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Un seminario sulla selezione automatizzata dei candidati

Nome:Data:

Una professoressa e due studenti discutono se gli strumenti automatizzati possano selezionare le candidature in modo equo e quali garanzie servirebbero a un'organizzazione.

Audio e correzione delle risposte: https://joy-english.com/it/listening/hiring-software-seminar

Atteggiamento, opinione e inferenza

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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

Ascoltare i dettagli

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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.

Soluzioni

Atteggiamento, opinione e inferenza

d) It treated the software as objective without questioning how it learnedDice che il software ha imparato dalle decisioni passate e ha semplicemente riprodotto ogni schema presente in esse, quindi chiamarlo oggettivo è stato imprudente.
b) They are also far from neutral, and a program can at least be checkedDice che gli esseri umani sono tutt'altro che neutrali, citando i giudizi affrettati, e che un programma può essere sottoposto a verifica.
a) That people judge applications very quicklyDice che suggerisce molti giudizi affrettati da parte dei revisori umani.
c) The supplier would not reveal how it workedIl fornitore ha detto che il suo metodo era un segreto commerciale, quindi l'azienda non poteva spiegare i propri rifiuti.
b) Rejected applicants had no way to challenge the decisionsDice che, senza una spiegazione, un candidato respinto non potrebbe contestare la decisione.
d) A tool that saves effort is wrongly assumed to be justDice che lo strumento risolve il problema dei costi, ma nessuno ha dovuto dimostrare che sia equo.
a) Firms only look for biases they already know aboutDice che alcune aziende testano con cura, ma solo i pregiudizi che già conoscono.
c) It is valuable only if the person genuinely judges the resultDice che chi si limita a cliccare su approva non è una gran garanzia, quindi conta il ruolo effettivo della persona.

Ascoltare i dettagli

ten / 10Marco dice che ha imparato da dieci anni di decisioni passate.
seven / 7Lin cita una media di sette secondi.
secretIl fornitore ha detto che il metodo era un segreto commerciale.
ten thousand / 10000 / 10,000Lin dice che diecimila candidature in una settimana sono un problema di costi, che lo strumento risolve.
one / 1 / singleLa professoressa chiede a ciascuno di loro di redigere una policy di una pagina.

Trascrizione

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.