api 規(guī)則定義

Disclaimer: I am an independent researcher @Taraaz with no affiliation with any of the companies mentioned below.

免責(zé)聲明：我是 @Taraaz 的獨(dú)立研究 人員 ，與以下提到的任何公司均無(wú)關(guān)聯(lián)。

Last month, my friend posted a story on Instagram. It was about boycotting a Unilever-made skin lightening product from India which goes by the brand name “Fair & Lovely.” The campaign’s goal? To bring attention to the larger problem of colorism in India.

上個(gè)月，我的朋友在Instagram上發(fā)布了一個(gè)故事。 這是關(guān)于抵制聯(lián)合利華(Unilever)生產(chǎn)的印度亮膚產(chǎn)品，該產(chǎn)品的商標(biāo)為“ Fair＆Lovely” 。 該運(yùn)動(dòng)的目標(biāo)？ 引起人們對(duì)印度色彩問題的關(guān)注。

I’m not Indian. But the campaign’s message resonated with me. In Iran, where I grew up, I too encountered similar “beauty” products that claimed to be able to lighten the skin of those who used them. I took them for granted when I was a kid. But these days, in the wake of the Black Lives Matter movement, there’s been a moment of awakening for many from different countries who think about racism and colorism at home.

我不是印度人。 但是競(jìng)選活動(dòng)的信息引起了我的共鳴。 在我長(zhǎng)大的伊朗，我也遇到過(guò)類似的“美容”產(chǎn)品，這些產(chǎn)品聲稱能夠減輕使用它們的人的皮膚。 我小時(shí)候把它們視為理所當(dāng)然。 但是這些天來(lái)，隨著“黑人生活問題”運(yùn)動(dòng)的到來(lái)，來(lái)自不同國(guó)家的許多人在家里都考慮到種族主義和色彩主義，這是一個(gè)覺醒的時(shí)刻。

While reading through tweets from the campaign, I began to think about the emotion behind these tweets. I wondered how Unilever would perceive and react to these tweets? Of course, their social media team wouldn’t be able to read every single tweet. But perhaps they use social media analysis tools –powered by emotion recognition technologies – to get a sense of people’s demand.

在閱讀競(jìng)選活動(dòng)中的推文時(shí)，我開始考慮這些推文背后的情感。 我想知道聯(lián)合利華會(huì)如何看待并回應(yīng)這些推文？ 當(dāng)然，他們的社交媒體團(tuán)隊(duì)無(wú)法閱讀每條推文。 但是也許他們使用由情感識(shí)別技術(shù)支持的社交媒體分析工具來(lái)了解人們的需求。

I’m a researcher in technology and human rights. My job is to understand how technical designs impact human rights. I know that one of the promises of text-based emotion analysis tools is to help companies to understand customer satisfaction based on social media engagement.

我是技術(shù)與人權(quán)研究人員。 我的工作是了解技術(shù)設(shè)計(jì)如何影響人權(quán)。 我知道，基于文本的情感分析工具的一項(xiàng)承諾是幫助公司基于社交媒體參與來(lái)了解客戶滿意度。

That’s why I decided to use the example of “Fair & Lovely” to scrutinize off-the-shelf machine learning-based emotion analysis APIs. How do these practices — which are now the norm among major brands — perform in a specific case such as this? In particular, I wondered whether the positive sentiment of the phrase “Fair & Lovely” might trick the emotion analysis tool and lead to the misclassification of a sentence’s sentiment, even if the overall sentiment of the sentence may not be positive.

這就是為什么我決定使用“公平可愛”的示例來(lái)仔細(xì)研究基于現(xiàn)成機(jī)器學(xué)習(xí)的情緒分析API。 這些做法(現(xiàn)在是主要品牌中的普遍做法)在這樣的特定情況下如何表現(xiàn)？ 我特別想知道，“ Fair＆Lovely”一詞的積極情緒是否會(huì)欺騙情緒分析工具，并導(dǎo)致句子情緒的錯(cuò)誤分類 ，即使句子的整體情緒可能不是積極的。

This question led me to write this blog post, especially for developers who use machine learning technologies as a service (MLaaS) and also for my fellow human rights practitioners who are interested in examining human rights implications of tech companies’ third-party relationships.

這個(gè)問題使我撰寫了這篇博客文章，特別是對(duì)于使用機(jī)器學(xué)習(xí)技術(shù)即服務(wù)(MLaaS)的開發(fā)人員，以及對(duì)研究技術(shù)公司的第三方關(guān)系對(duì)人權(quán)的影響感興趣的我的其他人權(quán)從業(yè)人員。

• I’ll tell you why APIs terms are so important to understand, and what have been some misuses of APIs in the past few years.
  我將告訴您為什么理解API術(shù)語(yǔ)如此重要，以及過(guò)去幾年對(duì)API的一些誤用。

• I’ll choose IBM Tone Analyzer API and ParallelDots Text Analysis Emotion API to test their result on tweets about Unilever’s “Fair & Lovely” product. I’ll walk you through those APIs’ developers’ policies, terms of service, APIs documents, and show you what could be some criteria to consider before choosing that API.

