Tag: hr

Survival of the Best Fit: A webgame on AI in recruitment

Survival of the Best Fit: A webgame on AI in recruitment

Survival of the Best Fit is a webgame that simulates what happens when companies automate their recruitment and selection processes.

You – playing as the CEO of a starting tech company – are asked to select your favorite candidates from a line-up, based on their resumés.

As your simulated company grows, the time pressure increases, and you are forced to automate the selection process.

Fortunately, some smart techies working for your company propose training a computer to hire just like you just did.

They don’t need anything but the data you just generated and some good old supervised machine learning!

To avoid spoilers, try the game yourself and see what happens!

The game only takes a few minutes, and is best played on mobile.

www.survivalofthebestfit.com/ via Medium

Survival of the Best Fit was built by Gabor CsapoJihyun KimMiha Klasinc, and Alia ElKattan. They are software engineers, designers and technologists, advocating for better software that allows members of the public to question its impact on society.

You don’t need to be an engineer to question how technology is affecting our lives. The goal is not for everyone to be a data scientist or machine learning engineer, though the field can certainly use more diversity, but to have enough awareness to join the conversation and ask important questions.

With Survival of the Best Fit, we want to reach an audience that may not be the makers of the very technology that impact them everyday. We want to help them better understand how AI works and how it may affect them, so that they can better demand transparency and accountability in systems that make more and more decisions for us.

survivalofthebestfit.com

I found that the game provides a great intuitive explanation of how (humas) bias can slip into A.I. or machine learning applications in recruitment, selection, or other human resource management practices and processes.

If you want to read more about people analytics and machine learning in HR, I wrote my dissertation on the topic and have many great books I strongly recommend.

Finally, here’s a nice Medium post about the game.

https://www.survivalofthebestfit.com/game/

Note, as Joachin replied below, that the game apparently does not learn from user-input, but is programmed to always result in bias towards blues.
I kind of hoped that there was actually an algorithm “learning” in the backend, and while the developers could argue that the bias arises from the added external training data (you picked either Google, Apple, or Amazon to learn from), it feels like a bit of a disappointment that there is no real interactivity here.

People Analytics: Is nudging goed werkgeverschap of onethisch?

People Analytics: Is nudging goed werkgeverschap of onethisch?

In Dutch only:

Voor Privacyweb schreef ik onlangs over people analytics en het mogelijk resulterende nudgen van medewerkers: kleine aanpassingen of duwtjes die mensen in de goede richting zouden moeten sturen. Medewerkers verleiden tot goed gedrag, als het ware. Maar wie bepaalt dan wat goed is, en wanneer zouden werkgevers wel of niet mogen of zelfs moeten nudgen?

Lees het volledige artikel hier.

Books for the modern, data-driven HR professional (incl. People Analytics)

Books for the modern, data-driven HR professional (incl. People Analytics)

With great pleasure I’ve studied and worked in the field of people analytics, where we seek to leverage employee, management-, and business information to better organize and manage our personnel. Here, data has proven valuable itself indispensible for the organization of the future.

Data and analytics have not traditionally been high on the list of HR professionals. Fortunately, there is an increased awareness that the 21st century (HR) manager has to be data-savvy. But where to start learning? The plentiful available resources can be daunting…

Have a look at these 100+ amazing books
for (starting) people analytics specialists.
My personal recommendations are included as pictures,
but feel free to ask for more detailed suggestions!


Categories (clickable)

  • Behavioural Psychology: focus on behavioural psychology and economics, including decision-making and the biases therein.
  • Technology: focus on the implications of new technology….
    • Ethics: … on society and humanity, and what can go wrong.
    • Digital & Data-driven HR: … for the future of work, workforce, and organization. Includes people analytics case studies.
  • Management: focus on industrial and organizational psychology, HR, leadership, and business strategy.
  • Statistics: focus on the technical books explaining statistical concepts and applied data analysis.
    • People analytics: …. more technical books on how to conduct people analytics studies step-by-step in (statistical) software.
    • Programming: … technical books specifically aimed at (statistical) programming and data analysis.
  • Communication: focus on information exchange, presentation, and data visualization.

Disclaimer: This page contains links to Amazon’s book shop.
Any purchases through those links provide us with a small commission that helps to host this blog.

Behavioural Psychology books

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Technology books

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Ethics in Data & Machine Learning

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Digital & Data-driven HR

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Management books

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Statistics books

Applied People Analytics

Programming

You can find an overview of 20+ free programming books here.

