Alex Hanna博士是社會學家和研究科學家，致力于Google的機器學習公平性和道德AI。 (Dr. Alex Hanna is a sociologist and research scientist working on machine learning fairness and ethical AI at Google.)

Before that, she was an Assistant Professor at the Institute of Communication, Culture, Information and Technology at the University of Toronto. She received her PhD in Sociology from the University of Wisconsin-Madison.

在此之前，她是多倫多大學傳播，文化，信息和技術研究所的助理教授。 她獲得了威斯康星大學麥迪遜分校的社會學博士學位。

To learn more about Dr. Alex Hanna’s background and work, you can check out her personal website and follow her on Twitter.

要了解有關Alex Hanna博士的背景和工作的更多信息，您可以查看她的 個人網站 并在 Twitter上 關注她 。

1.您在公司中扮演什么角色？ (1. What’s your role within the company?)

I’m a research scientist within the Ethical AI team at Google. The Ethical AI team focuses on ensuring that AI is deployed in ethical and fair ways. People on our team have been focused on a few different domains around fairness in algorithmic systems, and understanding transparency in models and data, and also trying to understand various ways of reporting all those levels.

我是道德AI團隊的研究科學家 在Google。 道德的AI團隊致力于確保以道德和公平的方式部署AI。 我們團隊中的人們專注于圍繞算法系統公平性的幾個不同領域，理解模型和數據的透明性，并試圖了解報告所有這些級別的各種方式。

I’m the team’s first sociologist, and a lot of what I do is focusing on understanding the assumptions of data that are used in machine learning systems, where the data comes from, and the sorts of considerations that are given when it comes to the data that machine learning models are trained on.

我是該團隊的第一位社會學家，我所做的很多工作都集中在理解機器學習系統中使用的數據假設，數據來自何處以及在涉及到數據時要考慮的各種因素。訓練機器學習模型的數據。

2.您的背景是什么？ 您是如何參與這項工作的，直到今天您所處的位置？ (2. What’s your background? How did you get involved with this work and end up where you are today?)

My PhD is in Sociology, and I started getting involved with AI ethics around 2017, when I attended a multi disciplinary workshop in the Netherlands with a few people that are in the space. I started wanting to get involved a bit more, and so I started to read the literature on it.

我的博士學位是社會學，我于2017年左右開始涉足AI倫理學，當時我參加了在荷蘭舉行的一次多學科研討會，當時在場的人數很少。 我開始想更多地參與其中，因此我開始閱讀有關它的文獻。

Before I was at Google, I was a professor at the University of Toronto’s Institute of Communication, Culture, Information and Technology. Then I came to Google, first in a different position; but then, I moved into the research direction, as I was already doing that work, which is how I found my way into the role.

在加入Google之前，我曾是多倫多大學傳播，文化，信息和技術學院的教授 。 然后我來到Google，先是擔任另一個職位。 但是后來，由于我已經在從事研究工作，所以我朝研究方向發展，這就是我找到角色的方式。

Google is very collaborative. I really appreciated that I’ve had the opportunity to work with a lot of different people, and to do work that I found quite important, and to publish it in venues which I thought were important. When I was at Toronto, I was not really having the sort of conversations that I was wanting to have, so that move has been excellent.

Google非常合作。 我非常感謝我有機會與許多不同的人一起工作，并做了我認為非常重要的工作，并將其發布在我認為很重要的場所。 當我在多倫多時，我并沒有真正想要的那種對話，所以這一舉動非常出色。

The team is really great — it’s got people from a wide variety of backgrounds, and is racially and gender diverse, probably more than any other team I’ve ever worked on, inside academia or outside.

這個團隊真的很棒-擁有來自不同背景的人，種族和性別也各不相同，這可能比我曾經在學術界或外部工作過的任何團隊都要多。

3.您在公司內部如何運作？您的日常工作是怎樣的？ (3. How do you operate within the company and what is your day to day work like?)