  我將選擇IBM Tone Analyzer API和ParallelDots Text Analysis Emotion API來(lái)測(cè)試有關(guān)聯(lián)合利華“ Fair＆Lovely”產(chǎn)品的推文上的結(jié)果。 我將向您介紹這些API的開發(fā)人員政策，服務(wù)條款，API文檔，并向您展示在選擇該API之前可能要考慮的一些標(biāo)準(zhǔn)。

• I’ll provide a set of recommendations for developers who want to use general-purpose APIs for a specific domain in a responsible manner. I’ll also provide recommendations for auditors and human rights practitioners who study companies’ third-party relationships.
  我將為想要以負(fù)責(zé)任的方式針對(duì)特定域使用通用API的開發(fā)人員提供一系列建議。 我還將為研究公司的第三方關(guān)系的審計(jì)師和人權(quán)從業(yè)人員提供建議。

So, let’s say you are a developer or a social media analyst, and you are approached by Unilever to analyze the emotion behind customers’ social media engagement. What do you do?

因此，假設(shè)您是開發(fā)人員或社交媒體分析師，聯(lián)合利華(Unilever)會(huì)聯(lián)系您分析客戶參與社交媒體背后的情感。 你是做什么？

As a hypothetical, we will assume that you don’t have the necessary skills, data, and computation power to build a whole custom machine learning model, nor do you want to use any pre-trained model. Instead, you choose the easiest route: an off-the-shelf general-purpose emotion-analysis API.

作為假設(shè)，我們將假設(shè)您沒有構(gòu)建完整的自定義機(jī)器學(xué)習(xí)模型所需的技能，數(shù)據(jù)和計(jì)算能力，也不希望使用任何經(jīng)過(guò)預(yù)先訓(xùn)練的模型。 相反，您選擇了最簡(jiǎn)單的方法：現(xiàn)成的通用情感分析API。

If that’s the case, what would be your criteria to choose and use these APIs in a responsible manner?

如果是這樣，您以負(fù)責(zé)任的方式選擇和使用這些API的標(biāo)準(zhǔn)是什么？

API有規(guī)則，而且功能強(qiáng)大 (APIs have rules — and power 🔍)

First, the basics. An Application Programming Interface (API) is what helps different software applications interact with each other. It allows one application to make a request (either data or service) and the other application respond to it. For example, if you are a social media company and want researchers to use your data to conduct research, you give them access to those data via an API. If you want IoT devices at home to interact with each other (for example your smart lamp reacts to events on your Google calendar) then you connect those services through APIs.

首先，基礎(chǔ)知識(shí)。 應(yīng)用程序編程接口(API)可以幫助不同的軟件應(yīng)用程序相互交互。 它允許一個(gè)應(yīng)用程序發(fā)出請(qǐng)求(數(shù)據(jù)或服務(wù))，而另一個(gè)應(yīng)用程序?qū)Υ俗鞒鲰憫?yīng)。 例如，如果您是一家社交媒體公司，并且希望研究人員使用您的數(shù)據(jù)進(jìn)行研究，則可以通過(guò)API授予他們?cè)L問這些數(shù)據(jù)的權(quán)限。 如果您希望家里的IoT設(shè)備相互交互(例如，智能燈對(duì)Google日歷中的事件做出React)，則可以通過(guò)API連接這些服務(wù)。

But as with any kind of interaction, there need to be rules between those services before starting working with each other. These rules are set by APIs policies, developers’ policies, and Terms of Service.

但是，與任何類型的交互一樣，在開始彼此合作之前，這些服務(wù)之間需要有規(guī)則。 這些規(guī)則由API政策，開發(fā)者政策和服務(wù)條款設(shè)置。

So far, so good. But when you give it more thought, you realize that those services make agreements between each other to provide services for you, as a user, or to handle your data, without you fully understand how they reach agreements.

到目前為止，一切都很好。 但是，當(dāng)您多加考慮時(shí)，您會(huì)意識(shí)到這些服務(wù)之間會(huì)相互達(dá)成協(xié)議以為您 (作為用戶)提供服務(wù)或處理您的數(shù)據(jù)，而無(wú)需您完全了解它們?nèi)绾芜_(dá)成協(xié)議。

It’s kind of bizarre, right?

有點(diǎn)奇怪，對(duì)吧？

Here are a couple of reminders of why this sort of thing is important.

這里有一些提醒，說(shuō)明這種事情為什么很重要。

1) You remember Cambridge Analytica and Facebook, right? (As a refresher, 87 million users’ information was “improperly” shared with Cambridge Analytica to analyze and manipulate Facebook users’ political behavior). Long story short, the underlying reason for such privacy-invasive data sharing practice was because of the abuse of Facebook’s APIs. As a result, Facebook restricted developers’ data access by making changes in their APIs policies.

