考研熱點話題-AI and Humanity
隨著大數據的發展，神經網絡算法和計算機技術的巨大進步，AI已經從學術研究化身為引領製造業、醫療保健、運輸、零售業等眾多行業的賦能力量。
我們看一下李飛飛發表的文章
AI 正在對人類社會各行各業帶來的巨大影響，「如果我們的時代確實在經歷許多人所說的『工業革命』，那麼 AI 無疑是其中一個推動力」，
在李飛飛看來，「以人為中心的 AI」包含三大主旨，分別是：
一、讓 AI 更好地反映人類的深層智能；
二、AI 應幫助人類變得更強，而不是替代人類；
三、確保 AI 在發展過程中對人類的影響得到正確的引導。
紐約時報-觀點：How to Make A.I. Human-Friendly
1. For a field that was not well known outside of academia a decade ago, artificial intelligence has grown dizzyingly fast. Tech companies from Silicon Valley to Beijing are betting everything on it, venture capitalists are pouring billions into research and development, and start-ups are being created on what seems like a daily basis. If our era is the next Industrial Revolution, as many claim, A.I. is surely one of its driving forces.
第一段直接開題，舉出科技公司和初創公司在AI 上的投入。
It is an especially exciting time for a researcher like me. When I was a graduate student in computer science in the early 2000s, computers were barely able to detect sharp edges in photographs, let alone recognize something as loosely defined as a human face. But thanks to the growth of big data, advances in algorithms like neural networks and an abundance of powerful computer hardware, something momentous has occurred: A.I. has gone from an academic niche to the leading differentiator in a wide range of industries, including manufacturing, health care, transportation and retail.
I worry, however, that enthusiasm for A.I. is preventing us from reckoning with its looming effects on society. Despite its name, there is nothing 「artificial」 about this technology — it is made by humans, intended to behave like humans and affects humans. So if we want it to play a positive role in tomorrow’s world, it must be guided by human concerns.
I call this approach 「human-centered A.I.」 It consists of three goals that can help responsibly guide the development of intelligent machines.
First, A.I. needs to reflect more of the depth that characterizes our own intelligence. Consider the richness of human visual perception. It’s complex and deeply contextual, and naturally balances our awareness of the obvious with a sensitivity to nuance. By comparison, machine perception remains strikingly narrow.
首先，AI需要更多地反映我們智能的深度。以人類視覺的豐富感知為例，它是如此複雜、深層次，並且能在明確地覺知前景和靈敏地捕獲背景中取得自然平衡。相比之下，機器感知仍然非常狹窄。
Sometimes this difference is trivial. For instance, in my lab, an image-captioning algorithm once fairly summarized a photo as 「a man riding a horse」 but failed to note the fact that both were bronze sculptures. Other times, the difference is more profound, as when the same algorithm described an image of zebras grazing on a savanna beneath a rainbow. While the summary was technically correct, it was entirely devoid of aesthetic awareness, failing to detect any of the vibrancy or depth a human would naturally appreciate.
有時候這種差異微不足道，例如，在我的實驗室裡，圖像字幕算法可以識別出「騎馬的人」，而完全沒有注意到兩個都是銅像。同樣的算法用來識別彩虹之下草原之上的斑馬時差異更明顯。雖然識別和描述實現了技術上的正確性，但完全沒有審美意識，沒有任何人類可以自然感受到的活力或深度。
That may seem like a subjective or inconsequential critique, but it points to a major aspect of human perception beyond the grasp of our algorithms. How can we expect machines to anticipate our needs — much less contribute to our well-being — without insight into these 「fuzzier」 dimensions of our experience?
這聽起來有點吹毛求疵，但是這也指出了我們人類感知超越機器算法的一個主要方面。如果我們不能洞察人類體驗中這些「模糊」的維度，又如何期待機器能預測我們的需求，何談為人類的福祉做貢獻？
Making A.I. more sensitive to the full scope of human thought is no simple task. The solutions are likely to require insights derived from fields beyond computer science, which means programmers will have to learn to collaborate more often with experts in other domains.
要讓AI對人類思維的全方位更敏感不是一件容易的事。這需要計算機科學之外其它領域的專業知識，這意味著程式設計師必須與其他領域的專家合作。
Such collaboration would represent a return to the roots of our field, not a departure from it. Younger A.I. enthusiasts may be surprised to learn that the principles of today’s deep-learning algorithms stretch back more than 60 years to the neuroscientific researchers David Hubel and Torsten Wiesel, who discovered how the hierarchy of neurons in a cat’s visual cortex responds to stimuli.
