面部識(shí)別軟件是一種強(qiáng)大的技術(shù)，對(duì)公民自由構(gòu)成嚴(yán)重的威脅。與此同時(shí)，面部識(shí)別也是一個(gè)蓬勃發(fā)展的行業(yè)。如今，數(shù)十家初創(chuàng)企業(yè)和科技巨頭的服務(wù)銷售對(duì)象包括酒店、零售店，甚至學(xué)校和夏令營(yíng)。新算法能夠比五年前更精確地識(shí)別面部，相關(guān)業(yè)務(wù)也蒸蒸日上。為了改進(jìn)算法，多家公司經(jīng)常在未經(jīng)許可的情況下利用數(shù)十億張面部照片訓(xùn)練。事實(shí)上，你自己的臉就可能在面部識(shí)別公司利用的“訓(xùn)練庫(kù)”里，也可能在公司客戶的數(shù)據(jù)庫(kù)里。

如果得知一些公司如何獲取面部照片，消費(fèi)者可能會(huì)吃一驚。舉例來(lái)說(shuō)，至少三個(gè)案例顯示，科技公司通過(guò)人們手機(jī)上的照片應(yīng)用程序弄到了數(shù)百萬(wàn)張圖片。目前，對(duì)面部識(shí)別軟件幾乎沒(méi)有法律限制，所以人們也沒(méi)有什么辦法阻止公司偷偷使用自己的面部數(shù)據(jù)。

2018年，華盛頓附近的機(jī)場(chǎng)廊橋里，乘客們匆匆走出，一臺(tái)攝像機(jī)在附近拍攝人們的臉。事實(shí)上，飛機(jī)和乘客都不是真的。整個(gè)場(chǎng)景只是美國(guó)國(guó)家科學(xué)技術(shù)研究所（NIST）用來(lái)演示“在自然環(huán)境下”裝置如何收集面部圖像。收集到的照片也成為NIST反復(fù)提及的競(jìng)爭(zhēng)優(yōu)勢(shì)，全球各地的公司都可以使用該裝置測(cè)試自家的面部識(shí)別軟件。

Facial recognition software is a powerful technology that poses serious threats to civil liberties. It’s also a booming business. Today, dozens of startups and tech giants are selling face recognition services to hotels, retail stores—even schools and summer camps. The business is flourishing thanks to new algorithms that can identify people with far more precision than even five years ago. In order to improve these algorithms, companies trained them on billions of faces—often without asking anyone’s permission. Indeed, chances are good that your own face is part of a “training set” used by a facial recognition firm or part of a company’s customer database.

Consumers may be surprised at some of the tactics companies have used to harvest their faces. In at least three cases, for instance, firms have obtained millions of images by harvesting them via photo apps on people’s phones. For now, there are few legal restrictions on facial recognition software, meaning there is little people can do to stop companies using their face in this manner.

In 2018, a camera collected the faces of passengers as they hurried down an airport jetway near Washington, D.C. In reality, neither the jetway nor the passengers were real; the entire structure was merely a set for the National Institute for Science and Technology (NIST) to demonstrate how it could collect faces “in the wild.” The faces would become part of a recurring NIST competition that invites companies across the globe to test their facial recognition software.

用應(yīng)用程序收集面部圖片

公司從哪可以獲得數(shù)百萬(wàn)圖像來(lái)訓(xùn)練軟件？來(lái)源之一是警方的嫌疑人照片庫(kù)，庫(kù)可以從機(jī)構(gòu)公開(kāi)獲取，也有私人公司出售。例如，總部位于加利福尼亞州的Vigilant Solutions便提供1500萬(wàn)張面部圖像的數(shù)據(jù)庫(kù)，也是該公司提供面部識(shí)別“解決方案”的一部分。

然而，一些初創(chuàng)公司發(fā)現(xiàn)了更好的面部圖像來(lái)源，即個(gè)人相冊(cè)應(yīng)用。類似應(yīng)用可以協(xié)助存儲(chǔ)個(gè)人手機(jī)上的照片，通常包含同一個(gè)人的各種姿勢(shì)，以及在多種環(huán)境下的照片，為培訓(xùn)算法提供了豐富的數(shù)據(jù)來(lái)源。

“我們的用戶照片包括同一個(gè)人在數(shù)千種不同場(chǎng)景里的樣子。站在陰影里，戴帽子，各種各樣……”舊金山面部識(shí)別公司Ever AI 的首席執(zhí)行官道格·阿萊說(shuō)，2012年該公司成立時(shí)名叫EverRoll，主要幫用戶管理大量的相冊(cè)。

