雷鋒網(wǎng) AI 科技評(píng)論按：DeepMind的聯(lián)合創(chuàng)始人 Mustafa Suleyman 近日在金融時(shí)報(bào)（Financial Times）上發(fā)表了一篇署名文章「Harnessing technology to challenge inequality」（用科技進(jìn)步消除不平等），講解了他對(duì)現(xiàn)代科學(xué)技術(shù)的風(fēng)險(xiǎn)與作用的見(jiàn)解。

除了文章本身，金融時(shí)報(bào)還提供了一個(gè)真人閱讀的有聲版本，所以我們也把英文原文附在譯文后，歡迎感興趣的英文讀者溫習(xí)溫習(xí)英文聽(tīng)力（笑）。

語(yǔ)音地址：鏈接 https://pan.baidu.com/s/1o7XrD38   密碼 yy4n

* 以下為雷鋒網(wǎng) AI 科技評(píng)論翻譯的中文譯文

人類社會(huì)有一些最緊迫又持久的問(wèn)題，從氣候變化到不平等，還有許多別的。如果想要解決它們，科學(xué)技術(shù)就會(huì)在其中起到領(lǐng)導(dǎo)性的作用。人工智能促進(jìn)下的科學(xué)突破可以帶來(lái)巨大的變化，它們可以在與人類最息息相關(guān)的領(lǐng)域幫助人類發(fā)現(xiàn)新的知識(shí)、新的想法和新的策略。

但是大眾對(duì)于科技工業(yè)中某些元素的越來(lái)越多的擔(dān)心已經(jīng)為我們敲響了警鐘?？茖W(xué)技術(shù)太重要了，它們的影響范圍也太大了，這都已經(jīng)是公眾的共識(shí)。新出現(xiàn)的一個(gè)個(gè)問(wèn)題背后的實(shí)質(zhì)，是科學(xué)技術(shù)的世界和真實(shí)世界之間已經(jīng)出現(xiàn)了至少三個(gè)不平衡的方面。

首先，開(kāi)發(fā)科學(xué)技術(shù)的人和使用這些科學(xué)技術(shù)的人之間是割裂的。硅谷的薪水是美國(guó)其它地區(qū)薪水中位數(shù)的兩倍，而且硅谷員工的組成在性別、種族、社會(huì)階級(jí)等等方面也不能代表全美的狀況。科學(xué)技術(shù)并不是價(jià)值中立的，而且如果我們想盡量減少技術(shù)造成的意外傷害的話，各種各樣的人群都需要參與到技術(shù)的構(gòu)建和調(diào)整中來(lái)。

這是一個(gè)非常緊急的問(wèn)題。女性和少數(shù)群體的訴求到現(xiàn)在都沒(méi)有得到足夠的關(guān)注，領(lǐng)袖們應(yīng)當(dāng)主動(dòng)打破這樣的局面。

第二點(diǎn)，科學(xué)技術(shù)真正的工作原理方面有巨大的信息不對(duì)稱。解決這個(gè)問(wèn)題一定要各個(gè)角色的人共同合作，并且需要新類型的團(tuán)體幫助人們深入理解復(fù)雜算法是如何運(yùn)轉(zhuǎn)的、這些算法對(duì)社會(huì)的影響又是什么。達(dá)到這個(gè)目標(biāo)需要勇氣、信任，以及超越每個(gè)人習(xí)以為常的社會(huì)角色之外的真正的討論和參與；而往往這種時(shí)候激進(jìn)主義者、政府和技術(shù)主義者會(huì)互相批評(píng)指責(zé)而不是通力協(xié)作。

它還需要讓數(shù)據(jù)的使用過(guò)程變得更加透明。許多企業(yè)、學(xué)術(shù)結(jié)構(gòu)和非營(yíng)利性組織都已經(jīng)開(kāi)始做出努力，嘗試開(kāi)發(fā)出一些方法讓算法產(chǎn)生的影響變得更好理解。MIT的研究人員Joy Buolamwini 和算法正義聯(lián)盟（Algorithmic Justice League）就已經(jīng)舉辦了一次展覽，讓人們更多地了解到面部識(shí)別系統(tǒng)遇到膚色較深的人的時(shí)候經(jīng)常會(huì)產(chǎn)生令人不快的結(jié)果。

第三點(diǎn)，而且這一點(diǎn)并不僅僅發(fā)生在科學(xué)技術(shù)身上，就是激勵(lì)結(jié)構(gòu)的不平衡。

衡量商業(yè)成就有一些標(biāo)準(zhǔn)的指標(biāo)，從融資規(guī)模到活躍用戶數(shù)等不一而足，但所有這些指標(biāo)都并沒(méi)有涵蓋企業(yè)在嘗試改變世界的同時(shí)背負(fù)的社會(huì)責(zé)任。

