Tag: bias

2019 Shortlist for the Royal Society Prize for Science Books

2019 Shortlist for the Royal Society Prize for Science Books

Since 1988, the Royal Society has celebrated outstanding popular science writing and authors.

Each year, a panel of expert judges choose the book that they believe makes popular science writing compelling and accessible to the public.

Over the decades, the Prize has celebrated some notable winners including Bill Bryson and Stephen Hawking.

The author of the winning book receives £25,000 and £2,500 is awarded to each of the five shortlisted books. And this year’s shortlist includes some definite must-reads on data and statistics!

Infinite Powers – by Steven Strogatz

The captivating story of mathematics’ greatest ever idea: calculus. Without it, there would be no computers, no microwave ovens, no GPS, and no space travel. But before it gave modern man almost infinite powers, calculus was behind centuries of controversy, competition, and even death. 

Taking us on a thrilling journey through three millennia, Professor Steven Strogatz charts the development of this seminal achievement, from the days of Archimedes to today’s breakthroughs in chaos theory and artificial intelligence. Filled with idiosyncratic characters from Pythagoras to Fourier, Infinite Powers is a compelling human drama that reveals the legacy of calculus in nearly every aspect of modern civilisation, including science, politics, medicine, philosophy, and more.

https://royalsociety.org/grants-schemes-awards/book-prizes/science-book-prize/2019/infinite-powers/

Invisible Women – by Caroline Criado Perez

Imagine a world where your phone is too big for your hand, where your doctor prescribes a drug that is wrong for your body, where in a car accident you are 47% more likely to be seriously injured, where every week the countless hours of work you do are not recognised or valued. If any of this sounds familiar, chances are that you’re a woman.

Invisible Women shows us how, in a world largely built for and by men, we are systematically ignoring half the population. It exposes the gender data gap–a gap in our knowledge that is at the root of perpetual, systemic discrimination against women, and that has created a pervasive but invisible bias with a profound effect on women’s lives. From government policy and medical research, to technology, workplaces, urban planning and the media, Invisible Women reveals the biased data that excludes women.

https://royalsociety.org/grants-schemes-awards/book-prizes/science-book-prize/2019/invisible-women/

Six Impossible Things – by John Gribbin

This book does not deal with data or statistics specifically, but might even be more interesting, as it covers the topic of quantum physics:

Quantum physics is strange. It tells us that a particle can be in two places at once. That particle is also a wave, and everything in the quantum world can be described entirely in terms of waves, or entirely in terms of particles, whichever you prefer. 

All of this was clear by the end of the 1920s, but to the great distress of many physicists, let alone ordinary mortals, nobody has ever been able to come up with a common sense explanation of what is going on. Physicists have sought ‘quanta of solace’ in a variety of more or less convincing interpretations. 

This short guide presents us with the six theories that try to explain the wild wonders of quantum. All of them are crazy, and some are crazier than others, but in this world crazy does not necessarily mean wrong, and being crazier does not necessarily mean more wrong.

https://royalsociety.org/grants-schemes-awards/book-prizes/science-book-prize/2019/six-impossible-things/

The other shortlisted books

Artificial Stupidity – by Vincent Warmerdam @PyData 2019 London

Artificial Stupidity – by Vincent Warmerdam @PyData 2019 London

PyData is famous for it’s great talks on machine learning topics. This 2019 London edition, Vincent Warmerdam again managed to give a super inspiring presentation. This year he covers what he dubs Artificial Stupidity™. You should definitely watch the talk, which includes some great visual aids, but here are my main takeaways:

Vincent speaks of Artificial Stupidity, of machine learning gone HorriblyWrong™ — an example of which below — for which Vincent elaborates on three potential fixes:

Image result for paypal but still learning got scammed
Example of a model that goes HorriblyWrong™, according to Vincent’s talk.

1. Predict Less, but Carefully

Vincent argues you shouldn’t extrapolate your predictions outside of your observed sampling space. Even better: “Not predicting given uncertainty is a great idea.” As an alternative, we could for instance design a fallback mechanism, by including an outlier detection model as the first step of your machine learning model pipeline and only predict for non-outliers.

I definately recommend you watch this specific section of Vincent’s talk because he gives some very visual and intuitive explanations of how extrapolation may go HorriblyWrong™.

Be careful! One thing we should maybe start talking about to our bosses: Algorithms merely automate, approximate, and interpolate. It’s the extrapolation that is actually kind of dangerous.

Vincent Warmerdam @ Pydata 2019 London

Basically, we can choose to not make automated decisions sometimes.

