Fairness and impartiality
Stock-based compensation such as options or restricted stock units (RSUs)
Being compensated for your work such that you are not advantaged or disadvantaged for non-compensable factors such as age, gender, race, LGBTQIA+ status, disability, etc.
Sometimes fair pay can also be called “consistent pay,” to emphasize that pay should be consistent within an organization.
Unintentionally or not, manipulating a person such that they begin to doubt their own experience.
Denying or making excuses for someone’s experience is a common gaslighting tactic.
idiosyncratic rater bias
When someone evaluates another person’s performance from the perspective of how skilled (or unskilled) they, themselves are in a particular area. As a result, they would rate someone else’s skills at a
level if they’re
good at it, and rate others’ skills at a
level in areas they
more skilled at.
Research suggests that, because of this bias, over 50% of a rating can be attributed to the rater, not the performance of the ratee!
Creating an organization where people can bring their full identity to work AND be heard and included in decision making and work.
Believing people when they say ‘this hurts me’ (validate, acknowledge).
A manager’s ability to apply their own judgment when deciding how much to pay those who report to them. This can be a single manager or small group of managers (often of limited diversity themselves) who exercise discretion over an individual’s pay. While discretion
like a good thing, managers who exercise discretion often introduce their own biases—knowingly or unconsciously.
Anchoring compensation in market data.
E.g., we pay at the 50th percentile for companies of our stage/size/industry for both cash and equity across the entire company.
An organization that
promotes and compensates its employees based on merit. I write aspirationally here, not because I don’t believe in this ideal, but because there are well-documented barriers and biases that prevent this ideal from being realized. Meritocracy is often used as an excuse for maintaining traditional and flawed pay-for-performance mechanisms.
See definition for “fair pay” above
pay for performance
A compensation policy that pays employees for different levels of assessed contribution or performance. A typical pay-for-performance program would result in individual employees’ compensation being determined on the basis of assessments, ratings, and manager calibration. It’s important to note that performance among knowledge workers, is often subjective and that in this type of structure, an employee is actually being paid for
performance that may be different from actual performance. This difference can be magnified because of bias.
The difference in earnings that members of different demographic groups experience. It can be viewed at an aggregate level (e.g., all women versus all men), but it is often viewed more granularly by intersectional race/gender groups and by education or management level.
Unfortunately the pay gap persists across many different cuts of the data. For example, the pay gap between executive women and men is much larger (75 cents on the dollar), than between front-line lower-compensated women and men (92 cents). For further information, this article is excellent:
See definition for “pay for performance” and consider how you are assessing “performance” and whether it includes bias
Unconscious biases are social stereotypes about certain groups of people that individuals form outside their own conscious awareness. Everyone holds unconscious beliefs about various social and identity groups, and these biases stem from one’s tendency to organize social worlds by categorizing.
Unconscious bias is far more prevalent than conscious prejudice and often incompatible with one’s conscious values. Certain scenarios can activate unconscious attitudes and beliefs. For example, biases may be more prevalent when multi-tasking or working under time pressure.
Specific documented biases include: affinity bias, recency bias, beauty bias (especially for women), like-me bias, confirmation bias, horns/halo biases, conformity bias and others.