缩小女孩的想象力差距 (Closing the Imagination Gap for Girls)

The term artificial intelligence (AI) was coined 64 years ago at a scholarly conference. The AI field hasn’t remained the theoretical province of computer scientists and mathematicians; it now is a pervasive part of everyday life. With a technology this powerful, it is critical to include the perspectives of all women, including those from underrepresented communities.

人工智能(AI)一词是64年前的一次学术会议上提出的。 人工智能领域还没有一直是计算机科学家和数学家的理论领域。 现在，它已成为日常生活的一部分。 借助如此强大的技术，至关重要的是要包括所有妇女的观点，包括来自代表性不足社区的妇女的观点。

AI applications — based on algorithms — are found in robotics, machine learning, natural language processing, machine vision, speech recognition and more. These applications are found in homes, vehicles and myriad other aspects of daily life. Examples include facial recognition; robots helping older people live more independently at home; autonomous vehicles; smart watches; and drone safety systems.

基于算法的AI应用程序存在于机器人技术，机器学习，自然语言处理，机器视觉，语音识别等领域。 这些应用存在于房屋，车辆和日常生活的其他方面。 例子包括面部识别； 帮助老年人在家中独立生活的机器人； 自动驾驶汽车； 智能手表 和无人机安全系统。

AI applications must be able to reach conclusions and offer information. Some require the capacity to sense emotions in order to relate to people.

AI应用程序必须能够得出结论并提供信息。 有些人需要具有感知情感的能力才能与人交往。

Today, women are making their way into AI and leading the way for more girls to enter AI careers. They’re helping this burgeoning industry progress and innovate in ways that otherwise might not be possible. In essence, adding women to the teams creating components of AI fundamentally changes the suitability and functionality of a product or service by eliminating biases and better reflecting the needs of a wider group of users.

如今，女性正在进入AI领域，并引导更多女孩进入AI职业。 他们正在帮助新兴的行业进步并以其他方式无法实现的方式进行创新。 本质上，通过消除偏见并更好地反映更广泛的用户需求，将女性加入创建AI组件的团队可以从根本上改变产品或服务的适用性和功能。

Taniya Mishra is director of Artificial Intelligence Research and lead speech scientist at Affectiva, which originated at MIT. The company’s technology calibrates people’s speech patterns to recognize emotions.

Taniya Mishra是MIT的人工智能研究总监和Affectiva的首席语音科学家。 该公司的技术可以校准人们的语音模式以识别情绪。

Mishra offers some concrete examples of machine learning algorithms.

Mishra提供了一些机器学习算法的具体示例。

“Algorithms are a set of rules — logic — or a set of instructions that you can give to a machine in order to get it to accomplish a goal — to make it behave like a human being,” Mishra says. “It could be any goal. It could be lifting a block from one place to another. It could be understanding human emotion. All of these could be the goals for designing a machine learning algorithm.”

“算法是一组规则-逻辑-或您可以赋予机器以使其达到目标的一组指令-使机器表现得像人一样，”米什拉说。 “这可能是任何目标。 可能是将一个方块从一个地方搬到另一个地方。 可能是了解人类的情感。 所有这些可能是设计机器学习算法的目标。”

“The basic algorithm recipe tells the computer when to do ‘x,’ then when to do ‘y’ and then ‘z.’ For this process to work right, the programmer must give the right instructions. For it to be inclusive, the programmer must think of the entire humanity of users,” Mishra notes.

“基本算法配方告诉计算机何时执行'x'，然后何时执行'y'，然后执行'z'。 为了使此过程正常进行，程序员必须给出正确的说明。 为了使其具有包容性，程序员必须考虑用户的整个人性。” Mishra指出。

实地不平等 (Inequities in the Field)

When it comes to diversity, AI benefits from including women and other underrepresented people. These voices must be included when writing instructions or algorithms to power machine learning or other elements of AI. The data gathered to support AI must also come from diverse groups of people, if the resulting algorithm is going to fully meet its potential.

在多样性方面，人工智能可以从包括妇女和其他代表性不足的人中受益。 在编写指令或算法以增强机器学习或AI的其他元素时，必须包含这些声音。 如果最终的算法要完全满足其潜力，则为支持AI而收集的数据也必须来自不同的人群。

For example, a small homogenous group designing a facial recognition program for a large heterogeneous group will miss the target if data about a variety of faces from the larger group is not represented. In other words, the algorithm is only as bias-free as the sources of data and the data sets.

例如，如果未表示有关来自较大组的各种面部的数据，则为大型异构组设计面部识别程序的小型同质组将错过目标。 换句话说，该算法仅与数据源和数据集一样无偏差。

To be effective, creators of AI-related applications need to be as diverse as the people using them.

为了发挥作用，与AI相关的应用程序的创建者必须与使用它们的人一样多样化。

Eighteen-year-old Betelhem Dessie is founder and chief executive officer of iCog-Anyone Can Code in Ethiopia. She also co-founded Solve IT, which provides technical resources to develop local solutions for community problems.