Our team is a research team that sits within Google Brain. Google Brain is oriented such that you can create things or do research that doesn’t necessarily have to be connected to product; but because of who we are and what we work on, we’re often in many conversations about products and policy. So, we can do both original research which may not have a direct bearing on product and what products do, but also work that has very serious policy and product implications.

我們的團隊是位于Google Brain內部的研究團隊。 Google Brain面向您，您可以創建不一定要與產品相關聯的事物或進行研究； 但是由于我們是誰以及我們在做什么，我們經常在關于產品和政策的許多對話中。 因此，我們既可以進行可能與產品沒有直接關系的原始研究，又可以進行產品的作用，但是也可以進行對政策和產品有非常嚴重影響的研究。

There’s also a lot of different stuff that we do both internally and externally related to policies around fairness and data ethics that people have come to our team to ask about. One thing I’m working on right now is thinking about how we annotate for gender in machine learning systems.

我們在內部和外部也有很多不同的東西，涉及到公平和數據道德方面的政策，人們來找我們的團隊來詢問。 我現在正在做的一件事是考慮如何在機器學習系統中為性別添加注釋。

Right now, machine learning systems such as most facial recognition or facial analysis systems look at a face and make some sort of judgment about gender, which is nonsensical because you can’t judge gender from someone’s face, gender identity is an internal state — what you’re getting at is simply something more like gender expression. Google has already stepped away from building a public API gender classifier, and has removed the gender terms from their vision APIs.

目前，諸如大多數面部識別或面部分析系統之類的機器學習系統會看著一張臉，并對性別做出某種判斷，這是荒謬的，因為您無法從某人的臉上判斷性別，性別認同是一種內部狀態–您得到的僅僅是性別表達。 Google已經遠離建立公共API性別分類器的步驟，并已從其視覺API中刪除了性別術語 。

This and similar things are in no small part due to our team. So we’re continuing that work, and we’re trying to come up with internal and external guidelines; because you shouldn’t annotate gender in order to build a classifier, and you need to take into consideration what the purpose of the system would be, and how it can potentially have detrimental downstream effects. So that’s one project I’m involved with.

這和類似的事情在很大程度上要歸功于我們的團隊。 因此，我們正在繼續進行這項工作，并試圖提出內部和外部準則； 因為您不應該為了建立分類器而對性別進行注釋，并且需要考慮系統的目的以及它可能對下游產生不利影響的原因。 這是我參與的一個項目。

We focus on original research, but with real product and policy impact.

我們專注于原始研究，但會對產品和政策產生實際影響。

4.您或您的團隊所影響的積極變化的具體例子是什么？ (4. What’s a concrete example of a positive change you or your team influenced?)

Model Cards is a framework that we published on two years ago at ACM FAT* (now ACM FAccT). It’s a way of reporting on models, and their performance across different demographic groups and also particular ethical considerations for models. That work was published in an academic venue, but then within the last year it was adopted in many different places. For instance, Google Cloud has two public APIs that now have public model cards, so anybody can go in and look at how they do across different population subgroups, and report out what they’re doing and how well they do.

模型卡是我們兩年前在ACM FAT *(現為ACM FAccT)上發布的框架。 這是報告模型，模型在不同人口群體中的表現以及模型的特殊道德考慮因素的一種方式。 該作品在學術場所出版，但在去年被許多不同的地方采用。 例如， Google Cloud有兩個公共API，現在有公共模型卡 ，因此任何人都可以進入并研究它們在不同人口子群體中的表現，并報告它們的工作狀況和運行狀況。

The model card work has also led to new technical infrastructure, namely Fairness Indicators, that allows for statistics that are a part of the framework to be computed more automatically. And, the framework outlines the steps that are necessary if you’re going to do this work, and what you need to consider — it’s not just pushing a button and seeing how your thing does, and then walking away from it. You have to think deeply about the model, and how it’s being used in practice. So that itself is something that’s very appealing to particular teams.