這種合作代表著回歸，而非背離我們這個領域的起源，年輕AI學生們可能會驚訝於今天深度學習算法原理，起源於 David Hubbard和Torsten Wiesel發現的貓視覺皮層中神經元的層次結構對刺激的反應機制。
Likewise, ImageNet, a data set of millions of training photographs that helped to advance computer vision, is based on a project called WordNet, created in 1995 by the cognitive scientist and linguist George Miller. WordNet was intended to organize the semantic concepts of English.
同樣，包含數百萬張訓練圖片的ImageNet，幫助發展了計算機視覺。這個項目，是基於認知科學家和語言學家George Miller在1995年創建的WordNet數據集。WordNet旨在組織英語的語義概念。
Reconnecting A.I. with fields like cognitive science, psychology and even sociology will give us a far richer foundation on which to base the development of machine intelligence. And we can expect the resulting technology to collaborate and communicate more naturally, which will help us approach the second goal of human-centered A.I.: enhancing us, not replacing us.
重新連接AI與認知科學、心理學甚至社會學，將給人工智慧一個更加強大的發展基礎。而且我們可以期待這樣發展出來的技術，會讓合作和交流更加自然，從而實現以人為本的第二個目標：強化人類，而不是取代人類。
Imagine the role that A.I. might play during surgery. The goal need not be to automate the process entirely. Instead, a combination of smart software and specialized hardware could help surgeons focus on their strengths — traits like dexterity and adaptability — while keeping tabs on more mundane tasks and protecting against human error, fatigue and distraction.
想像一下AI在手術中的作用。它的目標不是把整個過程完全自動化，相反，智能軟體和專用硬體的結合可以幫助外科醫生專注於自己的優勢——如靈活性和適應性——而讓機器從事更加常規性的工作， 以避免人類容易發生的失誤、疲勞和被幹擾。
Or consider senior care. Robots may never be the ideal custodians of the elderly, but intelligent sensors are already showing promise in helping human caretakers focus more on their relationships with those they provide care for by automatically monitoring drug dosages and going through safety checklists.
或者考慮老人護理的情景。機器人可能並不是老人看護的最佳人選，但智能感應器在幫助人類護理員方面前景很好。通過自動監測藥物劑量和自動核對安全檢查清單，人類護理員可以將更多的精力放在建設與被護理者之間的關係上。
These are examples of a trend toward automating those elements of jobs that are repetitive, error-prone and even dangerous. What’s left are the creative, intellectual and emotional roles for which humans are still best suited.
這些都是自動化取代那些重複的、容易出錯的甚至是危險工作的例子。而剩下的創造性的，需要智力和情感的工作，由人類來完成仍然是最適合的。
No amount of ingenuity, however, will fully eliminate the threat of job displacement. Addressing this concern is the third goal of human-centered A.I.: ensuring that the development of this technology is guided, at each step, by concern for its effect on humans.
然而，沒有任何聰明才智會完全消除工作流失的威脅。解決這個問題是以人為本的AI的第三個目標：確保這項技術的每一步發展都關注其對人類的影響。
Today’s anxieties over labor are just the start. Additional pitfalls include bias against underrepresented communities in machine learning, the tension between A.I.’s appetite for data and the privacy rights of individuals and the geopolitical implications of a global intelligence race.
今天對工作流失的焦慮只是一個開始。其他問題還包括弱勢群體中機器學習從業人數的偏倚，AI對數據的高需求與保護個人隱私之間的關係，以及全球智能競賽的地緣政治影響。
Adequately facing these challenges will require commitments from many of our largest institutions. Universities are uniquely positioned to foster connections between computer science and traditionally unrelated departments like the social sciences and even humanities, through interdisciplinary projects, courses and seminars. Governments can make a greater effort to encourage computer science education, especially among young girls, racial minorities and other groups whose perspectives have been underrepresented in A.I. And corporations should combine their aggressive investment in intelligent algorithms with ethical A.I. policies that temper ambition with responsibility.