2. Constrain thy Features

What we feed to our models really matters. […] You should probably do something to the data going into your model if you want your model to have any sort of fairness garantuees.

Vincent Warmerdam @ Pydata 2019 London

Often, simply removing biased features from your data does not reduce bias to the extent we may have hoped. Fortunately, Vincent demonstrates how to remove biased information from your variables by applying some cool math tricks.

Unfortunately, doing so will often result in a lesser predictive accuracy. Unsurprisingly though, as you are not closely fitting the biased data any more. What makes matters more problematic, Vincent rightfully mentions, is that corporate incentives often not really align here. It might feel that you need to pick: it’s either more accuracy or it’s more fairness.

However, there’s a nice solution that builds on point 1. We can now take the highly accurate model and the highly fair model, make predictions with both, and when these predictions differ, that’s a very good proxy where you potentially don’t want to make a prediction. Hence, there may be observations/samples where we are comfortable in making a fair prediction, whereas in most other situations we may say “right, this prediction seems unfair, we need a fallback mechanism, a human being should look at this and we should not automate this decision”.

Vincent does not that this is only one trick to constrain your model for fairness, and that fairness may often only be fair in the eyes of the beholder. Moreover, in order to correct for these biases and unfairness, you need to know about these unfair biases. Although outside of the scope of this specific topic, Vincent proposes this introduces new ethical issues:

Basically, we can choose to put our models on a controlled diet.

3. Constrain thy Model

Vincent argues that we should include constraints (based on domain knowledge, or common sense) into our models. In his presentation, he names a few. For instance, monotonicity, which implies that the relationship between X and Y should always be either entirely non-increasing, or entirely non-decreasing. Incorporating the previously discussed fairness principles would be a second example, and there are many more.

If we every come up with a model where more smoking leads to better health, that’s bad. I have enough domain knowledge to say that that should never happen. So maybe I should just make a system where I can say “look this one column with relationship to Y should always be strictly negative”.

Vincent Warmerdam @ Pydata 2019 London

Basically, we can integrate domain knowledge or preferences into our models.

Conclusion: Watch the talk!

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.

Propensity Score Matching Explained Visually

Propensity Score Matching Explained Visually

Propensity score matching (wiki) is a statistical matching technique that attempts to estimate the effect of a treatment (e.g., intervention) by accounting for the factors that predict whether an individual would be eligble for receiving the treatment. The wikipedia page provides a good example setting:

Say we are interested in the effects of smoking on health. Here, smoking would be considered the treatment, and the ‘treated’ are simply those who smoke. In order to find a cause-effect relationship, we would need to run an experiment and randomly assign people to smoking and non-smoking conditions. Of course such experiments would be unfeasible and/or unethical, as we can’t ask/force people to smoke when we suspect it may do harm.
We will need to work with observational data instead. Here, we estimate the treatment effect by simply comparing health outcomes (e.g., rate of cancer) between those who smoked and did not smoke. However, this estimation would be biased by any factors that predict smoking (e.g., social economic status). Propensity score matching attempts to control for these differences (i.e., biases) by making the comparison groups (i.e., smoking and non-smoking) more comparable.

Lucy D’Agostino McGowan is a post-doc at Johns Hopkins Bloomberg School of Public Health and co-founder of R-Ladies Nashville. She wrote a very nice blog explaining what propensity score matching is and showing how to apply it to your dataset in R. Lucy demonstrates how you can use propensity scores to weight your observations in such a way that accounts for the factors that correlate with receiving a treatment. Moreover, her explainations are strenghtened by nice visuals that intuitively demonstrate what the weighting does to the “pseudo-populations” used to estimate the treatment effect.

Have a look yourself: https://livefreeordichotomize.com/2019/01/17/understanding-propensity-score-weighting/

Privacy, Compliance, and Ethical Issues with Predictive People Analytics

Privacy, Compliance, and Ethical Issues with Predictive People Analytics

November 9th 2018, I defended my dissertation on data-driven human resource management, which you can read and download via this link. On page 149, I discuss several of the issues we face when implementing machine learning and analytics within an HRM context. For the references and more detailed background information, please consult the full dissertation. More interesting reads on ethics in machine learning can be found here.


Privacy, Compliance, and Ethical Issues

Privacy can be defined as “a natural right of free choice concerning interaction and communication […] fundamentally linked to the individual’s sense of self, disclosure of self to others and his or her right to exert some level of control over that process” (Simms, 1994, p. 316). People analytics may introduce privacy issues in many ways, including the data that is processed, the control employees have over their data, and the free choice experienced in the work place. In this context, ethics would refer to what is good and bad practice from a standpoint of moral duty and obligation when organizations collect, analyze, and act upon HRM data. The next section discusses people analytics specifically in light of data privacy, legal boundaries, biases, and corporate social responsibility and free choice.

