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Table of contents
- Federal election: how did we get it so wrong? - Between The Lines - ABC Radio National
- Total Employee Recognition & Employee Engagement
- Secondary Menu
How Zara and Mike's ability to live a 'normal' life while bagging lucrative Snow hits as Britain shivers amid 70mph winds today with warnings of a four-inch deluge of rain on way for Boris Johnson speaks to Iranian president Hassan Rouhani and demands 'an end to hostilities' in the Gulf The man who presided over ruin of British Airways' reputation steps down: Airline owner's chief executive Hope for thousands of women who endure the heartbreak of recurrent miscarriages as 'diabetes drug can help Female paedophile, 24, is jailed for 30 months over two-month lesbian affair with vulnerable year-old Former English National Ballet principal dancer, 48, 'groped three young women while working as private Transgender barber, 25, hanged himself while transitioning to become a man after suffering with a 'low mood' Netflix is ordered to stop showing film portraying Jesus as a gay man who introduces his 'boyfriend' to his North Korean mother 'faces prison for saving her children from a house fire instead of portrait of Kim What a jean-ius!
Mysterious 'hums' detected around the world are traced back to a magma-filled reservoir deep under the Hangman who will execute four Indian men who gang-raped and murdered a woman on a moving bus says killing Madame Tussauds separates Prince Harry and Meghan Markle's waxworks from the rest of the family following Revealed: Sussexroyal. Was Prince Harry's friend overhaul an early warning sign? How royal 'froze out' old pals like former wingman The Crown bosses 'doubt' they will cover Meghan and Harry quitting as royals because the Netflix drama will Meghan Markle and Prince Harry want to set up an eco charity focusing on 'the wellbeing of society and The power players who will help Prince Harry and Meghan Markle forge their new identity: Couple can call on Good causes that have Meghan and Harry as patrons face uncertain future as couple say they will create new Queen's ex-chef Darren McGrady launches blistering attack on 'fame-hungry' Meghan Markle and 'airhead' The world reacts to Megxit: Harry and Meghan are re-imagined as 'commoners' as European papers blast royal Royal experts blast Meghan and Harry's 'bizarre, impetuous' decision to step down saying it 'has echoes of Did Meghan Markle's power outfit hint at her big news?
Stylist reveals how the Duchess dons 'calm' camel A casual future Queen! A lot of discussion has revolved around whether there is evidence of a quid pro quo—whether Trump offered aid to Ukraine in exchange for assistance with his reelection campaign. Instead, it relies on an understanding between participants that is sensitive to nuances in tone of voice as well as a shared sense of the personal and political relationship between them and their countries.
Federal election: how did we get it so wrong? - Between The Lines - ABC Radio National
Communication depends on our ability to understand what is implied just as well as what is actually said. A numerical scale with a high number indicating high performance — as measured by the yearly performance review. A low number indicating poor performance and an arbitrary number — for example 0 or — indicating that the candidate was not hired.
In addition to the three mentioned above, many different targets were possible.
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The team designing the software needs to identify and weight the potential trade-offs explicitly. First of all, if the target is the selection for interview, the algorithm learns to replicate the decisions of the hiring managers for interview selection. However, the performance of the model will be, at best, equal to the human hiring managers. If the CV reviewers are biased against women or minorities, so will the model.
If the reviewers are poor at selecting high performing candidates, so will the model. That might be what happened at Amazon:. But by , the company realized its new system was not rating candidates for software developer jobs and other technical posts in a gender-neutral way. The algorithms are never sexist.
They do what we ask of them, and if we ask them to emulate sexist hiring managers, they do it without any hesitations. What about our other choices?
Total Employee Recognition & Employee Engagement
If we used the offer stage as a target, we give additional information to the model. Candidates that receive an offer must have been selected for interviews and then also done well in the discussions. One advantage of that approach is that the algorithm might learn to detect and screen candidates that are likely to do poorly in the interview.
The last and most interesting choice would be using a rating calculated from performance rating and other potential criteria. It seems that it is the option chosen by the Amazon team:. On paper, and assuming that data was available, it looks like a solid option. After all, the real objective of the company is to hire good employees, not necessarily to repeat past hiring practices — including past mistakes.
If we chose that option, the algorithm should learn to recognize in a resume the signals that are most predictive of employee performance as defined by the company. Does that seem like a perfect solution? There are still issues. Fallible humans conduct performance reviews. There might be gender bias, or worse. Employee ratings might not align perfectly with broader company goals such as team performance or public relations constraints.
A considerable body of management research shows that viewpoint diversity improves team performance. Increasing gender balance and minority representation might also be necessary for employee satisfaction and PR reasons, not to mention ethics. Those dimensions are independent of individual performance. How to construct our target variable to take those considerations into account? A possible solution is to build a specific score from all the variables of interest, including employee performance ratings.
Do you suspect that the ratings might be gender-biased? Boost the score of the discriminated gender. Do you need more employees from a particular demographic? Give bonus points to the score of the candidates of that demographic. The critical consideration is that your target score should align as closely as possible to the real company goals. That was easy to write.
Secondary Menu
Is it easy to do? Evidence points toward the Amazon team trying to use the scoring approach I just mentioned. The article says that the algorithms were grading the resumes on a 5 point scale, like a product review. What does that mean in practice? In the case of hiring — rating and ranking human beings — it can be very problematic. The team would need to document that parameter explicitly in the software code and its accompanying design documents.
One thing is sure: it goes much beyond technical design choices. Amazon could have easily corrected any gender bias in the past data by giving explicitly better ratings to female applicants. That is precisely the way that American colleges practice positive discrimination. The last consideration is also the most technical: the cost function.
Imagine the following example: a candidate was rated 1 star by the human reviewers, but the model falsely gives her a three stars rating. That an error of 2 stars. How much does it matter? Probably not much. An applicant with either a 1 star or 3-star rating is not going to be hired. The error is immaterial to the decision to be taken. However, consider another example: a candidate was rated five stars by the human reviewers, but the model gives her 3.
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However, this time, the error is very significant. The candidate should have been hired but will lose the job opportunity because of the lower rating.
What about the symmetrical case? How bad would it be to hire a 3. In more technical terms, should we give the same weight to false positives and false negatives? We are only using the absolute difference between the human-defined target score and the machine-predicted score. It is not a reasonable cost function in that setting.
It makes it hard to comment on the choices that were made, and whether or not they contributed to the ultimate failure of the project. Project failure is always multi-causal.