充分面對這些挑戰要求各大機構的共同付出。大學的獨特定位是通過跨學科項目、課程和研討會來促進計算機科學與傳統上不相關的學科，如社會科學甚至人文科學之間的聯繫。各國政府可以作出更大的努力，鼓勵計算機科學教育，特別是在AI中代表性不足的年輕女孩、少數種族和其他群體。公司應該將積極投資智能算法與倫理道德結合，兼顧抱負與責任。
No technology is more reflective of its creators than A.I. It has been said that there are no 「machine」 values at all, in fact; machine values are human values. A human-centered approach to A.I. means these machines don’t have to be our competitors, but partners in securing our well-being. However autonomous our technology becomes, its impact on the world — for better or worse — will always be our responsibility.
沒有哪項技術比AI更能反映它的創造者。實際上，雖然有人認為機器沒有價值觀，但事實是：機器的價值觀是其創造者的價值觀。AI以人為本的方法意味著這些機器不是人類的競爭對手，而是保證我們福祉的夥伴。無論我們的技術自動化到什麼程度，它對世界的影響——無論好壞——始終是我們的責任。
我們看一下經濟學人等外刊有關AI 的平行文本
2018年 4月 AI-Spy 的一篇文章
先看看開頭段該怎麼借鑑
1. ARTIFICIAL intelligence (AI) is barging its way（橫衝直撞） into business. Firms of all types are harnessing（利用） AI to forecast demand, hire workers and deal with customers. In 2017 companies spent around $22bn on AI-related mergers and acquisitions, about 26 times more than in 2015. The McKinsey Global Institute, a think-tank within a consultancy, reckons that just applying AI to marketing, sales and supply chains could create economic value, including profits and efficiencies, of $2.7trn over the next 20 years. Google’s boss has gone so far as to declare that AI will do more for humanity than fire or electricity.
人工智慧（AI）橫衝直撞，闖入了商業領域。各種各樣的公司都在利用人工智慧來預測需求、僱用員工、與客戶打交道。2017年，企業在AI方面的併購支出達220億美元上下，大約是2015年的26倍。諮詢公司麥肯錫的內部智庫麥肯錫全球研究院（McKinsey Global Institute）認為，僅僅是將人工智慧應用到營銷、銷售和供應鏈上，未來20年就能創造2.7萬億美元的經濟價值，包括利潤和效率。谷歌的老闆甚至宣稱對人類而言，人工智慧比火和電的用處更大。
第一段商業領域各種各樣的公司都在利用人工智慧來預測需求（harness AI to forcast demand）
1. 企業在AI方面的併購 (M&A)
2. 諮詢公司麥肯錫預測AI 帶來的經濟價值（economic value）
我們還可以借鑑2018年紐約時報：《為何機器人搶不走你的工作——至少現在還不行》 一段進行開頭
Today it’s widely accepted that brainy computers are coming for our jobs. They』ll have finished your entire weekly workload before you』ve had your morning toast – and they don’t need coffee breaks, pension funds, or even sleep. Although many jobs will be automated in the future, in the short term at least, this new breed of super-machines is more likely to be working alongside us.
今天，人們普遍認為智能計算機會搶走我們的工作，在你早餐還沒吃完以前，它就已經完成了你一周的工作量，而且他們還不休息，不喝咖啡，也不要退休金，甚至不用睡覺。但事實上，雖然很多工作未來都會自動化，但至少短期內，這種新品種智能機器更有可能是與我們一起工作（而不是取代人類）。
我們再看一下2018年6月經濟學人 《谷歌在人工智慧方面遭遇更多抨擊》開頭段
DISCOVERING and harnessing fire unlocked more nutrition from food, feeding the bigger brains and bodies that are the hallmarks（標誌） of modern humans.Google’s chief executive, Sundar Pichai, thinks his company’s development of artificial intelligence trumps（勝過） that. 「AI is one of the most important things that humanity is working on,」 he told an event in California earlier this year. 「It’s more profound than, I don’t know, electricity or fire.」
火的發現和利用使人們可以從食物中汲取更多營養，讓變大的大腦和身軀有營養，這是現代人的兩大特徵。谷歌執行長桑達爾·皮查伊（Sundar Pichai）則認為，谷歌在人工智慧方面的發展超越了這一點。今年早些時候，他在加利福尼亞舉行的一場活動上說道：「人工智慧是人類最重要的研究之一，或許比火和電更有意義。
我們再看一下2016年經濟學人《機械扭曲》開頭
EXPERTS warn that 「the substitution of machinery for human labour」 may 「render the population redundant」. They worry that 「the discovery of this mighty power」 has come 「before we knew how to employ it rightly」. Such fears are expressed today by those who worry that advances in artificial intelligence (AI) could destroy millions of jobs and pose a 「Terminator」-style threat to humanity. But these are in fact the words of commentators discussing mechanisation and steam power two centuries ago. Back then the controversy over the dangers posed by machines was known as the 「machinery question」. Now a very similar debate is under way.