Data Privacy

Technological advancements continue to change organizational capabilities to collect, store, and analyze workforce data and this forces us to rethink the concept of privacy (Angrave et al., 2016; Bassi, 2011; Martin & Freeman, 2003). For the HRM function, data privacy used to involve questions such as “At what team size can we use the average engagement score without causing privacy infringements?” or “How long do we retain exit interview data?” In contrast, considerably more detailed information on employees’ behaviors and cognitions can be processed on an almost continuous basis these days. For instance, via people analytics, data collected with active monitoring systems help organizations to improve the accuracy of their performance measurement, increasing productivity and reducing operating costs (Holt, Lang, & Sutton, 2016). However, such systems seem in conflict with employees’ right to solitude and their freedom from being watched or listened to as they work (Martin & Freeman, 2003) and are perceived as unethical and unpleasant, affecting employees’ health and morale (Ball, 2010; Faletta, 2014; Holt et al., 2016; Martin & Freeman, 2003; Sánchez Abril, Levin, & Del Riego, 2012). Does the business value such monitoring systems bring justify their implementation? One could question whether business value remains when a more long-term and balanced perspective is taken, considering the implications for employee attraction, well-being, and retention. These can be difficult considerations, requiring elaborate research and piloting.

Faletta (2014) asked American HRM professionals which of 21 data sources would be appropriate for use in people analytics. While some were considered appropriate from an ethical perspective (e.g., performance ratings, demographic data, 360-degree feedback), particularly novel data sources were considered problematic: data of e-mail and video surveillance, performance and behavioral monitoring, and social media profiles and messages. At first thought, these seem extreme, overly intrusive data that are not and will not be used for decision-making. However, in reality, several organizations already collect such data (e.g., Hoffmann, Hartman, & Rowe, 2003; Roth et al., 2016) and they probably hold high predictive value for relevant business outcomes. Hence, it is not inconceivable that future organizations will find ways to use these data for personnel-related decisions – legally or illegally. Should they be allowed to? If not, who is going to monitor them? What if the data are used for mutually beneficial goals – to prevent problems or accidents? These and other questions deserve more detailed discussion by scholars, practitioners, and governments – preferably together.

Legal Boundaries

Although HRM professionals should always ensure that they operate within the boundaries of the law, legal compliance does not seem sufficient when it comes to people analytics. Frequently, legal systems are unprepared to defend employees’ privacy against the potential invasions via the increasingly rigorous data collection systems (Boudreau, 2014; Ciocchetti, 2011; Sánchez Abril et al., 2012). Initiatives such as the General Data Protection Regulation in the European Union somewhat restore the power balance, holding organizations and their HRM departments accountable to inform employees what, why, and how personal data is processed and stored. The rights to access, correct, and erase their information is returned to employees (GDPR, 2016). However, such regulation may not always exist and, even if it does, data usage may be unethical, regardless of its legality.

For instance, should organizations use all personnel data for which they have employee consent? One could argue that there are cases where the power imbalance between employers and employees negates the validity of consent. For instance, employees may be asked to sign written elaborate declarations or complex agreements as part of their employment, without being fully aware of what they consent to. Moreover, employees may feel pressured to provide consent in fear of losing their job, losing face, or peer pressure. Relatedly, employees may be incentivized to provide consent because of the perks associated with doing so, without fully comprehending the consequences. For instance, employees may share access to personal behavioral data in exchange for mobile devices, wellness, or mobility benefits, in which case these direct benefits may bias their perception and judgement. In such cases, data usage may not be ethically responsible, regardless of the legal boundaries, and HRM departments in general and people analytics specialists in specific should take the responsibility to champion the privacy and the interests of their employees.

Automating Historic Biases

While ethics can be considered an important factor in any data analytics project, it is particularly so in people analytics projects. HRM decisions have profound implications in an imbalanced relationship, whereas the data within the HRM field often suffer from inherent biases. This becomes particularly clear when exploring applications of predictive analytics in the HRM domain.

For example, imagine that we want to implement a decision-support system to improve the efficiency of our organization’s selection process. A primary goal of such a system could be to minimize the human time (both of our organizational agents and of the potential candidates) wasted on obvious mismatches between candidates and job positions. Under the hood, a decision-support system in a selection setting could estimate a likelihood (i.e., prediction) for each candidate that he/she makes it through the selection process successfully. Recruiters would then only have to interview the candidates that are most likely to be successful, and save valuable time for both themselves and for less probable candidates. In this way, an artificially intelligent system that reviews candidate information and recommends top candidates could considerably decrease the human workload and thereby the total cost of the selection process.

