本篇文章段落比較多，全文一共有 14 段，前後兩半的結構屬於問題—解決方案類型。前半部分講解技術為工作者帶來的挑戰，包括對技術的依賴、工作崗位的減少等。後半部分則闡述另一種觀點，認為技術的影響並不如前面所說的如此嚴重，只要採取合適的政策就可以解決問題。

本篇考題英文原文與對應之中文翻譯整理如下。練習作答解題時若有對語意不清楚之處，請仔細查閱對照，以提升閱讀理解能力。

The Future of Work 未來的工作

1. 人工智慧進入職場

   According to a leading business consultancy, 3-14% of the global workforce will need to switch to a different occupation within the next 10-15 years, and all workers will need to adapt as their occupations evolve alongside increasingly capable machines. Automation – or ’embodied artificial intelligence’ (AI) – is one aspect of the disruptive effects of technology on the labour market. ‘Disembodied AI’, like the algorithms running in our smartphones, is another.

   據一家主要商業咨詢公司所稱，在未來 10-15 年內，3-14% 的全球勞動力將需要轉向不同的職業，而所有工人都需要適應，因為他們的職業將與能力越來越強的機器一起演變。自動化—或 “看得見的人工智慧”（AI）—是技術對勞動力市場破壞性影響的一個方面。 “看不見的人工智慧”，如我們智慧型手機中運行的算法，是另一個方面。

2. 知識經濟

   Dr Stella Pachidi from Cambridge Judge Business School believes that some of the most fundamental changes are happening as a result of the ‘algorithmication’ of jobs that are dependent on data rather than on production – the so-called knowledge economy. Algorithms are capable of learning from data to undertake tasks that previously needed human judgement, such as reading legal contracts, analysing medical scans and gathering market intelligence.

   劍橋大學賈吉商學院的斯特拉-帕奇迪博士認為，一些最根本的變化是工作內容 “演算法化” 的結果，即工作依賴於資料而非生產，也就是所謂的知識經濟。算法能夠從數據中學習，承擔以前需要人類判斷的任務，如閱讀法律合同、分析醫療影像和收集市場情報。

3. 機構選用演算法

   ‘In many cases, they can outperform humans,’ says Pachidi, ‘Organisations are attracted to using algorithms because they want to make choices based on what they consider is “perfect information”, as well as to reduce costs and enhance productivity.’

   帕奇迪說： “在許多情況下，它們可以勝過人類。” 組織機構被吸引去使用演算法，因為他們希望根據他們認為的 “完美資訊” 做出選擇，以及降低成本和提高生產力。

4. 人工智慧的代價

   ‘But these enhancements are not without consequences,’ says Pachidi. ‘If routine cognitive tasks are taken over by AI, how do professions develop their future experts?’ she asks. ‘One way of learning about a job is “legitimate peripheral participation” – a novice stands next to experts and learns by observation. If this isn’t happening, then you need to find new ways to learn.’

   帕奇迪說： “但這些功能提升並非沒有代價。” 她問道：“如果日常的認知任務都被人工智慧所取代，那麼各行業如何培養他們未來的專家？工作的一種學習方式是 “合法的周邊參與”—一個新手站在專家旁邊，通過觀察來學習。如果這種方式消失的話，那麼你就得尋找新的學習方法。

5. 影響勞動方式

   Another issue is the extent to which the technology influences or even controls the workforce. For over two years, Pachidi monitored a telecommunications company. ‘The way telecoms salespeople work is through personal and frequent contact with clients, using the benefit of experience to assess a situation and reach a decision. However, the company had started using a[n]…algorithm that defined when account managers should contact certain customers about which kinds of campaigns and what to offer them.’

   另一個問題是技術對勞動力的影響程度，甚至是控制程度。帕奇迪觀察一家電信公司兩年多的時間。電信銷售人員的工作方式是通過個人與客戶的頻繁接觸，利用經驗優勢來評估情況並作出決定。然而，該公司已經開始使用一種……演算法，定義客服經理應該何時該為哪種促銷與某些客戶聯繫，及為他們提供什麼。

6. 學習方式丕變

   The algorithm – usually built by external designers – often becomes the keeper of knowledge, she explains. In cases like this, Pachidi believes, a short-sighted view begins to creep into working practices whereby workers learn through the ‘algorithm’s eyes’ and become dependent on its instructions. Alternative explorations–where experimentation and human instinct lead to progress and new ideas–are effectively discouraged.

   她解釋說，演算法—通常是由外部設計師所建立的—往往成為知識的保管員。帕奇迪認為，在這樣的情況下，一種短視的觀點開始悄悄進入工作實踐中，員工通過 “演算法的眼睛” 來學習，並變得依賴其指令。另類的探索—實驗和人類的本能導致進步和新的想法—實際上也就被拒於門外了。

7. 員工取巧

   Pachidi and colleagues even observed people developing strategies to make the algorithm work to their own advantage.’We are seeing cases where workers feed the algorithm with false data to reach their targets,’ she reports.