專家們警告稱，「以機械化替代人力操作」有可能會「帶來人口過剩」。他們擔心，儘管「這種強大的力量已經被發明了」，可目前我們卻還不清楚該如何正確地利用它。 之所以在今天出現了這些擔憂，是因為他們害怕「人工智慧」（AI）的發展有可能令數百萬個就業機會喪失，並給人類帶來「終結者」式威脅。而實際上早在200年前，當評論家們討論機械化及蒸汽動力時，他們就說過同樣的話。當年，關於機械化所帶來的危害性的爭論被稱為「機械質疑」。而眼下，極其類似的爭論正在進行。
我們還可以進行對AI 比喻定義等，我們來看《經濟學人》——AI的遠大前程
Computers have been able to read text and numbers for decades, but have only recently learned to see, hear and speak. AI is an omnibus（綜合性的）term for a 「salad bowl」 of different segments and disciplines（邏輯）, says Fei-Fei Li, director of Stanford’s AI Lab and an executive at Google’s cloud-computing unit. Subsections of AI include robotics, which is changing factories and assembly lines（組裝線）, and computer vision, used in applications from identifying something or someone in a photo to self-driving-car（無人駕駛汽車） technology. Computer vision is AI’s 「killer app」, says Ms Li, because it can be used in so many settings, but AI has also become more adept at recognising speech. It underlies voice assistants（語音助手） on phones and home speakers（家庭音箱） and allows algorithms to listen to calls and take in the speaker’s tone（語氣、語調） and content.
官方譯文：計算機能閱讀文本和數字已經有幾十年了，但直到最近才學會了看、聽、說。AI是一個綜合性術語，就像是涵蓋了不同領域和學科的「一碗色拉」，史丹福大學人工智慧實驗室主管、谷歌雲計算部門負責人李飛飛說。它的下屬分支包括正在改變工廠和組裝線的機器人技術，以及部署在各種應用程式中的計算機視覺——從識別照片中的人或物到無人駕駛汽車技術等。李飛飛說，計算機視覺是AI的「殺手級應用」，因為運用場合是如此之多，但AI在語音識別方面也已變得更加嫻熟。它是配備在手機和家用音箱上的語音助理的技術基礎，還讓算法能夠監聽來電並識別說話者的語調和內容。
我們繼續看第二段
2. Such grandiose forecasts kindle anxiety as well as hope. Many fret that AI could destroy jobs faster than it creates them. Barriers to entry from owning and generating data could lead to a handful of dominant firms in every industry.
這些前景光明的預測不僅點燃了人們的希望，同時也引發了焦慮。很多人擔憂AI搶奪工作的速度要比創造崗位的速度更快。擁有和產生數據的壁壘將使得各個領域最終只有少數公司能夠佔據主導地位。
第二段 過渡段，對AI 的擔憂
1. 搶奪工作崗位（destroy job）
2. 壟斷公司產生 （a handful of dominant firms）
在寫作中我們如果借鑑該段想寫長一點的文章起到承上啟下的作用。
3. Less familiar, but just as important, is how AI will transform the workplace. Using AI, managers can gain extraordinary control over（嚴格監管） their employees. Amazon has patented a wristband that tracks the hand movements of warehouse workers and uses vibrations to nudge them into being （促使提高）more efficient. Workday, a software firm, crunches around 60 factors to predict which employees will leave. Humanyze, a startup, sells smart ID badges that can track employees around the office and reveal how well they interact with colleagues.
