1. Algorithmic Management and Fairness
Peter Cappelli
George W. Taylor Professor of Management
Director – Center for Human Resources
The Wharton School

2. What is it we are trying to do? Mainly make better predictions….
Recruiting – identifying possible candidates and persuading them to apply: are we securing good candidates?
Selection – choosing which candidate should receive job offers: Are we offering jobs to those who will be the best employees?
On-boarding – the initial process of bringing an employee into an organization, which includes a large number of administrative tasks
Training – what should we recommend for you? Do our interventions improve performance?
Performance management – can we identify good and bad performance: Do our practices improve job performance?
Advancement – who gets promoted: Can we predict who will perform best in new roles? Can we make recommendations for your career?
Retention – can we predict who is likely to leave and manage the level of retention?
Employee benefits – Can we identify which benefits matter most to employees and what the effects of those benefits are (e.g., do they improve recruiting and retention)?

3. We do that with algorithms
Old approach: Testing hypotheses about what predicts a good hire
Got answers for each technique – interviews predict this much, IQ predicts that much, etc.
That is statistics
New approach: Fitting the model to the data –
Gather everything you know about past hires, put it together and tinker until it can predict as much as possible about who is good.
Not clear which measures in the algorithm are doing what
That’s “machine learning,” the model was built/”learned” from the data

4. What’s the problem with using algorithms
Now you know exactly what it is that is producing your decisions.
As opposed to generating measures (e.g., IQ, personality scores, etc.) and having hiring managers use them as they see fit
Hard to miss if it has adverse impact, then we have to deal with it
Can you “explain” to users – or judges – what is going on in the model
Most people understand and accept seniority as a decision process
Will they understand and accept results from an algorithm?
Who do I appeal to – what’s the standard for fairness?

5. Algorithms are built on what worked before
So they will tell you to hire more white men

6. What does law and fairness require?
1st – our decisions shouldn’t discriminate/disparate impact
2nd – if they do, no better rule should be available that both predicts AND doesn’t discriminate

7. There is a vacuum of solutions, and in come the vendors….
moving-upstream

8. All the cool tools -
But do any of them predict real outcomes? HireVue, DeepSense, Vibe, HireIQ/Audilytics
If not, they are worthless
Some trying to mimic other predictors, such as personality
May not be worthless, but probably are
hiring-moving-upstream

9. Building algorithms based on “best performers” is a mistake
“Selecting on the Dependent Variable” – best performers have a lot of things in common with lousy performers. This doesn’t sort them out. Have to compare the two.

10. Algorithms “optimize” fit but only on one outcome
Algorithms do a better job of reading x-rays
Only if you are looking for one illness
They do a better job of predicting who will make it through the interview process
No necessarily who will be a good employee
BUT – suppose we have more than one concern
Parole algorithms – do a very good job of predicting potential violators
But they produce worse results for African Americans –
If you did two models – one for majority and one for minority populations – you’d avoid that problem, but then you have disparate treatment

11. Other problems -
Suppose something really predicts, but it is “weird,” like commuting…
Research shows if you take out human judgment in weighting info on candidates, prediction gets better. But will managers still buy in?
We can remove criteria that we think are a source of bias, such as demographic info, but then the predictions get worse
Is it more sensitive to “see” the bias than it is to have subjective, human assessments that predict worse and are probably more biased, but we can’t “see” the bias?
We are reasonably comfortable using algorithms for admissions or hiring. But how about for firing: should we dismiss someone if an algorithm indicates they will steal?

12. Data “science” concerns….
Algorithms are good at identifying association, not causation
E.g., being outgoing may be associated with advancing, but is that because it actually makes performance better or just because people like you more?
Causation matters to defensibility and fairness
On issues like hiring, we’ve already screened out candidates before we use them to build the algorithm, so we may be missing really important predictors
Need to keep running experiments: Hire some surprises
Building algorithms is very hard to do – because data sets aren’t easily merged
Each dive in is very expensive, so we need to choose carefully what to investigate
How to choose? Data scientists need guidance from HR.

13. Data problems….
If we have lousy measures, the algorithms are irrelevant
If we don’t trust job performance measures, no algorithm will predict good hires
What to do about “small” data….
Imputation, collaboration or vendors, granularity
New sources of data can be promising….
Social media is “authentic” – people cheat on other measures
messages even more so
Are we ok using these? Will it change behavior once we start?