   帕奇迪和同事們甚至觀察到人們制定策略，使演算法對他們自己有利。“我們看到的情況是，員工為達到他們的目標而向演算法提供虛假數據，” 她說。

8. 人工智慧需透明

   It’s scenarios like these that many researchers are working to avoid. Their objective is to make AI technologies more trustworthy and transparent, so that organisations and individuals understand how AI decisions are made. In the meantime, says Pachidi,’ We need to make sure we fully understand the dilemmas that this new world raises regarding expertise, occupational boundaries and control.’

   許多研究人員正努力避免這樣的情況發生。他們的目標是使人工智慧技術更加值得信賴和透明，以便組織和個人瞭解人工智慧的決策是如何做出的。同時，帕奇迪說：“我們需要確保我們充分理解這個新世界在專業知識、職業界限和控制方面引起的困境。”

9. 新時代的工作樣態

   Economist Professor Hamish Low believes that the future of work will involve major transitions across the whole life course for everyone: ‘The traditional trajectory of full-time education followed by full-time work followed by a pensioned retirement is a thing of the past,’ says Low. Instead, he envisages a multistage employment life: one where retraining happens across the life course, and where multiple jobs and no job happen by choice at different stages.

   經濟學家 Hamish Low 教授認為，未來的工作將涉及每個人整個生命過程中的重大轉變：“傳統的全職教育，然後是全職工作，最後是領取養老金退休的軌跡已經成為過去，” Low 說。相反，他設想了一個多階段的就業生活：一個在整個生命過程中進行再培訓的生活，以及在不同階段選擇多種工作或不工作的生活。

10. 失業率變化

    On the subject of job losses, Low believes the predictions are founded on a fallacy: “It assumes that the number of jobs is fixed. If in 30 years, half of 100 jobs are being carried out by robots, that doesn’t mean we are left with just 50 jobs for humans. The number of jobs will increase: we would expect there to be 150 jobs.’

    關於失業的問題，Low 認為預測是建立在一個謬誤之上：“它假設工作的數量是固定的。如果 30 年後，100 個工作中的一半由機器人來完成，這並不意味著我們只剩下 50 個工作給人類。工作崗位的數量會增加：我們會期望有 150 個工作崗位。”

11. 法律才是元兇

    Dr Ewan McGaughey, at Cambridge’s Centre for Business Research and King’s College London, agrees that ‘apocalyptic’ views about the future of work are misguided. ‘It’s the laws that restrict the supply of capital to the job market, not the advent of new technologies that causes unemployment.

    劍橋大學商業研究中心和倫敦國王學院的 Ewan McGaughey 博士也認為，關於未來工作的 “世界末日” 觀點是錯誤的。是法律限制了就業市場的資本供應，而不是新技術的出現導致了失業。

12. 教育訓練

    His recently published research answers the question of whether automation, AI and robotics will mean a ‘jobless future’ by looking at the causes of unemployment. ‘History is clear that change can mean redundancies. But social policies can tackle this through retraining and redeployment.’

    他最近發表的研究透過研究失業的原因，回答了自動化、人工智慧和機器人技術是否意味著 “無工作的未來” 的問題。“歷史很清楚，變革可能意味著裁員。但社會政策可以通過再培訓和重新部署來解決這個問題。”

13. 防範於未然

    He adds: ‘If there is going to be change to jobs as a result of AI and robotics then I’d like to see governments seizing the opportunity to improve policy to enforce good job security. We can “reprogramme” the law to prepare for a fairer future of work and leisure.’ McGaughey’s findings are a call to arms to leaders of organisations, governments and banks to pre-empt the coming changes with bold new policies that guarantee full employment, fair incomes and a thriving economic democracy.

    他補充說：“如果由於人工智慧和機器人技術的出現，工作將發生變化，那麼我希望看到政府抓住機會，改善政策，實施良好的工作保障。我們可以對法律進行 “重新編程”，為一個更公平的工作和休閒的未來做準備。McGaughey 的研究結果是對組織、政府和銀行等各界領導人的呼籲，以大膽的新政策預先阻止即將發生的變化，保證充分就業、公平收入和繁榮的經濟民主。

14. 社會革命

    ‘The promises of these new technologies are astounding. They deliver humankind the capacity to live in a way that nobody could have once imagined,’ he adds. ‘Just as the industrial revolution brought people past subsistence agriculture, and the corporate revolution enabled mass production, a third revolution has been pronounced. But it will not only be one of technology. The next revolution will be social.’

    “這些新技術帶來的前景令人震驚。它們為人類提供了以前沒有人能夠想像到的方式生活的能力，” 他補充說。正如工業革命使人們擺脫了自給自足的農業，而企業革命使大規模生產成為可行一樣，第三次革命已經宣告來到。但它將不僅僅是技術的革命。下一次革命將是社會革命。