Tag: wellbeing

Book tip: On the Clock

Book tip: On the Clock

Suppose you operate a warehouse where workers work 11-hour shifts. In order to meet your productivity KPIs, a significant number of them need to take painkillers multiple times per shift. Do you…

  1. Decrease or change the KPI (goals)
  2. Make shifts shorter
  3. Increase the number or duration of breaks
  4. Increase the medical staff
  5. Install vending machines to dispense painkillers more efficiently

Nobody in their right mind would take option 5… Right?

Yet, this is precisely what Amazon did according to Emily Guendelsberger in her insanely interesting and relevant book “On the clock(note the paradoxal link to Amazon’s webshop here).

Emily went undercover as employee at several organizations to experience blue collar jobs first-hand. In her book, she discusses how tech and data have changed low-wage jobs in ways that are simply dehumanizing.

These days, with sensors, timers, and smart nudging, employees are constantly being monitored and continue working (hard), sometimes at the cost of their own health and well-being.

I really enjoyed the book, despite the harsh picture it sketches of low wage jobs and malicious working conditions these days. The book poses several dilemma’s and asks multiple reflective questions that made me re-evaluate and re-appreciate my own job. Truly an interesting read!

Some quotes from the book to get you excited:

“As more and more skill is stripped out of a job, the cost of turnover falls; eventually, training an ever-churning influx of new unskilled workers becomes less expensive than incentivizing people to stay by improving the experience of work or paying more.”

Emily Guendelsberger, On the Clock

“Q: Your customer-service representatives handle roughly sixty calls in an eighty-hour shift, with a half-hour lunch and two fifteen-minute breaks. By the end of the day, a problematic number of them are so exhausted by these interactions that their ability to focus, read basic conversational cues, and maintain a peppy demeanor is negatively affected. Do you:

A. Increase staffing so you can scale back the number of calls each rep takes per shift — clearly, workers are at their cognitive limits

B. Allow workers to take a few minutes to decompress after difficult calls

C. Increase the number or duration of breaks

D. Decrease the number of objectives workers have for each call so they aren’t as mentally and emotionally taxing

E. Install a program that badgers workers with corrective pop-ups telling them that they sound tired.

Seriously—what kind of fucking sociopath goes with E?”

Emily Guendelsberger, On the Clock
My copy of the book
(click picture to order your own via affiliate link)

Cover via Freepik

How 457 data scientists failed to predict life outcomes

How 457 data scientists failed to predict life outcomes

This blog highlights a recent PNAS paper in which 457 data scientists and academic scholars were challenged use machine learning to predict life outcomes using a rich dataset.

Yet, I can not summarize the result better than this tweet by the author of the paper:

Over 750 scientific papers have used the Fragile Families dataset.

The dataset is famous for its richness of cohort (survey) data on the included families’ lives and their childrens’ upbringings. It includes a whopping 12.942 variables!!

Some of these variables reflect interesting life outcomes of the included families.

For instance, the childrens’ grade point averages (GPA) and grit, but also whether the family was ever evicted or experienced hardship, or whether their primary caregiver had received job training or was laid off at work.

You can read more about the exact data contents in the paper’s appendix.

A visual representation of the data
via pnas.org/content/pnas/117/15/8398/F1.medium.gif

Now Matthew and his co-authors shared this enormous dataset with over 160 teams consisting of 457 academics researchers and data scientists alike. Each of them well versed in statistics and predictive modelling.

These data scientists were challenged with this task: by all means possible, make the most predictive model for the six life outcomes (i.e., GPA, conviction, etc).

The scientists could use all the Fragile Families data, and any algorithm they liked, and their final model and its predictions would be compared against the actual life outcomes in a holdout sample.

According to the paper, many of these teams used machine-learning methods that are not typically used in social science research and that explicitly seek to maximize predictive accuracy.

Now, here’s the summary again:

If hundreds of [data] scientists created predictive algorithms with high-quality data, how well would the best predict life outcomes?

Not very well.

@msalganik

Even the best among the 160 teams’ predictions showed disappointing resemblance of the actual life outcomes. None of the trained models/algorithms achieved an R-squared of over 0.25.

Afbeelding
Via twitter.com/msalganik/status/1263886779603705856/photo/1

Here’s that same plot again, but from the original publication and with more detail:

Via pnas.org/content/117/15/8398

Wondering what these best R-squared of around 0.20 look like? Here’s the disappointg reality of plot C enlarged: the actual TRUE GPA’s on the x-axis, plotted against the best team’s predicted GPA’s on the y-axis.

Afbeelding
Via twitter.com/msalganik/status/1263886781449191424/photo/1

Sure, there’s some relationship, with higher actual scores getting higher (average) predictions. But it ain’t much.

Moreover, there’s very little variation in the predictions. They all clump together between the range of about 2.1 and 3.8… that’s not really setting apart the geniuses from the less bright!

Matthew sums up the implications quite nicely in one of his tweets:

For policymakers deploying predictive algorithms in high-stakes decisions, our result is a reminder of a basic fact: one should not assume that algorithms predict well. That must be demonstrated with transparent, empirical evidence.

@msalganik

According to Matthew this “collective failure of 160 teams” is hard to ignore. And it failure highlights the understanding vs. predicting paradox: these data have been used to generate knowledge on how the world works in over 750 papers, yet few checked to see whether these same data and the scientific models would be useful to predict the life outcomes we’re trying to understand.

I was super excited to read this paper and I love the approach. It is actually quite closely linked to a series of papers I have been working on with Brian Spisak and Brian Doornenbal on trying to predict which people will emerge as organizational leaders. (hint: we could not really, at least not based on their personality)

Apparently, others were as excited as I am about this paper, as Filiz Garip already published a commentary paper on this research piece. Unfortunately, it’s behind a paywall so I haven’t read it yet.

Moreover, if you want to learn more about the approaches the 160 data science teams took in modelling these life outcomes, here are twelve papers in which some teams share their attempts.

Very curious to hear what you think of the paper and its implications. You can access it here, and I’d love to read your comments below.