1)您還記得Cambridge Analytica和Facebook， 對(duì)嗎？ (作為復(fù)習(xí)，與劍橋分析公司“不當(dāng)”共享了8700萬(wàn)用戶的信息，以分析和操縱Facebook用戶的政治行為)。 長(zhǎng)話短說(shuō)，這種侵犯隱私的數(shù)據(jù)共享做法的根本原因是因?yàn)闉E用了Facebook的API。 結(jié)果，Facebook通過(guò)更改其API策略來(lái)限制開發(fā)人員的數(shù)據(jù)訪問。

2) There are also concerns when ML APIs are used as analytical services. In this case, developers are the ones who have the data and go to big tech companies’ ML APIs to process that data (MLaaS). Joy Buolamwini and Timnit Gebru’s Gender Shades study revealed significant racial and gender discrimination of several Facial Recognition APIs. In fact, as a result, big tech companies limited providing their APIs to law enforcement agencies in the US (who knows about their business relationship with other countries though…? 🤷🏻?♀?)

2)當(dāng)將ML API用作分析服務(wù)時(shí)，也存在一些問題。 在這種情況下，開發(fā)人員就是擁有數(shù)據(jù)并前往大型科技公司的ML API來(lái)處理該數(shù)據(jù)(MLaaS)的人。 Joy Buolamwini和Timnit Gebru的“ 性別陰影”研究顯示了幾種面部識(shí)別API的明顯種族和性別歧視。 實(shí)際上，結(jié)果是，大型科技公司將其API提供給美國(guó)的執(zhí)法機(jī)構(gòu)(盡管他們知道他們與其他國(guó)家的業(yè)務(wù)關(guān)系…？…?🤷🏻?)

But what about developers’ responsibilities who want to use tech companies’ general-purpose services? Is there any guidance to help them choose and use those ML APIs responsibly in their specific domains?

但是，想要使用科技公司的通用服務(wù)的開發(fā)人員的責(zé)任又如何呢？ 是否有任何指南可幫助他們?cè)谄涮囟I(lǐng)域中負(fù)責(zé)任地選擇和使用那些ML API？

情緒分析API：IBM Tone Analyzer或ParalletDots文本分析？ (An emotion analysis API: IBM Tone Analyzer or ParalletDots Text Analysis?)

As a developer, if you don’t want to build an ML system from scratch nor do you want to use a pre-trained model, the other option is to use cloud-based ML APIs. Everything is ready to go: you set up a developer account and receive API credentials, you provide input data, the service provider works its “magic,” and you receive the results as output. Easy! You don’t even need any knowledge about data science and machine learning to be able to integrate that API with your product. Or at least, this is how companies market their services.

作為開發(fā)人員，如果您不想從頭開始構(gòu)建ML系統(tǒng)，也不想使用預(yù)先訓(xùn)練的模型，那么另一種選擇是使用基于云的ML API。 一切準(zhǔn)備就緒：您設(shè)置了開發(fā)人員帳戶并獲得了API憑證，提供了輸入數(shù)據(jù)，服務(wù)提供者發(fā)揮了“魔力”，然后將結(jié)果作為輸出接收。 簡(jiǎn)單！ 您甚至不需要任何有關(guān)數(shù)據(jù)科學(xué)和機(jī)器學(xué)習(xí)的知識(shí)，就能將該API與您的產(chǎn)品集成。 至少，這就是公司營(yíng)銷其服務(wù)的方式。

As a developer, you have an obvious set of criteria to choose a service, right: criteria such as accuracy, cost, and speed. But what if you wanted to pick your ML API service based on other criteria, such as privacy, security, fairness, and transparency? What process do you go through? What do you check?

作為開發(fā)人員，您有一套顯而易見的選擇服務(wù)標(biāo)準(zhǔn)，正確的是：諸如準(zhǔn)確性，成本和速度之類的標(biāo)準(zhǔn)。 但是，如果您想根據(jù)其他標(biāo)準(zhǔn)(例如隱私，安全性，公平性和透明度)來(lái)選擇ML API服務(wù)，該怎么辦？ 您要經(jīng)歷什么過(guò)程？ 你查什么？

Let’s go back to the “Fair & Lovely” tweets. Putting myself of our hypothetical developer, I collected several hundred tweets about “fair & lovely” in the English language using Twint. Next, I looked at RapidAPI, a platform that helps developers to manage and compare different APIs, and picked IBM Watson Tone Analyzer and ParrallelDots as the best options. Both services promise to infer emotions including fear, anger, joy, happiness, etc. from tweets.

讓我們回到“公平可愛”的推文上 。 讓我成為假設(shè)的開發(fā)人員，我使用Twint收集了數(shù)百條有關(guān)英語(yǔ)中“公平和可愛”的推文。 接下來(lái)，我查看了RapidAPI (一個(gè)可幫助開發(fā)人員管理和比較不同API的平臺(tái))，并選擇了IBM Watson Tone Analyzer和ParrallelDots作為最佳選擇。 兩種服務(wù)都承諾從推文中推斷出包括恐懼，憤怒，喜悅，幸福等情緒。

Then I registered with both services and received API credentials for free developer accounts. IBM’s free “Lite” account provides 2500 API calls per month; ParallelDots is free for 1000 API hits/day.