模型卡的工作還導致了新的技術基礎結構，即公平性指標 ，它允許更自動地計算作為框架一部分的統計數據。 而且，該框架概述了如果您要進行此工作所需的步驟以及需要考慮的內容–它不僅是按一個按鈕并查看您的工作方式，然后再走一步。 您必須深思模型，以及如何在實踐中使用它。 因此，它本身對于某些團隊來說非常有吸引力。

5.做這項工作或擔任這個角色最讓您驚訝的是？ (5. What’s surprised you the most about doing this work or being in this role?)

I’ve been happily surprised that there’s a team that I’ve found that is very interdisciplinary, and where I’m still very welcome and happy. And this is my first job outside of academia, so I guess I was surprised that there’s a lot of interest in this kind of work.

令我感到驚訝的是，我發現有一支非常跨學科的團隊，而且我仍然非常歡迎和高興。 這是我在學術界以外的第一份工作，所以我想我對這種工作引起了極大的興趣，對此我感到驚訝。

I guess it’s not surprising that some of the more logical conclusions of this work clash with the imperatives of working in a corporation in a capitalist economy, but I also think that part of this work is about trying to get people to the point of realizing that.

我想這項工作的一些更合乎邏輯的結論與在資本主義經濟中在公司工作的必要性相沖突并不奇怪，但是我也認為這項工作的一部分是試圖使人們意識到這一點。 。

For instance, Manny Moss, Jake Metcalf, and danah boyd wrote this article that talks about what they call ethics owners in tech corporations, that is, people in my type of roles. They’ve tried to go through what this looks like across different companies by performing qualitative interviews.

例如， 曼尼·莫斯 ( Manny Moss) ， 杰克·梅特卡夫 ( Jake Metcalf )和丹娜·博伊德(danah boyd)撰寫了這篇文章，討論了他們所謂的科技公司的道德所有者 ，即擔任我這種角色的人。 他們試圖通過進行定性訪談來了解不同公司的情況。

And it confirms my intuitions when they say that there are limitations — what they’re effectively saying is that ethics owners are trying to do X, and guide product and policy development in particular ways, but market fundamentalism and tech solutionism resist those pushes.

當他們說有局限性時，這證實了我的直覺-他們實際上是在說道德負責人試圖做X，并以特定的方式指導產品和政策的開發，但是市場原教旨主義和技術解決方案卻抵制了這些推動。

6.您最大的挑戰和收獲是什么？ 您認為科技公司有可能關心道德并認真對待嗎？ 我們是否會達到倫理學家跨團隊扎根的地步？ (6. What have been your biggest challenges and takeaways? Do you think it’s possible for tech companies to care about ethics and take it seriously? Will we ever get to a point where ethicists are embedded across teams?)

The challenge of building ethical tech is that we are embedded in a system of racial surveillance capitalism. No one necessarily goes out and intentionally tries to make an AI that works poorer for people with darker skin, or to return search results that show sexualized images when you search Black or Asian girls. As Ruha Benjamin and Safiya Noble, amongst other critical tech and race scholars have shown, these machines are racist in their effects but typically not their intent. And that manifests in myriad ways because Google Search and AI are sociotechnical systems.

建設道德技術的挑戰在于，我們必須融入種族監督資本主義體系中。 沒有人一定會走出去，有意嘗試使一種AI對皮膚較黑的人表現較差 ，或返回當搜索黑人或亞洲女孩時顯示性圖像的搜索結果。 正如魯哈·本杰明 ( Ruha Benjamin)和薩菲婭·諾布爾 ( Safiya Noble )以及其他批判性技術和種族學者所表明的那樣，這些機器的作用是種族主義的，但通常不是意圖的。 由于Google搜索和AI是社會技術系統，因此這以多種方式體現。

It’s not just the training data that these systems are based upon, but because these systems are developed within this social and economic system. However, the incentives to act ethically can’t be left only to negative sanctions like brand risk. That’s why there needs to be regulation for data protection and AI — because it presents a way to actually force Silicon Valley’s hand on it.