儘管與人們的普遍印象不同，但同樣重要的，是AI對辦公領域的改變。藉助AI技術，經理可以嚴格監管員工。亞馬遜為一款手環申請了專欄，該手環可以追蹤庫房工人手臂移動的軌跡，並通過振動來協助他們提高效率。一家名為Workday的軟體公司，為了推斷員工的離職情況，深入分析了60種相關因素。還有一家名為Humanyze的創業公司，出售一款智能ID徽章，可以追蹤辦公室員工的運動軌跡，並藉此來判斷與其他同事
的互動情況。
第三段主旨句AI 對辦公領域的改變 （tranform the workplace）---嚴格監管員工
1. 亞馬遜手環Amazon wristband
2. Workday的軟體公司
3.Humanyze 公司的smart ID badges
4. Surveillance at work is nothing new. Factory workers have long clocked in and out（上下班打卡）; bosses can already see what idle workers do on their computers. But AI makes ubiquitous surveillance worthwhile, because every bit of data is potentially valuable. Few laws govern how data are collected at work, and many employees unguardedly consent to surveillance when they sign their employment contract. Where does all this lead?
對工作情況進行監管並不是什麼新鮮事。一直以來，工廠工人上下班就要打卡；老闆們在電腦前就可以看到無所事事的員工在忙些什麼。但是AI讓無處不在的監管變得有價值，因為每一個微小的數據都有潛在價值。幾乎沒有法律對辦公領域的數據收集做出規定，而且很多員工根本沒有意識到籤署勞動合同就意味著默認監管。這會有怎樣的影響呢？
第四段過渡段引出AI 監管員工的導致結果
5. Start with the benefits. AI ought to improve productivity. Humanyze merges data from its badges with employees』 calendars and e-mails to workout, say, whether office layouts favour teamwork. Slack, a workplace messaging app, helps managers assess how quickly employees accomplish tasks. Companies will see when workers are not just dozing off （打瞌睡）but also misbehaving. They are starting to use AI to screen for anomalies in expense claims, flagging（標示出） receipts from odd hours of the night more efficiently than a carbon-based beancounter can.
先從好處說起。AI可以用來改善生產效率。Humanyze公司綜合徽章中收集的關於員工日程、郵件和健身的數據，能夠判斷出辦公室布局是否有利於團隊合作。Slack是一家提供辦公信息軟體，能夠幫助老闆查看員工的工作效率。 今後有些公司不僅可以判斷員工是否偷懶，還可以防止他們犯錯。公司使用AI技術篩查異常報銷；標註夜間時段的收據，並且比會計工作效率更高。
第五段 人工智慧在辦公領域的好處
主旨句改善生產效率（improve productivity）
1. 判斷出辦公室布局是否有利於團隊合作（ favour teamwork）
2. 幫助老闆查看員工的工作效率
3. 防止偷懶，犯錯
4. 篩選異常報銷
6. Employees will gain,too. Thanks to strides in computer vision, AI can check that workers are wearing safety gear and that no one has been harmed on the factory floor. Some will appreciate more feedback on their work and welcome a sense of how to do better. Cogito, a startup, has designed AI enhanced software that listens to customer-service calls and assigns an「 empathy score」based on how compassionate agents are and how fast and how capably they settle complaints.
員工也可以從中獲益。得益於計算機視覺的巨大發展，人工智慧可以檢測到員工是否佩戴安全裝備，這樣，員工就不會在工作場地受到傷害。一些員工十分樂意得到其工作的反饋，並且願意了解到如何提高工作效率。創業公司Cogito設計出一款人工智慧升級軟體，這個軟體可以接聽客服服務電話，並且可以就代理商的態度以及他們解決投訴問題的速度和能力給出「感情分」。
第六段 AI監控對員工的好處
1. 檢測到員工是否佩戴安全裝備
2. 員工了解到如何提高工作效率
3. 解決客服問題
7. Machines can help ensure that pay rises and promotions go to those who deserve them. That starts with hiring. People often have biases but algorithms, if designed correctly, can be more impartial. Software can flag patterns that people might miss. Textio, a start up that uses AI to improve job descriptions, has found that women are likelier to respond to a job that mentions 「developing」 a team rather than 「managing」 one. Algorithms will pick up differences in pay between genders and races, as well as sexual harassment and racism that human managers consciously or unconsciously overlook.
