0
雷鋒網 AI 科技評論按：DeepMind的聯合創始人 Mustafa Suleyman 近日在金融時報（Financial Times）上發表了一篇署名文章「Harnessing technology to challenge inequality」（用科技進步消除不平等），講解了他對現代科學技術的風險與作用的見解。
除了文章本身，金融時報還提供了一個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溫習英文聽力（笑）。
語音地址：鏈接 https://pan.baidu.com/s/1o7XrD38 密碼 yy4n
人類社會有一些最緊迫又持久的問題，從氣候變化到不平等，還有許多別的。如果想要解決它們，科學技術就會在其中起到領導性的作用。人工智能促進下的科學突破可以帶來巨大的變化，它們可以在與人類最息息相關的領域幫助人類發現新的知識、新的想法和新的策略。
但是大眾對于科技工業中某些元素的越來越多的擔心已經為我們敲響了警鐘。科學技術太重要了，它們的影響范圍也太大了，這都已經是公眾的共識。新出現的一個個問題背后的實質，是科學技術的世界和真實世界之間已經出現了至少三個不平衡的方面。
首先，開發科學技術的人和使用這些科學技術的人之間是割裂的。硅谷的薪水是美國其它地區薪水中位數的兩倍，而且硅谷員工的組成在性別、種族、社會階級等等方面也不能代表全美的狀況。科學技術并不是價值中立的，而且如果我們想盡量減少技術造成的意外傷害的話，各種各樣的人群都需要參與到技術的構建和調整中來。
這是一個非常緊急的問題。女性和少數群體的訴求到現在都沒有得到足夠的關注，領袖們應當主動打破這樣的局面。
第二點，科學技術真正的工作原理方面有巨大的信息不對稱。解決這個問題一定要各個角色的人共同合作，并且需要新類型的團體幫助人們深入理解復雜算法是如何運轉的、這些算法對社會的影響又是什么。達到這個目標需要勇氣、信任，以及超越每個人習以為常的社會角色之外的真正的討論和參與；而往往這種時候激進主義者、政府和技術主義者會互相批評指責而不是通力協作。
它還需要讓數據的使用過程變得更加透明。許多企業、學術結構和非營利性組織都已經開始做出努力，嘗試開發出一些方法讓算法產生的影響變得更好理解。MIT的研究人員Joy Buolamwini 和算法正義聯盟（Algorithmic Justice League）就已經舉辦了一次展覽，讓人們更多地了解到面部識別系統遇到膚色較深的人的時候經常會產生令人不快的結果。
第三點，而且這一點并不僅僅發生在科學技術身上，就是激勵結構的不平衡。
衡量商業成就有一些標準的指標，從融資規模到活躍用戶數等不一而足，但所有這些指標都并沒有涵蓋企業在嘗試改變世界的同時背負的社會責任。
這種割裂的狀況產生得很早。科技領域可能有很多的資金，但是大多數的企業家還是會失敗。每個希望讓新的企業快速成長的創業者都需要想辦法說服投資人和自己的員工，讓他們相信企業未來會成長得多么大，然后不折不撓地向那個方向進發。達到這樣的目標需要一心一意地關注那些仿佛比較重要的數值，同時也就沒給考慮復雜的外在社會屬性或者聆聽反對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、最經過完善驗證的想法和商業模式吸引，這就可以算是其中的一部分原因。而這種狀況的結果就是他們推出了新的服務，給消費者提供個性化的軟飲料，但同時世界上還有5億的人喝不到干凈的水；又或者他們想出了新的用手機點單的方法，而同時還有8億人營養不良。人類社會需要新的激勵結構，鼓勵更多的創業者解決真實世界中的問題，心里也要懷著對道德倫理的尊重。
這幾件事沒有一件好做。但是更公平的世界也不會突然就自己出現。想得到符合倫理道德的結果，需要的遠不止是算法和數據，社會討論和責任的水平高低也有重要的影響。而事后能得到的獎賞是巨大的。如果人類能夠共同引導這些問題走向正確的方向，相信我們肯定可以在未來的幾十年內看到不可思議的科學進步與社會進步。社會中每一個相信科學技術力量的人都應該盡自己所能，保證這些人造的系統能夠體現出人類集體自我的最高價值。
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 AI 科技評論編譯。
相關文章：
100:0！Deepmind Nature論文揭示最強AlphaGo Zero，無需人類知識
你可能還不知道，WaveNet 為了進駐 Google Assistant 做出了這些重大變化
雷峰網版權文章，未經授權禁止轉載。詳情見轉載須知。