這些系統不僅基于訓練數據，還因為這些系統是在此社會和經濟系統中開發的。 但是，采取道德行動的動機不能僅歸因于品牌風險等負面制裁。 這就是為什么需要對數據保護和AI進行監管的原因-因為它提供了一種實際迫使硅谷介入的方法。

7.您如何處理未認真對待自己的聲音或工作的情況？ 您有關于如何處理的建議嗎？ (7. How do you deal with situations where your voice or work isn’t taken seriously? Do you have advice for how to deal with that?)

I suppose I just get louder. But also finding people who will be advocates or champions. I think that’s a good strategy [though] building those networks takes time.

我想我會變得更大聲。 而且還要找到將成為倡導者或擁護者的人。 我認為這是一個好策略，盡管建立這些網絡需要時間。

8.您希望在此空間中的哪個區域能發揮更大的作用或希望看到改善？ (8. What’s an area in this space that you wish you could make more of an impact in or want to see improve?)

I think that a lot of the vision of this work is still that people consider it to be highly technical, when it shouldn’t be considered technical. There’s plenty of work that’s been done in this space that really comes from different fields. Researchers need to be humble, especially technical researchers.

我認為這項工作的很多愿景仍然是人們認為它是高度技術性的，而不應將其視為技術性的。 在這個領域已經完成了很多工作，這些工作實際上來自不同領域。 研究人員必須謙虛，尤其是技術研究人員。

And of course I’m going to say this, because much of the research I do is non-technical, but there’s a plethora of work that’s been done in science and technology studies and sociology and whatnot that has a lot of bearing on this, and I feel like those ideas need to be taken seriously.

當然，我要說的是，因為我所做的大部分研究都是非技術性的，但是在科學技術研究和社會學領域已經做了大量的工作，而與此相關的事情很多，我覺得這些想法需要認真對待。

If you just think it’s a technical problem, you’re missing the forest for the trees.

如果您只是認為這是一個技術問題，那么您就錯過了森林。

9.您仰望誰？ 誰激勵你？ (9. Who do you look up to? Who inspires you?)

A lot of the folks driving the AI ethics conversations are women, especially Black women, women of color, and queer folks. A lot of the folks who are doing the heavy lifting, or have the most nuanced and creative views are that.

推動AI倫理對話的很多人都是女性，尤其是黑人女性，有色人種和同志。 許多正在做繁重工作或擁有最細微和最有創意的觀點的人就是這樣。

This is a funny thing to say, but I really respect my manager, Timnit Gebru (Twitter). In 2016 she went to NeurIPS and it was all these white guys, and afterwards she started Black in AI as a group to advocate for more black AI researchers, and it’s awesome. It took an immense amount of work. And instead of just being like, I don’t want to work in AI, she was like, let’s just change this entire field. So I really respect her and her advocacy. She’s been an immense advocate for me in social science research, and is just a great advocate in general.

這句話很有趣，但是我非常尊重我的經理Timnit Gebru ( Twitter )。 在2016年，她去了NeurIPS ，所有這些都是白人，然后她在AI中成立了Black，作為一個團隊來倡導更多的黑人AI研究人員，這很棒。 花費了大量的工作。 她不只是想不想，我不想在人工智能領域工作，而是讓我們改變整個領域。 因此，我非常尊重她和她的倡導。 她一直是我在社會科學研究領域的大力擁護者，而且總體而言只是一名偉大的擁護者。

I have immense amounts of respect for the organizing work that Tawana Petty has done in Detroit, both with the Detroit Community Technology Project and the Our Data Bodies project, along with the rest of her collaborators in ODB: Tamika Lewis, Mariella Saba, Seeta Pe?a Gangadharan, and Kim Reynolds. Mariella is also involved with the Stop LAPD Spying Coalition, who have been out in front fighting policing and surveillance technologies.