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Data Visualization books

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A note of thanks

I want to thank the active people analytics community, publishing in management journals, but also on social media. I knew Littral Shemer Haim already hosted a people analytics reading list, and so did Analytics in HR (Erik van Vulpen) and Workplaceif (Manoj Kumar). After Jared Valdron called for book recommendation on people analytics on LinkedIn, and nearly 60 people replied, I thought let’s merge these overviews.

Hence, a big thank you and acknowledgement to all those who’ve contributed directly or indirectly. I hope this comprehensive merged overview is helpful.

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Animated vs. Static Data Visualizations

Animated vs. Static Data Visualizations

GIFs or animations are rising quickly in the data visualization world (see for instance here).

However, in my personal experience, they are not as widely used in business settings. You might even say animations are frowned by, for instance, LinkedIn, which removed the option to even post GIFs on their platform!

Nevertheless, animations can be pretty useful sometimes. For instance, they can display what happens during a process, like a analytical model converging, which can be useful for didactic purposes. Alternatively, they can be great for showing or highlighting trends over time.  

I am curious what you think are the pro’s and con’s of animations. Below, I posted two visualizations of the same data. The data consists of the simulated workforce trends, including new hires and employee attrition over the course of twelve months. 

versus

Would you prefer the static, or the animated version? Please do share your thoughts in the comments below, or on the respective LinkedIn and Twitter posts!


Want to reproduce these plots? Or play with the data? Here’s the R code:

# LOAD IN PACKAGES ####
# install.packages('devtools')
# devtools::install_github('thomasp85/gganimate')
library(tidyverse)
library(gganimate)
library(here)


# SET CONSTANTS ####
# data
HEADCOUNT = 270
HIRE_RATE = 0.12
HIRE_ADDED_SEASONALITY = rep(floor(seq(14, 0, length.out = 6)), 2)
LEAVER_RATE = 0.16
LEAVER_ADDED_SEASONALITY = c(rep(0, 3), 10, rep(0, 6), 7, 12)

# plot
TEXT_SIZE = 12
LINE_SIZE1 = 2
LINE_SIZE2 = 1.1
COLORS = c("darkgreen", "red", "blue")

# saving
PLOT_WIDTH = 8
PLOT_HEIGHT = 6
FRAMES_PER_POINT = 5


# HELPER FUNCTIONS ####
capitalize_string = function(text_string){
paste0(toupper(substring(text_string, 1, 1)), substring(text_string, 2, nchar(text_string)))
}


# SIMULATE WORKFORCE DATA ####
set.seed(1)

# generate random leavers and some seasonality
leavers <- rbinom(length(month.abb), HEADCOUNT, TURNOVER_RATE / length(month.abb)) + LEAVER_ADDED_SEASONALITY

# generate random hires and some seasonality
joiners <- rbinom(length(month.abb), HEADCOUNT, HIRE_RATE / length(month.abb)) + HIRE_ADDED_SEASONALITY

# combine in dataframe
data.frame(
month = factor(month.abb, levels = month.abb, ordered = TRUE)
, workforce = HEADCOUNT - cumsum(leavers) + cumsum(joiners)
, left = leavers
, hires = joiners
) ->
wf

# transform to long format
wf_long <- gather(wf, key = "variable", value = "value", -month)
capitalize the name of variables
wf_long$variable <- capitalize_string(wf_long$variable)


# VISUALIZE & ANIMATE ####
# draw workforce plot
ggplot(wf_long, aes(x = month, y = value, group = variable)) +
geom_line(aes(col = variable, size = variable == "workforce")) +
scale_color_manual(values = COLORS) +
scale_size_manual(values = c(LINE_SIZE2, LINE_SIZE1), guide = FALSE) +
guides(color = guide_legend(override.aes = list(size = c(rep(LINE_SIZE2, 2), LINE_SIZE1)))) +
# theme_PVDL() +
labs(x = NULL, y = NULL, color = "KPI", caption = "paulvanderlaken.com") +
ggtitle("Workforce size over the course of a year") +
NULL ->
workforce_plot

# ggsave(here("workforce_plot.png"), workforce_plot, dpi = 300, width = PLOT_WIDTH, height = PLOT_HEIGHT)

# animate the plot
workforce_plot +
geom_segment(aes(xend = 12, yend = value), linetype = 2, colour = 'grey') +
geom_label(aes(x = 12.5, label = paste(variable, value), col = variable),
hjust = 0, size = 5) +
transition_reveal(variable, along = as.numeric(month)) +
enter_grow() +
coord_cartesian(clip = 'off') +
theme(
plot.margin = margin(5.5, 100, 11, 5.5)
, legend.position = "none"
) ->
animated_workforce

anim_save(here("workforce_animation.gif"),
animate(animated_workforce, nframes = nrow(wf) * FRAMES_PER_POINT,
width = PLOT_WIDTH, height = PLOT_HEIGHT, units = "in", res = 300))

Animated Citation Gates turned into Selection Gates

Bret Beheim — senior researcher at the Max Planck Institute for Evolutionary Anthropology — posted a great GIF animation of the response to his research survey. He calls the figure citation gates, relating the year of scientific publication to the likelihood that the research materials are published open-source or accessible.