然后，我同時(shí)注冊(cè)了這兩項(xiàng)服務(wù)，并獲得了免費(fèi)的開發(fā)人員帳戶的API憑據(jù)。 IBM的免費(fèi)“ Lite”帳戶每月提供2500次API調(diào)用。 ParallelDots免費(fèi)提供每天1000次API點(diǎn)擊。

Finally, I ran the experiments below. These are a result of providing my corpus of “fair & lovely” tweets as input and then gathering the APIs’ output. You can see more examples in this spreadsheet.

最后，我進(jìn)行了以下實(shí)驗(yàn)。 這些是提供我的“公平和可愛”推文集作為輸入，然后收集API的輸出的結(jié)果。 您可以在此電子表格中看到更多示例。

Please note the drastically different results of the two services.

請(qǐng)注意，兩種服務(wù)的結(jié)果截然不同。

I also changed the word “fair & lovely” to more neutral phrases such as “your product” and “this product.” The output result changed. However, from a human analytical standpoint the message — and its sentiment — are the same.

我還將“公平而可愛”一詞改為了更中性的詞組，例如“您的產(chǎn)品”和“此產(chǎn)品”。 輸出結(jié)果已更改。 但是，從人類分析的角度來(lái)看，信息及其情感是相同的。

At this point, I wouldn’t use either of these tools for this specific case! You tell me if the sentence “Unilever- cancel fair & lovely -sign the petition!” is joyous! 🤦🏻?♀?

在這一點(diǎn)上，在這種情況下，我不會(huì)使用任何一種工具！ 你告訴我，如果句子“ Unilever-取消公平和可愛-簽署請(qǐng)?jiān)笗?#xff01;” 很高興！ ♀?♀?

However, let’s say our hypothetical developer still thinks that there are benefits for using these tools.

但是，假設(shè)我們的假設(shè)開發(fā)人員仍然認(rèn)為使用這些工具會(huì)有好處。

In that case, we’d need to take into account the following criteria. I have to say this list is very preliminary and by no means comprehensive. But at least it gives you a sense of what to look for if you, as a developer, decide to use these tools.

在這種情況下，我們需要考慮以下條件。 我必須說(shuō)這個(gè)清單是非常初步的，絕不是全面的。 但是至少，如果您作為開發(fā)人員決定使用這些工具，它至少可以使您了解要尋找的內(nèi)容。

注冊(cè)：隱私政策和服務(wù)條款 (Registration: privacy policies and terms of service)

When you want to sign up for a developer account, always read the complete Terms of Service (ToS) and privacy policy. Crucially, this is different from a company website’s terms and policies.

當(dāng)您想要注冊(cè)開發(fā)者帳戶時(shí)，請(qǐng)務(wù)必閱讀完整的服務(wù)條款(ToS)和隱私權(quán)政策。 至關(guān)重要的是，這與公司網(wǎng)站的條款和政策不同。

In particular, be vigilant for information about how the data you provide as input is going to be handled. There is a service called Polisis that helps you to compare policies from different service providers (it’s not perfect, but it is still helpful).

尤其要警惕有關(guān)如何處理您作為輸入提供的數(shù)據(jù)的信息。 有一項(xiàng)名為Polisis的服務(wù)可以幫助您比較不同服務(wù)提供商的策略(雖然不完美，但仍然很有用)。

Read the developer’s privacy policy and product-specific policy to understand what data from you (as an account holder) the company collects, how they protect them? Do they encrypt the data at rest and in transit? Is the data they collect personally identifiable? Do they define what they mean by metadata? Do they collect the data you provide as an input to the service? Do they retain it? For how long? Do they keep the log files?

閱讀開發(fā)者的隱私權(quán)政策和特定于產(chǎn)品的政策，以了解公司從您(作為帳戶持有人)那里收集了哪些數(shù)據(jù)，它們?nèi)绾伪Ｗo(hù)它們？ 他們是否對(duì)靜態(tài)和傳輸中的數(shù)據(jù)進(jìn)行加密？ 他們收集的數(shù)據(jù)可以個(gè)人識(shí)別嗎？ 它們是否定義了元數(shù)據(jù)的含義？ 他們是否收集您提供的數(shù)據(jù)作為服務(wù)的輸入？ 他們保留嗎？ 多長(zhǎng)時(shí)間？ 他們是否保留日志文件？

Here’s a comparison between the policies of the two services. (For the rest of this post, my comparisons between the details of IBM Tone Analyzer and ParallelDots will appear in gray text boxes like the one below, featuring summaries of what I found in their posted policies and documentation).