Ever AI從科斯拉風(fēng)險(xiǎn)投資公司和硅谷其他風(fēng)投手里融到了2900萬(wàn)美元，也參加了最近NIST組織的面部識(shí)別競(jìng)賽，在“嫌疑犯照片”類別中排名第二，在“自然環(huán)境中面部”類別排名第三。阿萊認(rèn)為成功的原因是擁有巨大的照片數(shù)據(jù)庫(kù)，Ever AI估計(jì)有130億張照片。

Ever AI在剛成立時(shí)只是個(gè)照片應(yīng)用程序，其市場(chǎng)推廣策略非常激進(jìn)，引起了爭(zhēng)議，并導(dǎo)致2016年蘋(píng)果把它從應(yīng)用商店下架。該應(yīng)用最過(guò)分的是誘導(dǎo)用戶向所有電話聯(lián)系人發(fā)送推廣鏈接，該策略在硅谷被稱為“增長(zhǎng)黑客”。用戶還指責(zé)應(yīng)用吞掉自己的數(shù)據(jù)。

“剛下載應(yīng)用，第一件事就是收集所有電話號(hào)碼，立即給所有人發(fā)信息……然后就開(kāi)始收集所有照片并上傳到云端。” 2015年得克薩斯州肖像工作室老板格雷格·米勒在Facebook上發(fā)表的評(píng)論里寫(xiě)道。

四年后，米勒沮喪地發(fā)現(xiàn)曾經(jīng)叫Everroll的應(yīng)用程序還是存有自己的照片，而且搖身一變成了面部識(shí)別公司。

“不，我不知道（應(yīng)用在收集照片），我一點(diǎn)也不同意?！?米勒對(duì)《財(cái)富》雜志說(shuō)，“種種現(xiàn)狀說(shuō)明了一個(gè)真正的問(wèn)題。再也沒(méi)有隱私可言，想到這我真是不寒而栗?！?/p>

Ever AI的首席執(zhí)行官阿萊表示，公司數(shù)據(jù)庫(kù)里不會(huì)分享用戶個(gè)人信息，只用照片訓(xùn)練軟件。他補(bǔ)充說(shuō)，該公司類似社交媒體網(wǎng)絡(luò)，人們可以選擇退出。阿萊也否認(rèn)Ever AI從一開(kāi)始就想做面部識(shí)別，退出現(xiàn)已關(guān)閉的照片應(yīng)用只是商業(yè)決策。目前，Ever AI的客戶利用軟件從事一系列活動(dòng)，包括企業(yè)ID管理、零售、電信和執(zhí)法等。

Everroll并不是唯一從照片應(yīng)用轉(zhuǎn)型面部識(shí)別的公司。另一個(gè)例子是總部位于舊金山的Orbeus，2016被亞馬遜悄然收購(gòu)，該公司曾經(jīng)開(kāi)發(fā)出很受歡迎的照片管理工具，叫PhotoTime。

一位長(zhǎng)期在Orbeus工作的員工表示，該公司的人工智能技術(shù)領(lǐng)先，又擁有大量公共環(huán)境下的用戶照片，所以成為理想的收購(gòu)目標(biāo)。

“亞馬遜缺的就是這些，所以全盤(pán)收購(gòu)，然后關(guān)閉了應(yīng)用程序?！痹搯T工表示，他拒絕透露身份，因?yàn)橛斜Ｃ軈f(xié)議。

如今，雖然亞馬遜仍在繼續(xù)銷售另一款由Orbeus開(kāi)發(fā)名為Rekognition的產(chǎn)品，但當(dāng)初的PhotoTime應(yīng)用已經(jīng)不復(fù)存在。Rekognition也是一款面部識(shí)別軟件，供執(zhí)法部門和其他組織使用。

至于Orbeus的照片應(yīng)用如何用于訓(xùn)練Rekognition，亞馬遜拒絕透露細(xì)節(jié)，只說(shuō)從各種來(lái)源獲取數(shù)據(jù)供面部識(shí)別在內(nèi)的人工智能項(xiàng)目使用。該公司補(bǔ)充說(shuō)，不會(huì)使用Prime會(huì)員的照片訓(xùn)練算法。

