Joy English · Level C1 · Ethics and technologyjoy-english.com

A seminar on automated job screening

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A professor and two students debate whether automated tools can sort job applications fairly, and what safeguards an organisation would need.

Audio and answer checking: https://joy-english.com/listening/hiring-software-seminar

Attitude, opinion and inference

Listen and choose the best answer. Some answers are not stated directly.

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

Listen for details

Listen again and type the missing word or number. You can write numbers as words or digits.

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.

Answer key

Attitude, opinion and inference

d) It treated the software as objective without questioning how it learnedHe says the software learned from past decisions and simply reproduced any pattern in them, so calling it objective was careless.
b) They are also far from neutral, and a program can at least be checkedShe says humans are hardly neutral, citing snap judgements, and that a program can be audited.
a) That people judge applications very quicklyShe says it suggests plenty of snap judgements by human reviewers.
c) The supplier would not reveal how it workedThe supplier said its method was a trade secret, so the company could not explain its rejections.
b) Rejected applicants had no way to challenge the decisionsHe says that, without an explanation, a rejected applicant could not challenge the decision.
d) A tool that saves effort is wrongly assumed to be justShe says the tool solves the cost problem, but nobody had to show that it is fair.
a) Firms only look for biases they already know aboutHe says some firms test carefully, but only for the biases they already know about.
c) It is valuable only if the person genuinely judges the resultShe says someone who merely clicks approve is not much of a safeguard, so the person's actual role matters.

Listen for details

ten / 10Marco says it learned from ten years of past decisions.
seven / 7Lin cites an average of seven seconds.
secretThe supplier said the method was a trade secret.
ten thousand / 10000 / 10,000Lin says ten thousand applications in a week is a cost problem, which the tool solves.
one / 1 / singleThe professor asks each of them to draft a one-page policy.

Transcript

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.