這些智能設備可以確保那些在崗位上真正作出貢獻的人獲得漲薪和晉升的機會。這在招聘時就開始發揮其作用。人總是會帶有偏見，但算法，如果設計合理的話，要比人的判斷公平得多。軟體可以標記出那些人類可能會忽略的模式。一個名為Textio的創業企業利用人工智慧來提升職位說明，該企業發現，女性在回答求職面試的時候總是更傾向於「發展團隊合作」而不是「管理團隊」的工作。算法可以輕易地發現性別，種族之間的薪水差異。對於像工作中人力經理有意無意忽略的性騷擾以及種族歧視問題，算法也可以敏銳地察覺到。
第七段 人工智慧有助於確保加薪和晉升的機會留給那些應得的人
我們看一下經濟學人AI 的遠大前程，對AI 的優點的論述
（1）AI will change more than borrowers』 bank balances（銀行餘額）. Johnson & Johnson, a consumer-goods firm, and Accenture, a consultancy, use AI to sort through（分類、整理） job applications and pick the best candidates. AI helps Caesars, a casino（俱樂部） and hotel group, guess customers』 likely spending and offer personalised promotions to draw them in. Bloomberg, a media and financial-information firm, uses AI to scan companies』 earnings releases（財報） and automatically generate news articles. Vodafone, a mobile operator, can predict problems with its network and with users』 devices before they arise. Companies in every industry use AI to monitor cyber-security threats and other risks, such as disgruntled employees.
官方譯文：AI將改變的不僅僅是貸款人的帳戶餘額。消費品公司強生和諮詢公司埃森哲（Accenture）用AI查看應聘資料，篩選出最佳人選。AI幫助賭場和酒店集團凱撒娛樂（Caesars）估測客人的消費水平，提供個性化促銷來吸引他們。媒體和金融信息公司彭博用AI掃描企業財報，自動生成新聞報導。移動運營商沃達豐（Vodafone）用AI監測其網絡和用戶設備，提前預警故障。各行各業的公司都在使用AI監控網絡安全威脅和其他風險，比如心懷不滿的員工。
（2）Instead of relying on gut instinct（直覺） and rough estimates, cleverer and speedier AI-powered predictions promise（預示…可能發生） to make businesses much more efficient. At Leroy Merlin, a French home-improvement retailer, managers used to order new stock on Fridays, butdefaulted to（默認） the same items as the week before so they could start their weekend sooner. The firm now uses algorithms to take in（弄清楚）past sales data and other information that could affect sales, such as weather forecasts, in order to stock shelves more effectively. That has helped it reduce its inventory by 8% even as sales have risen by 2%, says Manuel Davy of Vekia, the AI startup that engineered（策劃） the program.
官方譯文：相比依賴直覺和粗略的估算，更聰明也更快速的AI預測將幫助企業大幅提高效率。法國家居裝飾零售商樂華梅蘭（Leroy Merlin）的管理層以前每周五下新訂單，默認的設置是重複前一周的訂單，這樣大家可以早點下班過周末。現在，公司用算法來斟酌歷史銷售數據和天氣預報等其他可能影響銷售的信息，以更有效地安排庫存。據創建該算法的AI創業公司Vekia的曼紐爾·戴維（Manuel Davy）說，這幫助該公司將庫存減少了8％，同時銷售額卻增長了2％。
（3）As AI spreads beyond the tech sector, it will fuel the rise of new firms that challenge incumbents（現任者、在職者）. This is already happening in the car industry, with autonomous-vehicle startups and ride-hailing（打車、約車） firms such as Uber. But it will also change the way other companies work, transforming traditional functions such as supply-chain（供應鏈） management, customer service and recruitment.
官方譯文：隨著AI傳播到科技行業之外，它將推動新企業的崛起，為成熟企業帶來挑戰。這已經在汽車產業裡發生——AI催生了無人駕駛汽車創業公司和優步等網約車公司。但它也將改變其他企業的運作方式，改變供應鏈管理、客服和招聘等傳統職能。
我們繼續看AI-Spy 文章的第八段AI 的劣勢
8. Yet AI’s benefits will come with many potential drawbacks. Algorithms may not be free of the biases of their programmers. They can also have unintended consequences. The length of a commute may predict whether an employee will quit a job, but this focus may inadvertently harm poorer applicants. Older staff might work more slowly than younger ones and could risk losing their positions if all AI looks for is productivity.