For legal compliance as well as ethical considerations, we would not want such a decision-support system to be biased towards any majority or minority group. Should we therefore exclude demographic and socio-economic factors from our predictive model? What about the academic achievements of candidates, the university they attended, or their performance on our selection tests? Some of those are scientifically validated predictors of future job performance (e.g., Hunter & Schmidt, 1998). However, they also relate to demographic and socio-economic factors and would therefore introduce bias (e.g., Hough, Oswald, & Ployhart, 2001; Pyburn, Ployhart, & Kravitz, 2008; Roth & Bobko, 2000). Do we include or exclude these selection data in our model?

Maybe the simplest solution would be to include all information, to normalize our system’s predictions within groups afterwards (e.g., gender), and to invite the top candidates per group for follow-up interviews. However, which groups do we consider? Do we only normalize for gender and nationality, or also for age and social class? What about combinations of these characteristics? Moreover, if we normalize across all groups and invite the best candidate within each, we might end up conducting more interviews than in the original scenario. Should we thus account for the proportional representation of each of these groups in the whole labor population? As you notice, both the decision-support system and the subject get complicated quickly.

Even more problematic is that any predictive decision-support system in HRM is likely biased from the moment of conception. HRM data is frequently infested with human biases as bias was present in the historic processes that generated the data. For instance, the recruiters in our example may have historically favored candidates with a certain profile, for instance, red hair. After training our decision-support system (i.e., predictive model) on these historic data, it will recognize and copy the pattern that candidates with red hair (or with correlated features, such as a Northwest European nationality) are more likely successful. The system thus learns to recommend those individuals as the top candidates. While this issue could be prevented by training the model on more objective operationalization of candidate success, most HRM data will include its own specific biases. For example, data on performance ratings will include not only the historic preferences of recruiters (i.e., only hired employees received ratings), but also the biases of supervisors and other assessors in the performance evaluation processes. Similar and other biases may occur in data regarding promotions, training courses, talent assessments, or compensation. If we use these data to train our models and systems, we would effectively automate our historic biases. Such issues greatly hinder the implementation of (predictive) people analytics without causing compliance and ethical issues.

Corporate Social Responsibility versus Free Choice

Corporate social responsibility also needs to be discussed in light of people analytics. People analytics could allow HRM departments to work on social responsibility agendas in many ways. For instance, people analytics can help to demonstrate what causes or prevents (un)ethical behavior among employees, to what extent HRM policies and practices are biased, to what extent they affect work-life balance, or how employees can be stimulated to make decisions that benefit their health and well-being. Regarding the latter case, a great practical example comes from Google’s people analytics team. They uncovered that employees could be stimulated to eat more healthy snacks by color-coding snack containers, and that smaller cafeteria plate sizes could prevent overconsumption and food loss (ABC News, 2013). However, one faces difficult ethical dilemmas in this situation. Is it organizations’ responsibility to nudge employees towards good behavior? Who determines what good entails? Should employees be made aware of these nudges? What do we consider an acceptable tradeoff between free choice and societal benefits?

When we consider the potential of predictive analytics in this light, the discussion gets even more complicated. For instance, imagine that organizations could predict work accidents based on historic HRM information, should they be forbidden, allowed, or required to do so? What about health issues, such as stress and burnout? What would be an acceptable accuracy for such models? How do we feel about false positive and false negatives? Could they use individual-level information if that resulted in benefits for employees?

In conclusion, analytics in the HRM domain quickly encounters issues related to privacy, compliance, and ethics. In bringing (predictive) analytics into the HRM domain, we should be careful not to copy and automate the historic biases present in HRM processes and data. The imbalance in the employment relationship puts the responsibility in the hands of organizational agents. The general message is that what can be done with people analytics may differ from what should be done from a corporate social responsibility perspective. The spread of people analytics depends on our collective ability to harness its power ethically and responsibility, to go beyond the legal requirements and champion both the privacy as well as the interests of employees and the wider society. A balanced approach to people analytics – with benefits beyond financial gain for the organization – will be needed to make people analytics accepted by society, and not just another management tool.

Simpson’s Paradox: Two HR examples with R code.

Simpson’s Paradox: Two HR examples with R code.

