哪种元数据，以便可以对语料库进行有意义的下游分析？
There’s something really beautiful about being able to have heated conversations about product decisions but come out the other side with not only a better understanding of the other person, but with a little better understanding about the world we live in.
能够就产品决策进行激烈的对话，但不仅可以更好地了解对方，而且可以更好地了解我们所生活的世界，这确实是一件很美的事情。
The very act of trying to adopt a mindset that genuinely attempts to understand others’ perspectives is a step in the right direction and will bring us closer to creating a more equitable society. Coming to compromises and building technology that is for biologists and accelerates their work is truly such a rewarding experience and over time, the conversations that once were quite hard, become easier.
试图采取一种真正尝试理解他人观点的思维方式，这是朝正确方向迈出的一步，它将使我们更接近于建立一个更加公平的社会。 采取妥协和构建适合生物学家的技术并加速他们的工作确实是一种有益的经验，随着时间的流逝，曾经很艰难的对话变得更加容易。
All of these efforts are absolutely worth it. We risk so much by choosing to separate computer science and “everything else.”
所有这些努力都是绝对值得的。 通过选择将计算机科学与“其他所有东西”分开，我们承担着巨大的风险。
We risk building a split society of the haves and have-nots and not solving the real crises of today. The Governor of California, Gavin Newsom, succinctly summarized this growing dichotomy in his keynote address at the opening of HAI: “There is an empathy gap… in technology.” I am very hopeful that this empathy gap can be solved, but we need to invest, promote, and encourage diversity in technology, more fervently than ever. We can take one small step towards this goal by at least addressing in our educational pursuits that the choice isn’t Computer Science or. It’s Computer Science and.
我们冒着建立一个由富人与富人组成的分裂社会的风险，而不能解决当今的真正危机。 加利福尼亚州州长加文·纽瑟姆(Gavin Newsom)在HAI开幕式上的主题演讲中简要总结了这种日益加剧的二分法：“技术上存在同理心差距。” 我非常希望可以解决这种移情鸿沟，但是我们需要比以往任何时候更加积极地投资，促进和鼓励技术多样性。 我们可以通过至少解决我们的选择不是计算机科学或计算机专业的教育追求迈出一小步。 它是计算机科学和 。
About the Author
关于作者
Arathi Mani is a Senior Software Engineer at the Chan Zuckerberg Initiative working on the Single Cell technology team within CZI Science. She is currently leading an effort to build a platform that enables the publication, discovery and exploration of interoperable single-cell datasets with the goal of eventually creating a map of all the cells in the human body. Prior to CZI, she worked as a Software Engineer at Google and was a Lecturer at Berkeley. She has a Master’s degree in Computer Science from Stanford University and a Bachelor’s in Computer Science Engineering from The Ohio State University. In her free time, she loves to hike, ski, and bake far too many cookies.
Arathi Mani是Chan Zuckerberg Initiative的高级软件工程师，在CZI Science的单细胞技术团队中工作。 她目前正在领导建立一个平台的工作，该平台可以发布，发现和探索可互操作的单细胞数据集，以最终创建人体所有细胞的图谱为目标。 在加入CZI之前，她曾在Google担任软件工程师，并曾在伯克利大学任讲师。 她拥有斯坦福大学的计算机科学硕士学位和俄亥俄州立大学的计算机科学工程学士学位。 在业余时间，她喜欢远足，滑雪和烘烤太多饼干。
Twitter: @ArathiMani • Instagram: @arathimani • LinkedIn: Arathi Mani • Medium: Arathi Mani
Twitter的：@ArathiMani•Instagram：@arathimani•LinkedIn：阿拉希玛尼•中： 阿拉希玛尼
翻译自: https://medium.com/stem-and-culture-chronicle/solving-the-empathy-gap-in-technology-8eb6be7fdf79
管理沟通 移情原则