然而，人工智慧在帶來許多便利的同時也有許多潛在的局限性。算法也許逃不掉程序設計員的偏見。算法還會產生意外的後果。上下班時間的長短可能會預測出員工會不會跳槽，但就這一點，它可能會無意間傷害那些經濟拮据的應聘者。和年輕的員工相比，老員工的工作速度相對較慢，倘若人工智慧一味地追求生產力的話，這些老員工將面臨被淘汰的風險。
邏輯結構：
五六七段對AI 的好處做了論述，第八段九段用了yet 進行轉折，開始說人工智慧的缺點
我們看看AI 遠大前程怎麼描述AI 的劣勢：
（1）The path ahead is exhilarating（令人興奮的） but perilous（危險的、冒險的）. Around 85% of companies think AI will offer a competitive advantage, but only one in 20 is 「extensively」 employing it today, according to a report by MIT’s Sloan Management Review and the Boston Consulting Group. Large companies and industries, such as finance, that generate a lot of data, tend to be ahead and often build their own AI-enhanced systems. But many firms will choose to work with the growing array of independent AI vendors, including cloud providers, consultants and startups.
官方譯文：前路令人振奮卻也危險重重。根據麻省理工學院的《斯隆管理評論》和波士頓諮詢集團聯合撰寫的報告，約85％的企業認為AI將帶來競爭優勢，但只有5％的公司正在「廣泛」地使用它。生成大量數據的大企業和金融等行業往往走在前頭，常常建立自己的AI增強系統。但許多企業會選擇與隊伍不斷擴大的獨立AI供應商合作，包括雲供應商、諮詢公司和創業公司等。
我們繼續看AI-Spy第九段
And surveillance may feel Orwellian—a sensitive matter now that people have begun to question how much Facebook and other tech giants know about their private lives. Companies are starting to monitor how much time employees spend on breaks. Veriato, a software firm, goes so far as to track and log every key stroke employees make on their computers in order to gauge how committed they are to their company. Firms can use AI to sift through not just employees』 professional communications but their social-media profiles,too. The clue is in Slack’s name, which stands for 「searchable logo fall conversation and knowledge」.
而且職場上的監督也會導致敏感的「奧威爾現象」出現，因為人們開始質疑臉書和其他科技巨頭究竟多大程度地侵入了他們的私生活。企業正對員工的休息時間展開監測。Veriato是一家軟體公司，為了評估其員工對企業的忠誠度竟然對員工在電腦上的每次操作都進行追蹤和記錄。利用人工智慧，企業不僅仔細查看員工的工作範圍以內的交流，而且還查看他們的社交文件。這一跡象在「Slack』」這個名字中就可以足以體現出來，其寓意是「談話和信息的可搜索日誌」。
我們看一下解決方案
11.As regulators and employers weigh the pros and cons of AI in the workplace, three principle sought to guide its spread. First, data should be anonymised where possible. Microsoft, for example, has a product that shows individuals how they manage their time in the office, but gives managers information only in aggregated form. Second, the use of AI ought to be transparent. Employees should be told what technologies are being used in their workplaces and which data are being gathered. As a matter of routine, algorithms used by firms to hire, fire and promote should be tested for bias and unintended consequences. Last, countries should let individuals request their own data, whether they are ex-workers wishing to contest a dismissal or job seekers hoping to demonstrate their ability to prospective employers.
對於人工智慧在職場中的利弊，儘管管理者和用人單位進行了權衡，其發展態勢仍需遵循三大原則。首先，儘可能地對用戶數據進行匿名。例如，微軟公司推出的一項產品可以向員工展示如何利用他們的辦公時間，但是在呈交給管理者時，僅僅是以數據集的形式呈現。其次，人工智慧的使用應該是透明公開的。員工應該被告知工作場所中所安裝的高科技設備以及其採集數據的範圍。作為一種常規的做法，算法在用於僱傭、解僱、以及員工的晉升時應該對其進行檢測，避免偏見和意外後果的出現。最後，各個國家應該允許個體索要其個人數據，無論他是一個對解僱提起質疑的前任員工，還是希望向潛在老闆展示能力的求職者。
我們看一下2016年經濟學人《人工智慧的未來有史為鑑——社會又該如何應對》
提出的解決方案
（1）As technology changes the skills needed for each profession, workers will have to adjust. That will mean making education and training flexible enough to teach new skills quickly and efficiently. It will require a greater emphasis on lifelong learning and on-the-job training, and wider use of online learning and video-game-style simulation. AI may itself help, by personalizing computer-based learning and by identifying workers』 skills gaps and opportunities for retraining.
