Back in 2021, writing about the seemingly-objective Supreme Court Justices making politicized decisions, Benn Stancil interrogates the idea that data-driven decisions are equivalent to objective decisions in Tilt and tilted:

Making arguments from data, like interpreting the law through legal deliberation, isn’t inherently problematic. Quite the opposite, in fact—to the extent that it’s possible, data should be foundational. But, also like the law, it’s a foundation built on less level ground than we often admit.

Though we think of data as irrefutable ground truth, it is, in fact, also almost “entirely self-referential and made up.” Often, data—and its computational cousin, the metric—isn’t an abstract representation of an innate natural quality we’re attempting to quantify; it’s an accounting identity.

Unfortunately, the common perspective of using data to make decisions (or do anything) is that data is the truest form of objectivity:

To use data is to be level-headed. The surest sign of fairness is to support your claims with numbers; the surest sign of prejudice is to fail to do so. Data is both a sword and a shield: It is a weapon for prosecuting your point, and a defense for protecting yourself as reasonable and impartial.

But bias isn’t something that can be easily removed:

For those of us who work with data, the solution isn’t to make our data or analysis more objective. We can’t. Our raw material is too tilted, as are we.

It’s very common to see data as objective, but it’s important to remember that it isn’t any more objective than we are.