No Politics Zone! This is a sincere question around how we conceptualize progress in Diversity/Equity/Inclusion (DEI). It is not a question inviting debate on the value of DEI.

For purposes of this question, it is assumed that the sentiment and overall mission of DEI is valid, but it is complicated to measure.

I am going to set up some context so it is clear what I am asking. The TL;DR; version is

"Has philosophy studied the underlying assumptions of DEI progress measures? Is the assumption that all groups are equally qualified for all positions in the general population, regardless of stratification method, sound? Or do we need to also adjust for differences in access to qualifying experiences? Or is it a normative statement meant to highlight differences in opportunities?

DEI has been increasingly important within many corporations, who have started introducing metrics/KPIs around progress in these dimensions. During a recent DEI session at my company (and many of you can also relate) we hear the refrain “We have made great progress but still have a long way to go.”

This got me thinking about how we measure progress towards DEI, so as to better identify what works and what doesn't.

A common way I see things measured is based on a proportion metric π (e.g., % holding sr management positions) calculated from an evaluation population E (e.g., my company), under a relevant stratification σ (e.g., gender) and then comparing it to the proportion of each stratum in a reference population R (e.g., Continental US Population), to calculate the representation gap

The representation gap γ(π,σ,E,R) is equal to π(E,σ) - σ(R), which is a list of such differences indexed over the elements of σ

Where σ(R) is just the proportion of the population in R that is in each stratum (evaluated element wise)

Example

A good example is looking at women holding senior leadership positions, where we have:

• π = "% holding senior management position" [assume there is a rigorous definition of this]
• E = US-based companies employing more than 5,000 people
• σ = Gender
• R = All registered workers in the lower 48 US states

Let's say we run our study and get π(E,σ)=10% (women), 90% (men) and σ(R)= 45% (women), 55% (men).

Therefore, γ(π,σ,E,R) = [women: 10%, men:90%] - [women: 45%, men:55%] = [women: -35%, men:+35%]

The conclusion most would draw from above is that working women are very underrepresented in senior management positions and we should take action to make promotion and evaluation policies less biased against women.

While this was made up, I think most would intuitively agree with the general sense that there are "not enough" women in senior management positions right now. However, the degree to which this is an issue and where to direct policy and non-policy action depends heavily on what we take R to be and if σ is sufficiently rich to capture confounding variables.

These are largely statistical questions, but there is a deeper, more principled assumption that I think warrants philosophical discussion. I'll call it the Equiqualification Principle:

Equiqualification Principle: For any choice of E,R,π,σ we expect γ(π,σ,E,R) = 0 for every element of γ(π,σ,E,R).

Where we can model the sampling distribution for a given E using multinomial distribution assuming the proportions hold from R.

With that, here's my more nuanced version of the question from my TL;DR; above:

Should we take the equiqualification principle as a positive or normative principle (or neither, if we think it's misguided) for measuring DEI objectives? Do we need to restrict R and/or σ to maintain relevance?