這是兩種服務(wù)的策略之間的比較。 (對(duì)于本文的其余部分，我在IBM Tone Analyzer和ParallelDots的詳細(xì)信息之間進(jìn)行的比較將顯示在下面的灰色文本框中，其中包含我在其發(fā)布的策略和文檔中所得到的摘要)。

IBM Tone Analyzer: When you want to create a developer account, IBM points you to their general privacy policy that lists everything from website visit to using cloud services. There are some vague statement such as:? "IBM may also share your personal information with selected partners to help us provide you ..." Who are their partners though?!? Or "We will not retain personal information longer than necessary to fulfill the purposes for which it is processed." What is "longer than necessary?"If you want specific information about data collection and retention via Tone Analyzer API go to the product document page. Some relevant information includes:? "Request logging is disabled for the Tone Analyzer service.the service does not log or retain data from requests and responses."? The service "processes but does not store users' data. Users of the Tone Analyzer service do not need to take any action to identify, protect, or delete their data for this service."ParallelDots: The website says that ParallelDots “protects your data and follow the GDPR compliance guidelines to the last word.” But it doesn’t go further? Which data? Metadata or developers’ information or users’ data? ? ParallelDots' ToS says "you may not access the services for purposes of monitoring their availability, performance or functionality, or for any other benchmarking or competitive purposes" This is bizarre to me, does that mean I broke their Tos?!

文獻(xiàn)資料 (Documentation)

If a company has already provided documentation such as Model Card for Model Reporting for that specific API, read it before using the service. If not, good luck on finding such important information! Look at the API’s documents and dig in for information about API security, background research. papers, training data, architecture and algorithms, evaluation metrics, and recommended use and not-to-use cases.

如果公司已經(jīng)提供了該特定API的文檔，例如用于模型報(bào)告的模型卡 ，請(qǐng)?jiān)谑褂迷摲?wù)之前先閱讀該文檔。 如果沒有，請(qǐng)找到這些重要信息，祝您好運(yùn)！ 查看API的文檔，并深入了解有關(guān)API安全性和背景研究的信息。 論文，培訓(xùn)數(shù)據(jù)，體系結(jié)構(gòu)和算法，評(píng)估指標(biāo)以及推薦使用和不使用的案例。

🔐 API Security. During the past few years, there have been numerous examples of data breaches via the use of insecure APIs. It’s an API provider’s responsibility to be able to detect security vulnerabilities, identify suspicious requests, provide encrypted traffic, and traffic monitoring methods. Make sure an API provider has already put these security practices in place — and read more about APIs security here.

🔐API 安全性。 在過(guò)去的幾年中，通過(guò)使用不安全的API出現(xiàn)了許多數(shù)據(jù)泄露的例子。 API提供者的職責(zé)是能夠檢測(cè)安全漏洞，識(shí)別可疑請(qǐng)求，提供加密的流量以及流量監(jiān)控方法。 確保API提供者已經(jīng)實(shí)施了這些安全性實(shí)踐-并在此處閱讀有關(guān)API安全性的更多信息。

Here’s another comparison, this time comparing API security:

這是另一個(gè)比較，這次比較的是API安全性：

IBM Tone Analyzer: ? IBM suggests developers use IBM Cloud Activity Tracker with LogDN to monitor the activity of an IBM Cloud account and investigate abnormal activity.? The service also requires a strong password and sends you a verification code to confirm your developer account.ParallelDots: There is no information about API security in the API document page. However they mentioned that they only provide encrypted access to premium content. ? For registration, developers are not required to set a strong password, however, ParallelDots sends you a verification email to confirm your account.

🌏 Accurate and Precise… but for who? In the example of “Fair & Lovely,” language plays an important role. English-only tweets don’t provide an accurate understanding of discussions around the product because it’s not one restricted to a single language.

🌏 準(zhǔn)確而精確……但是對(duì)于誰(shuí)呢？ 在“公平與可愛”的示例中，語(yǔ)言扮演著重要角色。 僅限英文的推文無(wú)法提供對(duì)產(chǎn)品討論的準(zhǔn)確理解，因?yàn)樗粌H限于一種語(yǔ)言。

Check the API to see if it support other languages. If so, what is the accuracy rate for different languages? Service providers often say they support multiple languages, but don’t provide broken down details about accuracy and other evaluation metrics for each language. Dig in the API document, background research pages, and try to find metrics for different sub-categories.

檢查API以查看其是否支持其他語(yǔ)言。 如果是這樣， 不同語(yǔ)言的準(zhǔn)確率是多少？ 服務(wù)提供商經(jīng)常說(shuō)他們支持多種語(yǔ)言，但沒有提供每種語(yǔ)言的準(zhǔn)確性和其他評(píng)估指標(biāo)的詳細(xì)信息。 在API文檔，背景研究頁(yè)面中進(jìn)行挖掘，并嘗試查找不同子類別的指標(biāo)。

In our case, here’s what I found:

在我們的案例中，這是我發(fā)現(xiàn)的內(nèi)容：

IBM Tone Analyzer: The company listed 11 supported languages. However, there is no breakdown information about accuracy or other evaluation metrics based on different languages.ParallelDots: The company listed 14 supported languages. However, there is no information about accuracy or other evaluation metrics based on different languages.