這種割裂的狀況產(chǎn)生得很早?？萍碱I(lǐng)域可能有很多的資金，但是大多數(shù)的企業(yè)家還是會(huì)失敗。每個(gè)希望讓新的企業(yè)快速成長(zhǎng)的創(chuàng)業(yè)者都需要想辦法說(shuō)服投資人和自己的員工，讓他們相信企業(yè)未來(lái)會(huì)成長(zhǎng)得多么大，然后不折不撓地向那個(gè)方向進(jìn)發(fā)。達(dá)到這樣的目標(biāo)需要一心一意地關(guān)注那些仿佛比較重要的數(shù)值，同時(shí)也就沒(méi)給考慮復(fù)雜的外在社會(huì)屬性或者聆聽(tīng)反對(duì)者的聲音留下什么空間。

為什么很多全世界最聰明的人都被最安全、最經(jīng)過(guò)完善驗(yàn)證的想法和商業(yè)模式吸引，這就可以算是其中的一部分原因。而這種狀況的結(jié)果就是他們推出了新的服務(wù)，給消費(fèi)者提供個(gè)性化的軟飲料，但同時(shí)世界上還有5億的人喝不到干凈的水；又或者他們想出了新的用手機(jī)點(diǎn)單的方法，而同時(shí)還有8億人營(yíng)養(yǎng)不良。人類社會(huì)需要新的激勵(lì)結(jié)構(gòu)，鼓勵(lì)更多的創(chuàng)業(yè)者解決真實(shí)世界中的問(wèn)題，心里也要懷著對(duì)道德倫理的尊重。

這幾件事沒(méi)有一件好做。但是更公平的世界也不會(huì)突然就自己出現(xiàn)。想得到符合倫理道德的結(jié)果，需要的遠(yuǎn)不止是算法和數(shù)據(jù)，社會(huì)討論和責(zé)任的水平高低也有重要的影響。而事后能得到的獎(jiǎng)賞是巨大的。如果人類能夠共同引導(dǎo)這些問(wèn)題走向正確的方向，相信我們肯定可以在未來(lái)的幾十年內(nèi)看到不可思議的科學(xué)進(jìn)步與社會(huì)進(jìn)步。社會(huì)中每一個(gè)相信科學(xué)技術(shù)力量的人都應(yīng)該盡自己所能，保證這些人造的系統(tǒng)能夠體現(xiàn)出人類集體自我的最高價(jià)值。

* 以下為英文原文（歡迎配合有聲版閱讀）

If we want to address society's most pressing and persistent challenges, from climate change to inequality, then technology will have a leading role to play. Scientific breakthroughs facilitated by artificial intelligence could make the crucial difference, by helping to discover new knowledge, ideas and strategies in the areas that matter most to us all.

But increasing public concern about some elements of the technology industry should serve as a wake-up call. Tech is too important, and its effects are too wide-ranging, not to form part of the public debate. Beneath the individual issues raised, there are at least three asymmetries between the world of tech and the real world.

First, the disconnect between people who develop technologies and the communities who use them. Salaries in Silicon Valley are twice the median wage for the rest of the US and the employee base is unrepresentative when it comes to gender, race, class and more. Technology isn't value neutral, and it needs to be built and shaped by diverse communities if we are to minimise the risk of unintended harms.

This is an urgent problem. Women and minority groups remain badly under-represented, and leaders need to be proactive in breaking the mould.

Second, there's an asymmetry of information regarding how technology actually works. Solving this has to be a collaborative effort, and requires new types of organisations that facilitate deep understanding of how complex algorithms operate and their impact on society. This takes courage, trust and the prioritisation of real debate and engagement over the comfort of our institutional roles, in which activists, governments and technologists are often more likely to criticise each other than to work together.

It also requires more visibility into how data are used. There are efforts under way within companies, alongside academics and non-profit organisations who are developing ways to make the impacts of algorithms easier to understand.

MIT researcher Joy Buolamwini and the Algorithmic Justice League have created museum exhibits to increase awareness of the disturbing ways facial recognition technologies often fail for individuals with darker skin tones.

Third — and this is by no means unique to tech — there is a structural imbalance of incentives.

The standard measures of business achievement, from fundraising valuations to active users, do not capture the social responsibility that comes with trying to change the world.

This disconnect starts early. There might be a lot of money in tech, but the vast majority of entrepreneurs still fail. Any founder hoping to get a new business off the ground has to convince investors and staff of future growth, and then deliver that relentlessly. Doing this takes single-minded focus on the metrics that appear to matter, with little room to consider complex societal externalities or listen to naysayers.

That's partly why some of the world's brightest minds gravitate towards the safest and most proven ideas and business models. They end up creating new services to personalise soda drinks when half a billion people don't have access to clean water, or new ways to order food by phone when more than 800m people are malnourished. We need new incentive structures to encourage more founders to take on real-world problems, and to do so with ethics at their heart.

None of this is easy. But a fairer world won't emerge by accident. Positive ethical outcomes depend on far more than algorithms and data: they depend on the quality of societal debate and accountability, too. The prize is enormous. If we get this right collectively, we can look forward to incredible scientific and social progress over the next few decades. All of us who believe in the power of technology must do everything we can to ensure these systems reflect humanity's highest collective selves.

（完）

雷鋒網(wǎng) AI 科技評(píng)論編譯。

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