由於科技改變了各行各業所需的技能，勞動者們也需要作出調整。也就是說，教育和培訓應該足夠靈活，才能更快更有效的教授這些新技能。我們要更加重視終身學習和在崗培訓，更廣泛的應用在線課程及電子遊戲式模擬教學。通過定製基於電腦的個性化學習方案、識別勞動者技術短板及再教育機會等途徑，人工智慧也可以為此提供便利。
（2）Social and character skills will matter more, too. When jobs are perishable, technologies come and go and people’s working lives are longer, social skills are a foundation. They can give humans an edge, helping them do work that calls for empathy and humaninteraction—traits that are beyond machines.
社交和個性技巧也將越來越重要。眼下，工作可能被隨時取代，技術更替層出不窮，人們工作年限也越來越長，各種社交技巧也就成為了立足之根基。這些技巧可以讓人類劍走偏鋒，幫助他們從事需要同情心和人際互動的工作——這些是機器望塵莫及的。
（3）And welfare systems will have to be updated, to smooth the transitions between jobs and to support workers while they pick up new skills. One scheme widely touted as a panacea is a 「basic income」, paid to everybody regardless of their situation. But that would not make sense without strong evidence that this technological revolution, unlike previous ones, is eroding the demand for labour. Instead countries should learn from Denmark’s 「flexicurity」 system, which lets firms hire and fire easily, while supporting unemployed workers as they retrain and look for new jobs. Benefits, pensions and health care should follow individual workers, rather than being tied (as often today) to employers.
此外，福利體系也得更新，才能讓崗位轉換更為順利，並在勞動者們學習新技能時為其提供支持。作為備受吹捧的萬能藥，無視個人情況而直接支付給每個人的「基本收入」是一種方案。不過，倘若沒有有力證據顯示這次技術革命不同於以往的許多次，的確吞噬了人力需求的話，「基本收入」的方案也沒有什麼意義。而丹麥的「流動-穩定」體系（flexicurity，flexibility and security）不僅讓公司招聘和解聘更為便捷，還同時在下崗人員再深造或找工作時提供支持，倒是值得各國借鑑。津貼、養老金、醫保等福利應該與勞動者個人掛鈎，而不是掛靠在僱主身上（正如目前大多數情況）。
我們繼續看AI-Spy文章的結尾段
13. The march of AI into the workplace calls for trade-offs between privacy and performance. A fairer, more productive workforce is a prize worth having, but not if it shackles（阻撓） and de-humanises employees. Striking a balance will require thought, a willingness for both employers and employees to adapt, and a strong dose of humanity.
人工智慧進軍職場需要實現保護個人隱私和提高員工工作效率之間的平衡。一個更加公平，高效的職場是我們值得擁有的，但是，如果它成為員工的枷鎖，抹殺了人性的話，就不應該得以存在。要實現這種平衡，我們需要用人單位和員工的深思熟慮以及對雙方適應的意願，同時，還需要更多人性的思索。
我們看看《人工智慧的未來有史為鑑——社會又該如何應對》結尾段
Despite the march of technology, there is little sign that industrial-era education and welfare systems are yet being modernised and made flexible. Policy makers need to getgoing now because, the longer they delay, the greater the burden on the welfare state. John Stuart Mill wrote in the 1840s that 「there cannot be a more legitimate object of the legislator’s care」 than looking after those whose livelihoods are disrupted by technology. That was true in the era of the steam engine, and it remains true in the era of artificial intelligence.
儘管科技在不斷進步，但沒有跡象表明，工業時代的教育和福利體系也實現了現代化，變得靈活了。決策者們需要馬上行動起來，因為拖延的時間越長，福利方面的負擔也就越大。約翰·穆勒（John Stuart Mill）在上世紀40年代曾經寫道，去照顧那些生活因為科技進步而陷入困境的人們，「才是立法者們最應該考慮的立法目標」。在蒸汽動力時代的確如此，而在人工智慧時代，它依舊是顛撲不破的真理。
我們也在學習一下《AI 遠大前程》 結尾段：
A longer-term concern is the way AI creates a virtuous circle （良性循環）or 「flywheel」 effect, allowing companies that embrace（採取） it to operate more efficiently, generate more data, improve their services, attract more customers and offer lower prices. That sounds like a good thing, but it could also lead to more corporate concentration and monopoly power—as has already happened in the technology sector.官方譯文：從更長遠的視角看，我們要擔憂AI將創造良性循環或「慣性輪」效應：它會使那些採納它的企業更高效地運營，生成更多數據，改善服務，吸引到更多客戶，提供更低的價格。這聽起來像是一件好事，但它會導致更多企業整合和壟斷——就像科技領域已經發生的那樣。