另一家使用消費(fèi)者照片應(yīng)用訓(xùn)練面部識(shí)別算法的是Real Networks。該公司總部位于西雅圖，20世紀(jì)90年代曾經(jīng)以在線視頻播放器而聞名，現(xiàn)在專門開(kāi)發(fā)能夠在學(xué)校里識(shí)別孩子面部的軟件。該公司還提供一款針對(duì)家庭叫RealTimes的智能手機(jī)應(yīng)用，一位評(píng)論人士表示該應(yīng)用正是獲取面部數(shù)據(jù)的接口。

“在該應(yīng)用里，用戶可用自己照片制作視頻幻燈片。想象一下，媽媽做視頻幻燈片發(fā)送給奶奶，相關(guān)圖片被用來(lái)訓(xùn)練數(shù)據(jù)識(shí)別下一代的臉。太可怕了?！眴讨味卮髮W(xué)教授克萊爾·加維說(shuō)，他發(fā)表了一篇關(guān)于面部識(shí)別技術(shù)頗有影響力的報(bào)告。

Real Networks證實(shí)了照片應(yīng)用有助于改進(jìn)其面部識(shí)別工具，但補(bǔ)充說(shuō)也使用了其他數(shù)據(jù)源。

在公司使用照片應(yīng)用獲取面部照片訓(xùn)練數(shù)據(jù)的各種案例里，都沒(méi)有明確征求消費(fèi)者的許可。相反，各公司似乎通過(guò)服務(wù)協(xié)議獲得了法律許可。

然而，比起其他一些面部識(shí)別公司，利用照片應(yīng)用獲取數(shù)據(jù)的公司已經(jīng)算得上很努力。在NIST負(fù)責(zé)面部識(shí)別競(jìng)賽的帕特里克·格洛特說(shuō)，面部識(shí)別公司通常會(huì)編寫(xiě)一些程序，從SmugMug或Tumblr之類網(wǎng)站直接“抓取”圖片。此類情況下，公司更沒(méi)有必要向面部照片被用于訓(xùn)練數(shù)據(jù)的用戶征求同意。

最近，美國(guó)國(guó)家廣播公司（NBC）的一篇報(bào)道重點(diǎn)批評(píng)了類似的“自助抓取”做法，道詳細(xì)描述了IBM如何從照片共享網(wǎng)站Flickr中抓走超過(guò)100萬(wàn)張面部照片，用作人工智能研究。（IBM研究部門負(fù)責(zé)人工智能技術(shù)的約翰·史密斯告訴NBC，該公司將努力“保護(hù)個(gè)人隱私”，只要有人希望刪除自己的數(shù)據(jù)，都會(huì)積極配合。）

面對(duì)種種情況，人們不禁產(chǎn)生疑問(wèn)，各公司如何保護(hù)收集的面部數(shù)據(jù)，政府是否應(yīng)該加強(qiáng)監(jiān)督。隨著面部識(shí)別在社會(huì)上的更多領(lǐng)域里應(yīng)用，推動(dòng)大小公司業(yè)務(wù)發(fā)展，相關(guān)問(wèn)題也更加緊迫。

An App for That

Where might a company obtain millions of images to train its software? One source has been databases of police mug shots, which are publicly available from state agencies and are also for sale by private companies. California-based Vigilant Solutions, for instance, offers a collection of 15 million faces as part of its facial recognition “solution.”

Some startups, however, have found an even better source of faces: personal photo album apps. These apps, which compile photos stored on a person’s phone, typically contain multiple images of the same person in a wide variety of poses and situations—a rich source of training data.

“We have consumers who tag the same person in thousands of different scenarios. Standing in the shadows, with hats-on, you name it,” says Doug Aley, the CEO of Ever AI, a San Francisco facial recognition startup that launched in 2012 as EverRoll, an app to help consumers manage their bulging photo collections.

Ever AI, which has raised $29 million from Khosla Ventures and other Silicon Valley venture capital firms, entered NIST’s most recent facial recognition competition, and placed second in the contest’s “Mugshots” category and third in “Faces in the Wild.” Aley credits the success to the company’s immense photo database, which Ever AI estimates to number 13 billion images.

In its earlier days, when Ever AI was a mere photo app, its aggressive marketing practices created controversy and temporarily led Apple to ban EverRoll from the App Store in 2016. Notably, the app induced users to send promotional links to all of their phone contacts, a tactic known as “growth hacking” in Silicon Valley parlance. Users also accused it of gobbling their data.

“The first thing it does even as it is installing is to harvest all your phone numbers and immediately message everybody… This thing then starts to pull all your photos and put them into the cloud,” wrote Greg Miller, a Texas-based portrait studio owner, in a 2015 Facebook review.