??Suggested (Not) Use Cases. Companies also provide guidance about the suggested use of their services, but sometimes the use case can be dangerous or unethical. Companies need to be transparent about the cases in which developers should not use their services.

?? 建議(非)用例。 公司還提供有關(guān)建議使用其服務(wù)的指導(dǎo)，但有時(shí)用例可能是危險(xiǎn)的或不道德的。 公司必須對(duì)開發(fā)人員不應(yīng)使用其服務(wù)的情況保持透明。

IBM Tone Analyzer: Tone Analyzer use cases according to the document page include predicting customer satisfaction in support forums; predicting customer satisfaction in Twitter responses; predicting online dating matches; Predicting TED Talk applause. There is no indication about Not-to-Use Cases.ParallelDots: There are two suggested use cases: “target[ing] detractors to improve service to them” and “brand-watching.” There is no indication about Not-to-Use Cases.

?? Fairness Practices. In the past couple of years, researchers and practitioners have raised awareness about discriminatory outcomes of machine learning systems. They’ve provided numerous toolkits to help companies assess the human rights implications of their tools and be transparent about potential social risks. I keep track of different initiatives, papers, toolkits here.

Fair? 公平實(shí)踐。 在過(guò)去的幾年中，研究人員和從業(yè)人員提高了對(duì)機(jī)器學(xué)習(xí)系統(tǒng)歧視性結(jié)果的認(rèn)識(shí)。 他們提供了許多工具包，以幫助公司評(píng)估其工具對(duì)人權(quán)的影響，并對(duì)潛在的社會(huì)風(fēng)險(xiǎn)保持透明。 我在這里跟蹤不同的計(jì)劃，論文和工具包。

But how many companies provide that information for their specific ML APIs?

但是，有多少公司為其特定的ML API提供該信息？

IBM Tone Analyzer: IBM provides information about background research, data collection process (twitter data), and data annotation method. However there is no mention of potential discrminatory outcomes and no breakdown information about demographics and measurement based on different sub-groups (language, gender, age, etc.)? Fun fact: IBM Research is one of the pioneers in providing fairness and explanability toolkits (check out IBM 360). They also proposed using FactSheets for every ML model to show the origin of training datasets, model specifications, and use cases. But when it comes to their own model, you rarely find such information on their product pages! This reminded me of the great piece of poem from Nizami, basically meaning first fix your own flaws before being too critical of others:??? ???? ???? ? ????? ???? ???? ??? ?? ??????? ????ParallelDots: I found no information about fairness practices.

🛠 Maintenance and Updates

🛠 維護(hù)和更新

IBM Tone Analyzer: The company frequently update the service and provides information about the updates. However, in some updates there are generic sentecs including "The service was also updated for internal changes and improvements." What are those internal changes and improvements?ParallelDots: I couldn't find information about updates and maintenance.

💬 Developers Community. Communities of developers(via Slack Workspace, Stack Overflow, GitHub, etc) help share feedback, interact with themselves and service providers, and raise issues around privacy, security, fairness, explainability about a certain product and in a specific domain.

💬開發(fā)者社區(qū)。 開發(fā)者社區(qū)(通過(guò)Slack Workspace，Stack Overflow，GitHub等)有助于共享反饋，與他們自己和服務(wù)提供商進(jìn)行交互，并引發(fā)有關(guān)特定產(chǎn)品和特定領(lǐng)域的隱私，安全性，公平性，可解釋性的問題。

IBM Tone Analyzer: IBM Watson provides Slack workspace (there is no dedicated channel for ethical uses, however) and a Stack Overflow developers community. The Github page for the Tone Analyzer is here. ParallelDots: The company has a GitHub page.

推薦建議 (Recommendations)

給開發(fā)者 (To developers)

• Don’t use machine learning APIs blindly, especially if they are black boxes. In addition to criteria such as cost, speed, and accuracy — as marketed by a service provider — consider criteria related to fairness, privacy, security, and transparency.

  不要盲目使用機(jī)器學(xué)習(xí)API，尤其是當(dāng)它們是黑匣子時(shí)。 除服務(wù)提供商所銷售的成本，速度和準(zhǔn)確性等標(biāo)準(zhǔn)外，還要考慮與公平性，私密性，安全性和透明性相關(guān)的標(biāo)準(zhǔn)。

• If it’s not documented, reach out to service providers and ask them whether they have conducted any fairness audits. It’s their responsibility to publish this information online or walk you through it. Use your buying power, they’ll listen!

  如果沒有記錄，請(qǐng)與服務(wù)提供商聯(lián)系，詢問他們是否進(jìn)行了任何公平性審核。 他們有責(zé)任在網(wǎng)上發(fā)布此信息或引導(dǎo)您進(jìn)行逐步了解。 使用您的購(gòu)買力，他們會(huì)聽的！

• Think about the domain for which you will be using the tool. Who might be affected disproportionally by the outcome of integrating a given ML API with your product? Think about gender, race, religion, age, language, accent, country, socio-economic status (read this to learn more about vulnerable groups who are protected under human rights conventions). I keep track of different ML assessment tools here; you might find them helpful in your assessment process.

