EigoPro 準1級 読解 — The Ethics of Artificial Intelligence

When an algorithm decides who is granted a loan, flagged for additional security screening, or recommended for parole, the stakes of its mistakes are no longer abstract. As artificial intelligence migrates from laboratories into the machinery of everyday life, three intertwined questions have come to dominate the ethical debate: whether such systems are fair, whether anyone can be held responsible when they err, and whether their reasoning can be understood at all. The problem of bias is the most insidious because it is rarely deliberate. A model learns from historical data, and history is seldom impartial. If past hiring decisions favoured one group, an algorithm trained on them will faithfully reproduce that preference while wrapping it in the reassuring language of objectivity. The machine does not invent prejudice; it launders it, lending old inequalities the authority of mathematics. Worse, because the bias is buried in millions of parameters, those affected may never know why they were rejected. This opacity feeds directly into the second dilemma: accountability. When a self-driving car causes a fatal collision, who is at fault—the manufacturer, the programmer, the owner, or the system itself? Traditional notions of responsibility assume a human agent who chose to act, yet a neural network's "decisions" emerge from statistical correlations no single person fully designed. The temptation is to treat the algorithm as a scapegoat, a convenient entity to blame precisely because it cannot be punished. The most promising response is transparency. Researchers in "explainable AI" are building systems that can justify their conclusions in terms a human can audit. Critics counter that a plausible-sounding explanation is not the same as a true one, and that demanding interpretability may force engineers to abandon their most accurate models. Yet the alternative—delegating consequential decisions to processes no one can scrutinise—is a bargain few societies should be willing to accept.

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この単元の問題を3問、解説つきで公開しています

準1級 · 読解 · 全5問

問 1

What is the main idea of the passage?

  • Self-driving cars are the single greatest ethical danger posed by modern technology.
  • Engineers should abandon all complex models that cannot be fully explained to the public.
  • As AI takes on consequential decisions, society must grapple with whether such systems are fair, accountable, and intelligible.
  • Artificial intelligence has become so accurate that ethical concerns about it are largely obsolete.
Phrase

whether such systems are fair, whether anyone can be held responsible ... and whether their reasoning can be understood at all

準1級読解テクノロジー倫理主旨把握
⚡ひとことで

本文は AI の3大倫理問題=公平性 (bias)・説明責任 (accountability)・透明性 (transparency) を順に論じる。

選択肢の解説Choice Analysis

「As AI takes on consequential decisions, society must grapple with whether such systems are fair, accountable, and intelligible.」

正解

第1段で『AI が日常生活の機構に入り込むにつれ、(1) 公平か (2) 誤った時に責任を負えるか (3) その推論が理解できるか の3つの問いが倫理論争を支配する』と明示する。以降の3段が順にこの3点を扱う構成なので、『重大な決定を担うAIに対し社会は公平性・説明責任・理解可能性を問わねばならない』が主旨。

  • 「Artificial intelligence has by now become so accurate that ethical concerns about it are largely obsolete.」

    = AIは正確になり倫理的懸念は無用 ━ 本文はむしろ倫理問題が『支配的になった』と述べており正反対。

  • 「Self-driving cars are by far and away the single greatest ethical danger that is posed by any modern technology.」

    = 自動運転車が最大の危険 ━ 自動運転車は accountability の一例に過ぎず、3問題の1つの例示にすぎない。

  • 「Engineers ought to abandon every one of the complex models that cannot be fully explained to the general public.」

    = 複雑なモデルを全廃すべき ━ 本文は interpretability を求めると精度を犠牲にする恐れに『触れる』だけで全廃を主張していない。

▦Collocation
パターン意味例
the stakes of ~~の重大さ・賭け金the stakes of its mistakes
migrate from A into BAからBへ移行するAI migrates from laboratories into everyday life
come to dominate ~~を支配するに至るquestions that come to dominate the debate
be held responsible責任を問われるanyone can be held responsible
intertwined questions絡み合った問いthree intertwined questions
❝例文

  • As AI migrates into everyday life, the stakes of its errors rise.

    AIが日常生活に入り込むにつれ、その誤りの重大さは増す。

  • Three intertwined questions came to dominate the debate.

    3つの絡み合った問いが論争を支配するに至った。

  • No one could be held responsible for the outcome.

    その結果について誰も責任を問えなかった。

💡Tip · 覚えるコツ

主旨設問は第1段の問題提起文 (三つ並列の whether ...) を探す。本文の構成は『3問題の提示→各段で1つずつ展開』の典型型。

問 2

According to the passage, why is algorithmic bias described as especially insidious?

  • Programmers deliberately encode their personal prejudices into every model.
  • It only affects groups that were already aware they were being discriminated against.
  • It usually arises unintentionally from historical data and is then disguised as objective.
  • It can be easily detected and removed by inspecting a few key parameters.
Phrase

The problem of bias is the most insidious because it is rarely deliberate.