Four years later, Miller was dismayed to discover that the app once known as EverRoll still had his photos, and that it was now a facial recognition company.

“No, I was not aware of that, and I don’t agree with it one bit,” Miller tells Fortune. “All of this being tracked is a real problem. Nothing is private anymore and that just scares the hell out of me.”

Aley, the Ever AI CEO, says the company doesn’t share identifying information about individuals in its database, and only uses the photos to train its software. He added the company is akin to a social media network from which people can opt out. Aley also denied that Ever AI had intended to become a facial recognition company from the get-go, saying the move away from the now-shuttered photo app was a business decision. Currently, Ever AI’s customers are using it for a range of activities, including corporate ID management, retail, telecommunications, and law enforcement.

EverRoll is not the only facial recognition company that once offered a consumer photo app. Another example is Orbeus, a San Francisco-based startup quietly acquired by Amazon in 2016, which once offered a popular picture organizer called PhotoTime.

According to a longtime Orbeus employee, the startup’s A.I. technology and its large collection of photos with people in public settings made it an appealing acquisition target.

“Amazon was looking for that capability. They acquired everything, then shut down the app,” says the employee, who declined to be identified, citing non-disclosure agreements.

Today the PhotoTime app no longer exists, though Amazon continues to sell another Orbeus product known as Rekognition. The product is a type of facial recognition software used by law enforcement and other organizations.

Amazon declined to provide details about the extent to which Orbeus’s photo app was used to train the Rekognition software, only stating it obtains data for its A.I. projects, including facial recognition, from a variety of sources. The company added it does not use its customers’ Prime photo service to train its algorithms.

Another company that uses a consumer photo app to train its facial recognition algorithm is Real Networks. The Seattle-based company, once known for its 1990s-era online video player, today specializes in software that can recognize children’s faces in schools. At the same time, it offers a smartphone app aimed at families called RealTimes, which one critic says has served as a pretext to obtain facial data.

“The app allows users to make video slideshows of their own photos. Imagine mom putting together a video slide show to send to grandma, and those images being used to train a dataset to use on young faces. It’s pretty horrible,” says Clare Garvie, a Georgetown University professor who published an influential report on facial recognition technology.

Real Networks confirmed the photo app helps improve its facial recognition tool, but added that it uses additional data sources for the purpose.

In all of these cases where companies used a photo app to harvest faces for training data, they didn’t ask for consumers’ explicit permission. Instead, the firms appear to have obtained legal consent through their terms of service agreements.

This is, however, more than what some other facial recognition companies have done. According to Patrick Grother, who runs the face competitions at NIST, it’s common for facial recognition companies to write programs that “scrape” pictures from websites like SmugMug or Tumblr. In these cases, there is not even a pretext of consent from those whose faces end up in training sets.

This “help yourself” approach was underscored by a recent NBC News report detailing how IBM siphoned more than one million faces from the photo sharing site Flickr as part of the company’s artificial intelligence research. (John Smith, who oversees AI technology for IBM’s research division, told NBC News that the company was committed to “protecting the privacy of individuals” and would work with those who sought removal from the dataset.)

All of this raises questions about what companies are doing to safeguard the facial data they collect, and whether governments should provide more oversight. The issues will only be become more pressing as facial recognition spreads to more areas of society, and powers the business of companies large and small.

從商店到學(xué)校

面部識(shí)別軟件并不是新鮮事物。20世紀(jì)80年代美國(guó)數(shù)學(xué)家開(kāi)始將人臉定義為一系列數(shù)值，用概率模型尋找匹配時(shí)，該技術(shù)的原始版本就已經(jīng)存在。在2001年的橄欖球冠軍賽超級(jí)碗上，佛羅里達(dá)州坦帕市的保安人員便已應(yīng)用，賭場(chǎng)則已經(jīng)使用多年。但過(guò)去幾年里情況出現(xiàn)了變化。

“面部識(shí)別正經(jīng)歷一場(chǎng)革命。” NIST的格洛瑟表示。他補(bǔ)充說(shuō)，快速閃過(guò)或像素差的圖像提升最大?！盎A(chǔ)技術(shù)已經(jīng)改變。舊技術(shù)已經(jīng)被新一代算法取代，而且非常有效?！?/p>