  考慮您將使用該工具的域。 誰(shuí)可能會(huì)受到結(jié)果的不成比例的影響 將給定的ML API與您的產(chǎn)品集成？ 想想性別，種族，宗教，年齡，語(yǔ)言，口音，國(guó)家，社會(huì)經(jīng)濟(jì)地位(讀這更多地了解誰(shuí)是人權(quán)公約所保護(hù)的弱勢(shì)群體)。 我在這里跟蹤不同的機(jī)器學(xué)習(xí)評(píng)估工具； 您可能會(huì)發(fā)現(xiàn)它們對(duì)您的評(píng)估過(guò)程有幫助。

• Try to find benchmark datasets that relate to discriminatory outcomes of ML projects (Equity Evaluation Corpus is an example of a benchmark dataset used to examine biases in sentiment analysis systems). Reach out to people who are involved in creating such benchmarks and ask them for help to scrutinize the API in your specific domain. Check out FAccT conference directory for finding people who work on these issues.

  嘗試查找與ML項(xiàng)目的歧視性結(jié)果相關(guān)的基準(zhǔn)數(shù)據(jù)集 (股權(quán)評(píng)估語(yǔ)料庫(kù)是用于檢查情緒分析系統(tǒng)中偏差的基準(zhǔn)數(shù)據(jù)集的示例)。 與參與創(chuàng)建此類基準(zhǔn)測(cè)試的人員聯(lián)系，并請(qǐng)他們尋求幫助來(lái)仔細(xì)檢查您特定域中的API。 查看FAccT會(huì)議目錄，查找從事這些問題的人員。

• When you suspect something is ethically wrong with an API service in your specific domain, share it with other developers by opening an issue on that service’s GitHub page, Stack Overflow, or developers’ community pages. Almost all service providers have these platforms for the developers to share their issues. Service providers might say it is impossible to test and audit their tools for every single domain because their service is a general-purpose tool. But you can inform them about ethical issues you face within a specific domain for your use cases. By providing public information you can also help other developers who might want to use that service!

  如果您懷疑特定域中的API服務(wù)在道德上有問題，請(qǐng)?jiān)谠摲?wù)的GitHub頁(yè)面，Stack Overflow或開發(fā)者社區(qū)頁(yè)面上打開一個(gè)問題，與其他開發(fā)者共享它。 幾乎所有服務(wù)提供商都擁有這些平臺(tái)，供開發(fā)人員共享他們的問題。 服務(wù)提供商可能會(huì)說(shuō)不可能針對(duì)每個(gè)域測(cè)試和審核他們的工具，因?yàn)樗麄兊姆?wù)是通用工具。 但是您可以告知他們有關(guān)用例的特定領(lǐng)域內(nèi)您面臨的道德問題。 通過(guò)提供公共信息，您還可以幫助其他想要使用該服務(wù)的開發(fā)人員！

• If you integrate third party ML APIs in your product, mention it in your product’s privacy policy and terms of services. Don’t minimize it to a sentence saying “we use other parties’ services.” Include information about those third-party services — in this case, an ML service provider. Be transparent about how users’ data are handled because of that specific third-party relationship.

  如果您在產(chǎn)品中集成了第三方ML API，請(qǐng)?jiān)诋a(chǎn)品的隱私權(quán)政策和服務(wù)條款中提及。 不要將其最小化為“我們使用其他方的服務(wù)”。 包括有關(guān)這些第三方服務(wù)的信息，在本例中為ML服務(wù)提供商。 由于特定的第三方關(guān)系，因此對(duì)于用戶數(shù)據(jù)的處理方式應(yīng)保持透明。

到機(jī)器學(xué)習(xí)API服務(wù)提供商 (To Machine Learning APIs Service providers)

The focus of this post was not on service providers but on third-party developers. However, to highlight some of the service providers’ responsibilities when it comes to informing developers, I would say:

這篇文章的重點(diǎn)不是服務(wù)提供商，而是第三方開發(fā)人員。 但是，要強(qiáng)調(diào)通知服務(wù)開發(fā)人員時(shí)服務(wù)提供商的某些職責(zé)，我要說(shuō)：

• Document and be transparent! Don’t bury fairness criteria in a 500-page document. Use more visible and friendly user Interfaces to guide developers to read about fairness and privacy criteria before signing up to use your service.

  文檔并保持透明！ 不要將公平標(biāo)準(zhǔn)掩埋在500頁(yè)的文件中。 使用更直觀，更友好的用戶界面來(lái)指導(dǎo)開發(fā)人員在注冊(cè)使用您的服務(wù)之前先了解公平性和隱私權(quán)標(biāo)準(zhǔn) 。

• Add issues related to the fairness, security, and privacy of your own API services in your developers’ portals and community pages. Let developers discuss these issues within those portals (e.g. creating a dedicated Slack channel within developers’ Workspace) and encourage developers to share their experience dealing with fairness, privacy, and security while using your services (IBM 360 Slack Channel, Salesforce UI warnings are good examples). Don’t only showcase “successful” uses and positive testimonial on your marketplace page!