十八岁的Betelhem Dessie是埃塞俄比亚iCog-Anyone Can Code的创始人兼首席执行官。 她还与他人共同创立了Solve IT，该公司提供技术资源来开发针对社区问题的本地解决方案。

“As different AI tools were being developed, I observed a lack of contributions from people of color and women,” Dessie notes. “The solution, I thought, was having early childhood tech education — but also inspiring girls who are already in the workforce to pursue these types of career paths. The most rewarding part of my work is inspiring others — particularly women and girls — to pursue careers in technology.”

Dessie指出：“随着正在开发不同的AI工具，我发现有色人种和女性缺乏贡献。” “我认为解决方案是接受早期儿童技术教育，同时也激励已经在职场中寻求这种职业道路的女孩。 我工作中最有意义的部分是激励其他人，尤其是妇女和女孩，从事技术职业。”

But gender and diversity issues remain.

但是性别和多样性问题仍然存在。

A 2019 article written by Kari Paul for The Guardian states “the lack of diversity in the AI field has reached ‘a moment of reckoning,’ according to findings by a New York University research center. The survey of more than 150 studies and reports, published by AI Now Institute, found that ‘diversity disaster’ has contributed to flawed systems that perpetuate gender and racial biases,” Paul writes.

纽约大学研究中心的调查结果显示，卡里·保罗(Kari Paul)在2019年为《卫报》撰写的一篇文章指出，“人工智能领域缺乏多样性已经到了一个“时刻”。 由AI Now Institute发表的对150多项研究和报告的调查发现，“多样性灾难”助长了存在缺陷的系统，使性别和种族偏见长期存在。

有什么解决方案？ (What Are the Solutions?)

One remedy is educating girls — including girls of color — sooner and more widely about the field and making appropriate educational opportunities and career guidance accessible to them early on.

一种补救措施是对女孩(包括有色女孩)进行早期和广泛的有关领域的教育，并尽早为她们提供适当的教育机会和职业指导。

Mastery of complex subjects is required, so girls must continue building on their basic math and science education, and intensify their focus as early as seventh grade. High school and certainly college may be too late to capture their interest so they can acquire the needed foundation.

必须精通复杂的科目，因此女孩必须继续建立基础数学和科学教育，并从七年级开始加强学习重点。 高中，当然还有大学，可能为时已晚，无法引起他们的兴趣，因此他们可以获得所需的基础。

Girls interested in AI will need to write code, algorithms and source data sets. Beyond that, they will need to understand and eliminate bias in data sets, as well as in applications designed to serve humanity.

对AI感兴趣的女孩将需要编写代码，算法和源数据集。 除此之外，他们还需要理解并消除数据集以及为人类服务的应用程序中的偏见。

Along with a rigorous early academic foundation, girls must develop social and emotional learning skills to help fuel their careers. These skills will prove beneficial whether they are leading a team or a company or programming soft skills into a robot.

除了严格的早期学术基础之外，女孩还必须发展社交和情感学习技能，以助其事业发展​​。 无论是带领团队或公司，还是将软技能编程为机器人，这些技能都将证明是有益的。

A proven method for inspiring girls is to bring female role models working in AI into your classroom. Give girls a chance to ask these experts questions about their careers and personal stories. One way to start your search for experts is to inquire at universities and businesses from your local community; network with those professionals to build your sources.

激励女孩的一种行之有效的方法是将在AI中工作的女性榜样带入课堂。 让女孩有机会向这些专家询问有关其职业和个人故事的问题。 开始寻找专家的一种方法是向当地社区的大学和企业打听； 与这些专业人员建立联系以建立您的资源。

Girls’ visions for the future are boosted when they’re introduced to female role models who demonstrate rewarding careers in the AI field and show that girls can excel in this arena.

将女性的榜样介绍给女性榜样，可以提高女孩对未来的看法，这些榜样在AI领域表现出可喜的职业，并表明女孩可以在这个领域脱颖而出。

As women enter the profession and assume leadership roles, society is seeing the advantages of perspectives they bring to AI systems.

随着女性进入该行业并担任领导角色，社会正在看到她们为AI系统带来的观点的优势。

For example, Mishra builds new systems that enhance people’s lives and give them a positive experience of interacting with technology. “AI is ingrained into every aspect of our lives now and will be even more so in the future,” says Mishra. Her advice to girls is to “dream big: ambition is attractive and inspires those around you.”

例如，米什拉(Mishra)建立了新的系统来改善人们的生活，并为他们提供与技术互动的积极体验。 米什拉说：“人工智能已经根深蒂固，现在已经渗透到我们生活的方方面面，将来会更加如此。” 她对女孩的建议是“大胆梦想：雄心勃勃，会激发周围的人。”

About the author –– Linda Calhoun is the founder and executive producer of Career Girls. Career Girls is a nonprofit organization dedicated to closing the imagination gap for girls around the world.

作者简介– Linda Calhoun是《 职业女孩》 的创始人兼执行制片人 。 职业女孩组织是一个非盈利性组织，致力于消除世界各地女孩的想象力鸿沟。