Simpson (1951) demonstrated that a statistical relationship observed within a population—i.e., a group of individuals—could be reversed within all subgroups that make up that population. This phenomenon, where X seems to relate to Y in a certain way, but flips direction when the population is split for W, has since been referred to as Simpson’s paradox. Others names, according to Wikipedia, include the Simpson-Yule effect, reversal paradox or amalgamation paradox.

The most famous example has to be the seemingly gender-biased Berkeley admission rates:

“Examination of aggregate data on graduate admissions to the University of California, Berkeley, for fall 1973 shows a clear but misleading pattern of bias against female applicants. Examination of the disaggregated data reveals few decision-making units that show statistically significant departures from expected frequencies of female admissions, and about as many units appear to favor women as to favor men. If the data are properly pooled, taking into account the autonomy of departmental decision making, thus correcting for the tendency of women to apply to graduate departments that are more difficult for applicants of either sex to enter, there is a small but statistically significant bias in favor of women. […] The bias in the aggregated data stems not from any pattern of discrimination on the part of admissions committees, which seem quite fair on the whole, but apparently from prior screening at earlier levels of the educational system.” – part of abstract of Bickel, Hammel, & O’Connel (1975)

In a table, the effect becomes clear. While it seems as if women are rejected more often overall, women are actually less often rejected on a departmental level. Women simply applied to more selective departments more often (E & C below), resulting in the overall lower admission rate for women (35% as opposed to 44% for men).

Afbeeldingsresultaat voor berkeley simpson's paradox
Copied from Bits of Pi

Examples in HR

Simpsons Paradox can easily occur in organizational or human resources settings as well. Let me run you through two illustrated examples, I simulated:

Assume you run a company of 1000 employees and you have asked all of them to fill out a Big Five personality survey. Per individual, you therefore have a score depicting his/her personality characteristic Neuroticism, which can run from 0 (not at all neurotic) to 7 (very neurotic). Now you are interested in the extent to which this Neuroticism of employees relates to their Job Performance (measured 0 – 100) and their Salary (measured in Euro’s per Year). In order to get a sense of the effects, you may decide to visualize both these relations in scatter plots:

downloaddownload (6)

From these visualizations it would look like Neuroticism relates significantly and positively to both employees’ performance and their yearly salary. Should you select more neurotic people to improve your overall company performance? Or are you discriminating emotionally-stable (non-neurotic) employees when it comes to salary?

Taking a closer look at the subgroups in your data, you might however find very different relationships. For instance, the positive relationship between neuroticism and performance may only apply to technical positions, but not to those employees’ in service-oriented jobs.

download (7).png

Similarly, splitting the employees by education level, it becomes clear that there is a relationship between neuroticism and education level that may explain the earlier association with salary. More educated employees receive higher salaries and within these groups, neuroticism is actually related to lower yearly income.

download (8).png

If you’d like to see the code used to simulate these data and generate the examples, you can find the R markdown file here on Rpubs.

Solving the paradox

Kievit and colleagues (2013) argue that Simpsons paradox may occur in a wide variety of research designs, methods, and questions, particularly within the social and medical sciences. As such, they propose several means to “control” or minimize the risk of it occurring. The paradox may be prevented from occurring altogether by more rigorous research design: testing mechanisms in longitudinal or intervention studies. However, this is not always feasible. Alternatively, the researchers pose that data visualization may help recognize the patterns and subgroups and thereby diagnose paradoxes. This may be easy if your data looks like this:

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But rather hard, or even impossible, when your data looks more like the below:

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Clustering may nevertheless help to detect Simpson’s paradox when it is not directly observable in the data. To this end, Kievit and Epskamp (2012) have developed a tool to facilitate the detection of hitherto undetected patterns of association in existing datasets. It is written in R, a language specifically tailored for a wide variety of statistical analyses which makes it very suitable for integration into the regular analysis workflow. As an R package, the tool is is freely available and specializes in the detection of cases of Simpson’s paradox for bivariate continuous data with categorical grouping variables (also known as Robinson’s paradox), a very common inference type for psychologists. Finally, its code is open source and can be extended and improved upon depending on the nature of the data being studied.

One example of application is provided in the paper, for a dataset on coffee and neuroticism. A regression analysis would suggest a significant positive association between coffee and neuroticism overall. However, when the detection algorithm of the R package is applied, a different picture appears: the analysis shows that there are three latent clusters present and that the purported positive relationship only holds for one cluster whereas it is negative in the others.

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Update 24-10-2017: minutephysics – one of my favorite YouTube channels – uploaded a video explaining Simpson’s paradox very intuitively in a medical context:

Update 01-11-2017: minutephysics uploaded a follow-up video:

The paradox is that we remain reluctant to fight our bias, even when they are put in plain sight.