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自動化された採用選考についてのセミナー

名前:日付:

教授と2人の学生が、自動化されたツールが求人への応募書類を公平に振り分けられるのか、そして組織にはどのような安全策が必要になるのかを議論します。

音声と答え合わせ：https://joy-english.com/ja/listening/hiring-software-seminar

態度、意見、推論

聞いて、最も適切な答えを選びましょう。直接述べられていない答えもあります。

What is Marco's main criticism of the company?
- a) It used a program that was too slow
- b) It refused to hire any new staff
- c) It ignored its own past decisions completely
- d) It treated the software as objective without questioning how it learned
What point is Lin making about human recruiters?
- a) They are always faster than software
- b) They are also far from neutral, and a program can at least be checked
- c) They should never review applications
- d) They have no influence on the final decision
What does the figure of seven seconds suggest to Lin?
- a) That people judge applications very quickly
- b) That programs read applications more slowly
- c) That most applications are rejected unread
- d) That recruiters are overworked
What prevented the company from auditing the tool?
- a) The company did not employ enough staff
- b) The tool was too new to be tested
- c) The supplier would not reveal how it worked
- d) Applicants refused to give their data
Why does Marco call the lack of explanation "the real scandal"?
- a) It made the company lose money
- b) Rejected applicants had no way to challenge the decisions
- c) It broke the supplier's contract
- d) It meant too many people were hired
What does Lin mean by saying efficiency is "mistaken for fairness"?
- a) Fair tools are always the most efficient
- b) Efficient firms treat applicants better
- c) Fairness is easier to measure than speed
- d) A tool that saves effort is wrongly assumed to be just
What reservation does Marco express about testing?
- a) Firms only look for biases they already know about
- b) Testing is too expensive for most firms
- c) Tools cannot be tested before they are used
- d) Testing always proves that a tool is fair
What does the professor imply about a human decision at the end of the process?
- a) It is unnecessary if the program is accurate
- b) It should be carried out by the supplier
- c) It is valuable only if the person genuinely judges the result
- d) It always prevents unfairness

細かい点を聞き取る

もう一度聞いて、抜けている単語または数字を入力しましょう。数字は単語でも算用数字でも書けます。

The software had learned from ___ years of past hiring decisions.
Reviewers spent an average of ___ seconds on each application.
The supplier described its method as a trade ___.
Lin describes ___ applications in a week as a cost problem.
Each student must draft a ___-page policy.

解答

態度、意見、推論

d) It treated the software as objective without questioning how it learnedソフトウェアは過去の決定から学び、そこにあるどんなパターンもそのまま再現しただけなので、客観的だと呼ぶのは軽率だったと述べています。
b) They are also far from neutral, and a program can at least be checked人間も決して中立ではなく、即断がその例であり、プログラムは監査できると述べています。
a) That people judge applications very quickly人間の審査担当者による即断がたくさんあったことを示唆していると述べています。
c) The supplier would not reveal how it worked納入業者は自社の手法は企業秘密だと言ったので、会社は不採用の理由を説明できませんでした。
b) Rejected applicants had no way to challenge the decisions説明がなければ、不採用になった応募者はその決定に異議を唱えられないと述べています。
d) A tool that saves effort is wrongly assumed to be justそのツールはコストの問題を解決するが、それが公平であることを誰も示す必要がなかったと述べています。
a) Firms only look for biases they already know about慎重にテストする企業もあるが、すでに知っている偏りについてだけだと述べています。
c) It is valuable only if the person genuinely judges the result単に承認をクリックするだけの人はあまり安全策にならないので、その人が実際にどんな役割を担うかが重要だと述べています。

細かい点を聞き取る

ten / 10Marco は、過去10年分の決定から学んだと述べています。
seven / 7Lin は、平均7秒という数字を挙げています。
secret納入業者は、その手法は企業秘密だと言いました。
ten thousand / 10000 / 10,000Lin は、1週間に1万件の応募はコストの問題であり、このツールがそれを解決すると述べています。
one / 1 / single教授は、それぞれに1ページの方針案を作成するよう求めています。

スクリプト

Professor Siddiqui: Last week we looked at how automated tools sort job applications. This week I'd like your reactions to the case study. Marco, you've read it. Was the company wrong to use the system?

Marco: Not necessarily wrong, though I'd say careless. The company claimed the software was objective because it had no opinions. But it had learned from ten years of past hiring decisions, and if those decisions favoured a certain kind of candidate, the system simply reproduced the pattern, only faster.

Lin: I'd push back a little. Human recruiters are hardly neutral. One study in the reading found that people reviewing applications spent an average of seven seconds on each one, which suggests plenty of snap judgements. At least a program can be audited.

Professor Siddiqui: An important distinction. Can it be audited in practice?

Lin: That depends on whether anyone is allowed to look inside. In this case the supplier refused, saying the method was a trade secret, so the company couldn't explain its own rejections.

Marco: Which seems to me the real scandal. Had the company been able to explain a decision, a rejected applicant could have challenged it. As it was, people were turned away by a process nobody could describe.

Professor Siddiqui: So the objection is not to the technology as such, but to its opacity.

Lin: Mostly, yes, though I'd add that efficiency is being mistaken for fairness. Ten thousand applications in a week is a cost problem, and the tool solves the cost problem. Whether it's fair is a separate matter that nobody was obliged to answer.

Marco: I'm not sure I'd go quite that far. Some firms do test their tools carefully. The difficulty is that they test for the biases they already know about.

Professor Siddiqui: Which brings us to your assignment. I'd like each of you to draft a one-page policy for a small organisation that wants to use such a tool. I don't want a ban, and I don't want blind enthusiasm. What safeguards would you require? Think about who should be able to see the results, how often the system should be reviewed, and what an applicant is entitled to ask.

Lin: Can the policy include a human decision at the end?

Professor Siddiqui: Certainly, but be specific about what that person actually does. A human who merely clicks approve on a machine's recommendation is not much of a safeguard.