  在開發(fā)人員的門戶和社區(qū)頁(yè)面中添加與您自己的API服務(wù)的公平性，安全性和隱私性有關(guān)的問題。 讓開發(fā)人員在這些門戶中討論這些問題(例如，在開發(fā)人員的工作區(qū)中創(chuàng)建專用的Slack通道)，并鼓勵(lì)開發(fā)人員在使用服務(wù)時(shí)分享他們?cè)谔幚砉叫?#xff0c;隱私和安全性方面的經(jīng)驗(yàn)( IBM 360 Slack Channel ， Salesforce UI警告是不錯(cuò)的選擇)例子)。 不僅要在您的市場(chǎng)頁(yè)面上展示“成功”的用法和正面的評(píng)價(jià)！

• Each tier of developer account (free, standard, premium) brings different levels of responsibilities for you. Develop privacy-protective practices to monitor potential misuses of your services. This paper offers some feasible solutions: Monitoring Misuse for Accountable ‘Artificial Intelligence as a Service.’

  每層開發(fā)人員帳戶(免費(fèi)，標(biāo)準(zhǔn)，高級(jí))為您帶來(lái)不同級(jí)別的責(zé)任。 制定保護(hù)隱私的做法，以監(jiān)視對(duì)服務(wù)的潛在濫用。 本文提供了一些可行的解決方案： 監(jiān)視對(duì)負(fù)責(zé)任的“人工智能即服務(wù) ”的濫用 。

致ML審核員和人權(quán)與技術(shù)從業(yè)人員 (To ML Auditors and Human Rights & Technology Practitioners)

We hear a lot about democratizing building blocks of digital technologies; also we hear a lot about the interoperability of digital services. These are all good. But they bring new kinds of interactions, data flows, and data ownership matters.

我們聽到了很多關(guān)于使數(shù)字技術(shù)的基礎(chǔ)架構(gòu)民主化的信息。 我們也聽到了很多有關(guān)數(shù)字服務(wù)互操作性的信息。 這些都很好。 但是它們帶來(lái)了新型的交互，數(shù)據(jù)流和數(shù)據(jù)所有權(quán)問題。

The purpose of this blog post has been to raise awareness about the importance of these often-overlooked relationships and actors. It’s for developers to think about their responsibilities before integrating these APIs into their services. But it’s also for human rights practitioners, privacy advocates, ethical tech researchers to dissect these issues and find practical guidance to help smaller actors in our data-driven world.

這篇博客的目的是提高人們對(duì)這些經(jīng)常被忽視的關(guān)系和參與者的重要性的認(rèn)識(shí)。 這是讓開發(fā)人員在將這些API集成到其服務(wù)中之前考慮其職責(zé)的。 但是，對(duì)于人權(quán)從業(yè)者，隱私倡導(dǎo)者，道德技術(shù)研究人員來(lái)說(shuō)，也要剖析這些問題并找到實(shí)用的指南，以幫助我們數(shù)據(jù)驅(qū)動(dòng)的世界中的小規(guī)模參與者。

• Scrutinize third-party relationships when you audit a certain product/service and try to assess potential adverse human rights impacts of it. Both groups play a role when things go wrong. Going forward, let’s pay more attention to such things as supply chain issues, and carefully examine the role and responsibilities of different actors of the digital technologies ecosystem.

  當(dāng)您審核某種產(chǎn)品/服務(wù)并嘗試評(píng)估其潛在的不利人權(quán)影響時(shí)，請(qǐng)仔細(xì)檢查第三方關(guān)系。 出現(xiàn)問題時(shí)，這兩個(gè)小組都將發(fā)揮作用。 展望未來(lái)，讓我們更加關(guān)注供應(yīng)鏈問題，并仔細(xì)檢查數(shù)字技術(shù)生態(tài)系統(tǒng)不同參與者的角色和責(zé)任。

I work on issues at the intersection of technology and human rights. If you are a developer and have been thinking about ways to choose and use building blocks of your product more responsibly please reach out to me. I would be happy to speak with you: rpakzad@taraazresearch.org.

我致力于技術(shù)與人權(quán)的交匯處。 如果您是開發(fā)人員，并且一直在考慮更負(fù)責(zé)任地選擇和使用產(chǎn)品構(gòu)建塊的方法，請(qǐng)與我聯(lián)系。 我很高興與您交談：rpakzad@taraazresearch.org。

If you are interested in tech & human rights check out Taraaz’s website and sign up for our newsletter.

如果您對(duì)技術(shù)和人權(quán)感興趣，請(qǐng)?jiān)L問 Taraaz的 網(wǎng)站并注冊(cè)我們的 新聞通訊 。

翻譯自: https://medium.com/taraaz/developers-choose-wisely-a-guide-for-responsible-use-of-machine-learning-apis-e006e4263cae

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