To generate the visualization, Bret used R’s base plotting functionality combined with Thomas Lin Pedersen‘s R package tweenrto animate it.

Bret shared his R code for the above GIF of his citation gates on GitHub. With the open source code, this amazing visual display inspired others to make similar GIFs for their own projects. For example, Anne-Wil Kruijt’s dance of the confidence intervals:

A spin-off of the citation gates: A gif showing confidence intervals of sample means.

Applied to a Human Resource Management context, we could use this similar animation setup to explore, for instance, recruitment, selection, or talent management processes.

Unfortunately, I couldn’t get the below figure to animate properly yet, but I am working on it (damn ggplot2 facets). It’s a quick simulation of how this type of visualization could help to get insights into the recruitment and selection process for open vacancies.

The figure shows how nearly 200 applicants — sorted by their age — go through several selection barriers. A closer look demonstrates that some applicants actually skip the screening and assessment steps and join via a fast lane in the first interview round, which could happen, for instance, when there are known or preferred internal candidates. When animated, such insights would become more clearly visible.

Univers Interview: “Algorithms haven’t replaced the HR manager yet”

Univers Interview: “Algorithms haven’t replaced the HR manager yet”

The magazine of Tilburg University — Univers — recently interviewed me on my PhD research on People Analytics and data-driven Human Resource management. The Dutch write-up by interviewer Ron Vaessen you can find here, but is unfortunately available in Dutch only.

The full text of my dissertation can be accessed in a flipbook here or downloaded directly via this link.

I have also dedicated several blogs to more background information. A small extract on the ethics of people analytics and machine learning in HR I posted here. Those interested in visualizing survival curves like I did can see this post. Curious about the cover design, read this post

Checklist to Optimize Training Transfer in Organizations

Checklist to Optimize Training Transfer in Organizations

Ashley Hughes, Stephanie Zajac, Jacqueline Spencer, and Eduardo Salas wrote a recent research note for the International Journal of Training and Development. The research note is build around an evidence-based checklist of actionable insights for practitioners that will help to enhance the effectiveness of training interventions. These actionable insights would help to prevent ‘transfer problem’, meaning that trained skills are not being used on the job. 


Screenshot of the first page of the published research note, containing the abstract

Unfortunately, these published academic papers are often behind a paywall, but you may request a PDF from the authors here on ResearchGate.

Screenshot of the appendix of the research note containing the checklist for practitioners.

For the full details and scientific evidence behind each suggested action, I suggest you access the research note. Nevertheless, here’s my summary of their main advice on improving training transfer before, during, and after training implementation:

Before training

  • Conduct a training needs analysis to align the training’s content and participants with the organizational objectives
  • Involved stakeholders should be aware of training, understand its importance, and — obviously — be prepared for the training program. The scholars provide seven specific actions here, including the setting of personal training goals, and aligning resources and rewards with the training.
  • Training attendance should be framed as an opportunity, and the training’s anticipated benefits could be emphasized (e.g. improvement of work processes or on-the-job performance).
  • A climate which encourages learning should be created, with dedicated time (and opportunities) for post‐training learning 
    and a sense of accountability for using trained knowledge, skills, and abilities.

During training

  • Piloting the training with a single department or subset of trainees is highly encouraged. This is one way that greatly helps to assess whether the training design is appropriate in terms of content and delivery.
  • Error‐encouragement framing can influence a trainee’s learning orientation and thus errors made during training should be framed as growth opportunities.

After training

  • Use of the trained skills should be supported and planned. For instance, participants could be given a small workload reduction to provide opportunities to apply the learned knowledge and skills once they return to their position. 
  • Management and training participants should be held accountable for their use of skills on the job.
  • Think about using just‐in‐time or refresher training and coaching, if needed.
  • Assess training effectiveness criteria including training transfer using metrics and analytics. Specifically, the scholars propose that the criteria measured in the training evaluation should correspond to the training needs identified through the training needs analysis that was conducted before the training. 
  • Training evaluation criteria should consider the scope and timeframe of the training. Take into account that distal outcomes such as ROI may take longer to realize.