面部識(shí)別的革命得益于廣泛改變?nèi)斯ぶ悄茴I(lǐng)域的兩大因素。第一項(xiàng)因素是新興的深度學(xué)習(xí)科學(xué)，模仿人腦的模式識(shí)別系統(tǒng)。第二項(xiàng)因素則是史無(wú)前例的海量數(shù)據(jù)，借助云計(jì)算可以實(shí)現(xiàn)低成本存儲(chǔ)解析數(shù)據(jù)。

毫無(wú)疑問(wèn)，最先充分利用各項(xiàng)新技術(shù)的公司是谷歌和Facebook。2014年，社交網(wǎng)絡(luò)Facebook推出了名叫DeepFace的項(xiàng)目，可以識(shí)別兩張臉是否屬于同一個(gè)人，準(zhǔn)確率為97.25%，與人類水平相當(dāng)。安全公司Gemalto的數(shù)據(jù)顯示，一年后谷歌的facenet程序準(zhǔn)確率達(dá)100%。

如今，谷歌、Facebook和微軟等其他科技巨頭在面部識(shí)別方面都處于領(lǐng)先地位，主要原因是巨頭擁有龐大的面部數(shù)據(jù)庫(kù)。不過(guò)，隨著越來(lái)越多的初創(chuàng)公司在逐漸增長(zhǎng)的面部識(shí)別軟件市場(chǎng)上尋找突破，識(shí)別準(zhǔn)確度方面得分也很高。

僅在美國(guó)就有十幾家從事該業(yè)務(wù)的初創(chuàng)公司，其中包括Kairos和FaceFirst。市場(chǎng)研究公司PitchBook稱，硅谷正在扎堆進(jìn)入該行業(yè)，該公司還披露了過(guò)去幾年發(fā)生的數(shù)十宗投資交易。據(jù)PitchBook的數(shù)據(jù)顯示，過(guò)去三年平均總投資金額7870萬(wàn)美元。按照硅谷標(biāo)準(zhǔn)，這一數(shù)字算不上太亮眼，但能夠看出風(fēng)險(xiǎn)投資人在下重注賭其中一些初創(chuàng)公司會(huì)迅速成長(zhǎng)為大公司。

From Shops to Schools

Facial recognition software is not new. Primitive versions of the technology have existed since the 1980s when American mathematicians began defining faces as a series of numerical values, and used probability models to find a match. Security personnel in Tampa, Fla. deployed it at the 2001 Super Bowl and casinos have used it for years. But in the last few years, something changed.

“Facial recognition is undergoing something of a revolution,” says Grother of NIST, adding the change is most pronounced with fleeting or poor quality images. “The underlying technology has changed. The old tech has been replaced by a new generation of algorithms, and they’re remarkably effective.”

This revolution in facial recognition comes thanks to two factors that are transforming the field of artificial intelligence more broadly. The first is the emerging science of deep learning, a pattern recognition system that resembles the human brain. The second is an unprecedented glut of data that can be stored and parsed at low cost with the aid of cloud computing.

The first companies to take full advantage of these new developments, unsurprisingly, were Google and Facebook. In 2014, the social network launched a program called DeepFace that could discern if two faces belonged to the same person with an accuracy rate of 97.25%—a rate equivalent to what humans scored on the same test. A year later, Google topped this with its FaceNet program, which obtained a 100% accuracy score, according to security firm Gemalto.

Today, those companies and other tech giants like Microsoft are leaders in facial recognition—in no small part because of their access to large databases of faces. A growing number of startups, though, are also posting high accuracy scores as they seek a niche in a growing market for face software.

In the U.S. alone, there are more than a dozen such startups, including Kairos and FaceFirst. Silicon Valley has been flocking to the sector, according to market researcher PitchBook, which reveals dozens of investment deals taking place in the last few years. The average total investment in the last three years is $78.7 million, according to PitchBook. This is not an eye-popping number by Silicon Valley standards, but reflects a significant bet by venture capitalists that at least a few facial recognition startups will mushroom into major companies.