準1級読解テクノロジー倫理詳細把握
⚡ひとことで

バイアスは意図的でなく、過去データから生じ『客観性の装い』で覆われるから陰険。

選択肢の解説Choice Analysis

「It usually arises unintentionally from historical data and is then disguised as objective.」

正解

第2段は『bias は意図的であることが稀 (rarely deliberate) だから最も陰険』とし、過去の偏った決定を学習した model がそれを『客観性という安心させる言葉で包む (wrapping it in the reassuring language of objectivity)』と説明する。よって『過去データから無意識に生じ、客観的に見せかけられる』が正解。

  • 「Programmers deliberately encode their personal prejudices」

    = プログラマが故意に偏見を組み込む ━ 本文は『故意ではなく rarely deliberate』と明言しており逆。

  • 「only affects groups that were already aware」

    = 既に自覚した集団だけに影響 ━ 本文は『なぜ拒否されたか当事者は知り得ない (may never know)』とあり逆。

  • 「easily detected and removed by inspecting a few key parameters」

    = 少数のパラメータ点検で容易に除去 ━ 本文は『何百万のパラメータに埋もれる (buried in millions of parameters)』と困難さを強調。

▦Collocation
パターン意味例
rarely deliberateほとんど意図的でないbias is rarely deliberate
learn from ~~から学習するA model learns from historical data.
reproduce a preference選好を再現するIt will reproduce that preference.
wrap A in BAをBで包むwrapping it in the language of objectivity
be buried in ~~に埋もれているbias buried in millions of parameters
❝例文

  • The model faithfully reproduced the old preference.

    そのモデルは古い選好を忠実に再現した。

  • The flaw was buried in millions of parameters.

    その欠陥は何百万ものパラメータに埋もれていた。

  • It wrapped prejudice in the language of objectivity.

    それは偏見を客観性の言葉で包んだ。

💡Tip · 覚えるコツ

detail 設問は本文語を別語に置換した肢が正解 (rarely deliberate→unintentionally, reassuring language of objectivity→disguised as objective)。極端な only / easily を含む肢は要注意。

問 3

What does the passage suggest is the difficulty in assigning accountability for a self-driving car's fatal collision?

  • Neural networks are programmed line by line by a single identifiable engineer.
  • Manufacturers have already agreed to accept full liability for all accidents.
  • The law clearly assigns blame to the car's owner in every case.
  • Responsibility traditionally presumes a human chooser, yet the system's decisions emerge from correlations no individual fully designed.
Phrase

Traditional notions of responsibility assume a human agent who chose to act, yet a neural network's "decisions" emerge from statistical correlations no single person fully designed.

準1級読解テクノロジー倫理詳細把握
⚡ひとことで

従来の責任論は『行為を選んだ人間』を前提するが、AIの決定は誰も設計しきれない相関から生じる。

選択肢の解説Choice Analysis

「Responsibility traditionally presumes a human chooser, yet the system's decisions emerge from correlations no individual fully designed.」

正解

第3段は『従来の責任概念は、行為を選んだ人間 (a human agent who chose to act) を前提とする』が、neural network の決定は『誰一人完全には設計していない統計的相関から生じる (emerge from statistical correlations no single person fully designed)』と述べる。この食い違いこそ責任帰属の難しさで、それを言い換えた肢が正解。

  • 「The law as it currently stands clearly assigns all of the blame to the car's registered owner in every single case.」

    = 法は常に所有者に責任を課す ━ 本文は誰に責任があるか不明 (who is at fault) と問うており『明確に所有者』とは述べていない。

  • 「Neural networks of this kind are all programmed line by line by one single and identifiable engineer at the company.」

    = 1人の技術者が逐行で記述 ━ 本文は『誰一人完全には設計していない』とあり正反対。

  • 「The manufacturers have all already agreed among themselves to accept full legal liability for every accident that occurs.」

    = メーカーが全責任を受諾済み ━ 本文にそのような合意の記述はない。

▦Collocation
パターン意味例
feed into ~~につながる・流れ込むOpacity feeds into accountability.
be at fault落ち度があるwho is at fault?
assume a human agent人間の行為主体を前提とするnotions that assume a human agent
emerge from ~~から生じるdecisions emerge from correlations
treat A as a scapegoatAを身代わりにするtreat the algorithm as a scapegoat
❝例文

  • It was unclear who was at fault for the collision.

    その衝突で誰に落ち度があるか不明だった。

  • The decision emerged from statistical correlations.

    その決定は統計的相関から生じた。

  • They treated the system as a convenient scapegoat.

    彼らはそのシステムを都合のよい身代わりにした。

💡Tip · 覚えるコツ

別段落の detail 設問は段落の topic 文 (ここでは accountability の段) を特定してから根拠文を探す。yet / but の後に対比の核心が来る型。

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