我對Tawana Petty在底特律所做的組織工作深表敬意，包括底特律社區技術項目和我們的數據機構項目，以及她在ODB中的其他合作者：Tamika Lewis，Mariella Saba，SeetaPe?a Gangadharan和Kim Reynolds。 瑪麗埃拉(Mariella)還參與了制止LAPD間諜聯盟 ( Stop LAPD Spying Coalition)的工作 ，后者在前線警務和監視技術方面一直處于領先地位。

I also really respect danah boyd, who I know has been doing this work for years, and Joan Donovan (Twitter), who started the Critical Internet Studies Slack workspace, is someone who’s also been at this for quite some time. And my friends who have known me for like a decade, like Anna Lauren Hoffmann (Twitter), who helped bring me into this work. She’s a professor at the University of Washington.

我也非常尊敬danah boyd ，我知道他從事這項工作已經很多年了，而開始了Critical Internet Studies Slack工作區的Joan Donovan ( Twitter )也已經從事了很長時間。 還有我認識我已有十年之久的朋友，例如安娜·勞倫·霍夫曼 ( Anna Lauren Hoffmann ， 推特 )，他幫助我從事了這項工作。 她是華盛頓大學的教授。

10. 對于想要參與但不知道如何開始的學生，應屆畢業生或技術工作者，您有什么建議？ (10. What advice do you have for students, new grads or tech workers who want to get involved but don’t know how to start?)

I think what they should do is work on assessing the field. Some part of that is trying to understand what kind of subsection of the work really interests them — it could be something that’s technical, legal scholarship, social theory, political philosophy, or critical race theory, or sociology of gender.

我認為他們應該做的是評估該領域的工作。 其中的一部分是試圖弄清作品的哪一部分真正讓他們感興趣—可能是技術，法律學術，社會理論，政治哲學，批判種族理論或性別社會學。

There’s lots of different angles in which to come from it, and there’s just so much work that still needs to be done, especially in translating some of the concepts from one field to another.

它有很多不同的角度，還有很多工作要做，尤其是將某些概念從一個領域轉換到另一個領域時。

Folks need to explore the different angles and find what they’re interested in or passionate about, and then from there, read what they find, follow citation trails, and find the scholars, organizers, and activists who are doing work in this space.

人們需要探索不同的角度，找到自己感興趣或感興趣的事物，然后從那里閱讀發現的事物，遵循引文線索，并找到在此領域開展工作的學者，組織者和活動家。

感謝您收看采訪并支持該系列！ (Thanks for checking out the interview and supporting the series!)

This project was started by Tiffany Jiang and Shelly Bensal out of curiosity. Even before we graduated and started working in tech, we asked ourselves: Who are the ethicists working within tech companies today? Which companies offer such roles or teams? How much of the work is self-initiated? Lastly, what does “responsible innovation” or “ethics” work entail exactly?

這個項目是由Tiffany Jiang和Shelly Bensal出于好奇而發起的。 甚至在我們畢業并開始從事技術工作之前，我們就問自己： 今天在技術公司工作的道德主義者是誰？ 哪些公司提供此類角色或團隊？ 多少工作是自我發起的？ 最后，“負責任的創新”或“道德”的工作究竟意味著什么？

We hope this series can serve as a helpful resource to students, new grads or anybody wishing to do work in this space but don’t know how to get involved. If you have any thoughts or comments you want to share with us, we’d love to hear them. Let us know if there’s someone you’d like us to interview next!

我們希望本系列可以對學生，應屆畢業生或希望在此領域工作但不知道如何參與的任何人提供有用的資源。 如果您有任何想法或意見要與我們分享，我們很樂意聽到。 讓我們知道是否有人想要我們接下來面試！

Twitter: (@EthicsModels) | Email: ethicsmodels.project@gmail.com.

Twitter： (@EthicsModels) | 電子郵件：ethicsmodels.project@gmail.com。