臉上的未來(lái)

2017年播出的技術(shù)反烏托邦電視劇《黑鏡》（Black Mirror）里，一位焦慮的母親看到女兒跟小混混在一起的畫(huà)面，非常擔(dān)心。為了確認(rèn)身份，她將男孩的面部圖像上傳到用戶面部識(shí)別服務(wù)。軟件立即顯示了男孩的名字和工作地點(diǎn)，然后她找過(guò)去對(duì)質(zhì)。

曾經(jīng)遙不可及的情景，如今已經(jīng)近在咫尺。盡管人們對(duì)面部識(shí)別的擔(dān)憂主要集中在政府使用上，但私人公司甚至個(gè)人的“黑鏡式”利用也將造成明顯的隱私風(fēng)險(xiǎn)。

隨著越來(lái)越多的公司開(kāi)始銷售面部識(shí)別技術(shù)，人們的面部數(shù)據(jù)進(jìn)入更多的數(shù)據(jù)庫(kù)，偷窺者和跟蹤者也可能用上新技術(shù)。商人和房東也能夠用來(lái)識(shí)別不受歡迎的人，悄悄地拒絕提供住房或服務(wù)。

“凡是有攝像機(jī)和大量人流的地方，都可以開(kāi)始積累圖像數(shù)據(jù)庫(kù)，然后用分析軟件查看是否與既有數(shù)據(jù)庫(kù)匹配?！?美國(guó)公民自由聯(lián)盟的政策分析師杰伊·斯坦利表示。

此外也存在黑客攻擊的風(fēng)險(xiǎn)。網(wǎng)絡(luò)安全公司Gemini Advisors的安德雷·巴雷舍維奇說(shuō)，曾經(jīng)見(jiàn)過(guò)從印度國(guó)家生物特征數(shù)據(jù)庫(kù)中竊取的資料，都在“暗網(wǎng)”上出售。他還沒(méi)發(fā)現(xiàn)有人出售美國(guó)人面部數(shù)據(jù)庫(kù)，但補(bǔ)充說(shuō)，“這只是時(shí)間問(wèn)題?！比绻l(fā)生這種情況，從酒店或零售商盜取的顧客面部圖片可以幫助罪犯實(shí)施詐騙或冒充身份。

隨著技術(shù)在幾乎沒(méi)有政府監(jiān)管的情況不斷推廣，限制濫用的最大希望可能在于軟件開(kāi)發(fā)商自身。在接受《財(cái)富》雜志采訪時(shí)，面部識(shí)別初創(chuàng)公司的首席執(zhí)行官們都表示，對(duì)隱私風(fēng)險(xiǎn)有著深刻的認(rèn)識(shí)。包括FaceFirst首席執(zhí)行官在內(nèi)不少人認(rèn)為，中國(guó)面部監(jiān)控系統(tǒng)普及比較有警示意義。

首席執(zhí)行官們還提出兩種方法限制行業(yè)濫用技術(shù)。第一是與購(gòu)買軟件者密切合作，確?？蛻舨粫?huì)隨意使用。舉例來(lái)說(shuō)，Ever AI的阿萊便表示，公司遵循的標(biāo)準(zhǔn)比亞馬遜更高，他聲稱亞馬遜幾乎向所有客戶出售Rekognition軟件。

為了回應(yīng)如何管理濫用的問(wèn)題，亞馬遜提供了馬特·伍德之前發(fā)表的一份聲明。馬特·伍德在亞馬遜云服務(wù)平臺(tái)負(fù)責(zé)人工智能服務(wù)，他指出公司政策禁止非法或有害行為。

負(fù)責(zé)面部識(shí)別業(yè)務(wù)高管還提到另一項(xiàng)潛在的隱私保護(hù)措施，使用技術(shù)措施確保數(shù)據(jù)庫(kù)中識(shí)別的面部不會(huì)遭到黑客攻擊。

Waldo公司的首席執(zhí)行官賴斯表示，面部圖像是以字母數(shù)字散列的形式存儲(chǔ)。也就是說(shuō)，即使發(fā)生數(shù)據(jù)泄露，人們的隱私也不會(huì)受到影響，因?yàn)楹诳蛯o(wú)法使用散列的面部圖像重建面部并對(duì)應(yīng)身份。其他人表示同意。

賴斯還擔(dān)心，立法者制定面部識(shí)別技術(shù)使用規(guī)則，可能弊大于利?！鞍押⒆雍拖丛杷黄鸬钩鋈?，制定一些瘋狂的規(guī)定，都差不多，最后只會(huì)是鬧劇?！彼f(shuō)。

與此同時(shí)，一些開(kāi)發(fā)面部識(shí)別軟件的公司也在應(yīng)用新技術(shù)，可能不再需要大量收集面部圖片訓(xùn)練算法?？偛课挥谶~阿密的面部識(shí)別初創(chuàng)公司Kairos便是其中之一，其客戶包括一家大型連鎖酒店。首席安全官斯蒂芬·摩爾表示，Kairos正在打造“合成”面部數(shù)據(jù)，復(fù)制各種表情和照明條件下的面部。他說(shuō)，“人造面孔”意味著公司開(kāi)發(fā)產(chǎn)品時(shí)需要的數(shù)據(jù)庫(kù)規(guī)?？梢孕∫恍?/p>

不管是監(jiān)督面部識(shí)別軟件客戶，還是保障數(shù)據(jù)安全，打造綜合培訓(xùn)工具，各項(xiàng)措施都可以將企業(yè)使用人們面部圖像的問(wèn)題緩和一些。與此同時(shí)，F(xiàn)aceFirst的特雷普相信隨著人們對(duì)該技術(shù)越發(fā)熟悉，相關(guān)焦慮也會(huì)減少。他甚至認(rèn)為2002年科幻電影《少數(shù)派報(bào)告》當(dāng)中出現(xiàn)的面部識(shí)別場(chǎng)景會(huì)變得很正常。

“千禧一代更愿意提供面部圖像?！渡贁?shù)派報(bào)告》里呈現(xiàn)的世界即將到來(lái)?！彼f(shuō)。“如果做得好，人們將享受到便利，而且將變成積極的經(jīng)歷。不會(huì)感覺(jué)太古怪?！?/p>

包括美國(guó)公民自由聯(lián)盟在內(nèi)的其他方面則沒(méi)有那么樂(lè)觀。不過(guò)，盡管有關(guān)該技術(shù)的爭(zhēng)論日益激烈，目前幾乎沒(méi)有任何法律限制使用面部數(shù)據(jù)。例外只有伊利諾斯州、得克薩斯州和華盛頓州，三個(gè)州里使用某人的臉之前需要一定程度的同意。相關(guān)法律并未真正檢驗(yàn)過(guò)，只有一地例外：伊利諾伊州，該州的消費(fèi)者可提起訴訟行使權(quán)利。

目前，伊利諾伊州的法律也是一起與Facebook有關(guān)的、備受關(guān)注的上訴法院案件主題，該案聲稱，獲取面部的限制并不能延伸到數(shù)字掃描。2017年，F(xiàn)acebook和谷歌進(jìn)行了一場(chǎng)失敗的游說(shuō)活動(dòng)，希望說(shuō)服伊利諾伊州的議員降低法律的影響。1月底，伊利諾伊州最高法院裁定，如果消費(fèi)者想就未經(jīng)授權(quán)識(shí)別生物特征提起訴訟，無(wú)須證明在現(xiàn)實(shí)世界中受到傷害，進(jìn)一步鼓勵(lì)了該法律的支持者。

其他國(guó)家也在考慮制定生物識(shí)別方面的法律。在聯(lián)邦一級(jí)，目前立法者還很少關(guān)注。然而情況可能會(huì)發(fā)生變化，因?yàn)閰⒆h員布萊恩·沙茨（夏威夷州）和羅伊·布朗特（密蘇里州）在3月提出了一項(xiàng)法案，要求公司在公共場(chǎng)所使用面部識(shí)別功能和/或與第三方共享面部數(shù)據(jù)之前，必須獲得許可。

喬治敦大學(xué)的研究員加維支持由法律監(jiān)督該項(xiàng)技術(shù)。但她也表示立法者很難跟上技術(shù)發(fā)展的腳步。

“面部識(shí)別面臨的一項(xiàng)挑戰(zhàn)是，由于傳統(tǒng)數(shù)據(jù)庫(kù)存在，其吸收速度非?？臁Ｈ藗兊拿娌繄D片太容易被采集到了?！彼f(shuō)?！案讣y不一樣的是，長(zhǎng)期以來(lái)指紋采集的方式和時(shí)間一直有規(guī)則限制，而面部識(shí)別技術(shù)領(lǐng)域沒(méi)有規(guī)則?！保ㄘ?cái)富中文網(wǎng)）

譯者：馮豐

審校：夏林

The Future of Your Face

In a 2017 episode of the techno-dystopian TV series Black Mirror, an anxious mother frets over images of a ne’er-do-well carrying on with her daughter. To identify him, she uploads an image of his face to a consumer facial identification service. The software promptly displays his name and place of work, and she goes to confronts him.

Such a scenario, once far-fetched, feels close at hand today. While fears over facial recognition have focused on its use by governments, its deployment by private companies or even individuals—Black Mirror-style—poses obvious privacy risks.

As more companies start to sell facial recognition, and as our faces end up in more databases, the software could catch on with voyeurs and stalkers. Merchants and landlords could also use it to identify those they deem to be undesirable, and quietly withhold housing or services.

“Anybody with a video camera and a place with a lot of foot traffic can start to compile a databases of images, and then use this analytic software to see if there’s a match with what you’ve compiled,” says Jay Stanley, a policy analyst of the ACLU.

There’s also the risk of hacking. Andrei Barysevich of Gemini Advisors, a cybersecurity firm, says he has seen profiles stolen from India’s national biometrics database for sale on “dark web” Internet sites. He has yet to see databases of American faces for sale, but added, “It’s just a matter of time.” If such a thing were to occur, a stolen collection of customer faces from a hotel or retailer could help criminals carry out fraud or identity theft.

As the technology spreads with little government oversight, the best hope to limit its misuse may lie with the software makers themselves. In interviews with Fortune, the CEOs of facial recognition startups all stated they were deeply attuned to privacy perils. A number, including the CEO of FaceFirst, cited the spread of face surveillance systems in China as a cautionary tale.

The CEOs also offered two ways the industry can limit misuse of their technology. The first is by working closely with the purchasers of their software to ensure clients don’t deploy it willy-nilly. Aley of Ever AI, for instance, says his company follows a higher standard than Amazon, which he claims furnishes its Rekognition tool to nearly all comers.

In response to a question of how it polices misuse, Amazon provided a previously published statement by Matt Wood, who overseas artificial intelligence services at Amazon Web Services, pointing to a company policy prohibiting activity that is illegal or harmful to others.

The other potential privacy safeguard cited by facial recognition executives is the use of technical measures to ensure the faces identified in their databases can’t be hacked.

Rice, the CEO of the Waldo, says faces are stored in the form of alphanumeric hashes. This means that, even in the event of a data breach, privacy would not be compromised because a hacker would not be able to use the hashes to reconstruct the faces and their identities. The point was echoed by others.

Rice is also wary that lawmakers could do more harm than good by making rules for the use of facial technology. “Throwing the baby out with the bathwater, and creating a bunch of crazy regulations, that would be a travesty,” he says.

Meanwhile, some companies that make facial recognition software are using new techniques that may reduce the need for large collections of faces to train their algorithms. This is the case with Kairos, a Miami-based facial recognition startup that names a major hotel chain among its clients. According to chief security officer Stephen Moore, Kairos is creating “synthetic” facial data to replicate a wide variety of facial expressions and lighting conditions. He says these “artificial faces” means the company can rely on smaller sets of real world faces to build its products.

All of these measures—oversight of facial recognition customers, sound data security, and synthetic training tools—could allay some of the privacy concerns related to companies’ use of our faces. At the same time, Trepp of FaceFirst believes anxiety over the technology will diminish as we become more familiar with it. He even argues that facial recognition scenes in the 2002 sci-fi movie Minority Report will start to feel normal.

“Millennials are much more willing to hand over their face. That [Minority Report] world is coming,” he says. “Done properly, I think people are going to enjoy it and it’s going to be a positive experience. It won’t feel creepy.”

Others, including the ACLU, are less sanguine. Still, despite the growing controversy around the technology, there is, for now, almost nothing in the way of laws to limit the use of your face. The only exception comes from a trio of states—Illinois, Texas, and Washington—that require a degree of consent before the use of someone’s face. These laws have not really been tested, with one exception: Illinois, where consumers can bring lawsuits to enforce the right.

Currently, the Illinois law is the subject of a high profile appeals court case involving Facebook, which claims that restrictions on obtaining faces does not extend to digital scans. In 2017, Facebook and Google ran an unsuccessful lobbying campaign to persuade Illinois lawmakers to dilute the law. In late January, the law’s supporters got a boost when the Illinois Supreme Court ruled that consumers do not have to show real-world harm if they want to sue over the unauthorized use of their biometrics.

Other states are considering biometrics laws of their own. At the federal level, lawmakers have so far devoted little attention to the matter. This may be changing, however, as Senators Brian Schatz (D-Ha.) and Roy Blount (R.MO) this month introduced a bill that would require companies to get permission before using facial recognition in public places and or share face data with third parties.

Garvie, the Georgetown researcher, is in favor of laws to oversee the technology. But she says it has been difficult for lawmakers to keep up.

“One challenge of facial recognition is it’s been incredibly quick on the uptake because of legacy databases. There are so many instances where our faces were captured,” she says. “Unlike fingerprints, where there have long been rules on how and when they’re collected, there are no rules for face technology.”