The Illusion of Objectivity | Interpreting Coordinates Ch. 5

Interpreting Coordinates
The Human Role in an Algorithmic World
PART I · Chapter 5

Why Claiming Neutrality Can Hide the Position Behind a Judgment

People often say:

“I looked at it objectively.”
“I removed my personal feelings.”
“I tried to stay neutral.”

These are among the fastest ways to gain trust. At the same time, they can become some of the most dangerous sentences we use.

Because removing personal feeling does not, by itself, make a judgment objective.¹

1. Objectivity Is Not an Attitude. It Is a Condition.

Objectivity is not a mindset. It is not merely a matter of intention or sincerity. A claim of objectivity becomes meaningful only when its conditions are made explicit.

• What was chosen as the comparison group?
• What was defined as the whole?
• What was excluded—and for what reason?

When someone claims objectivity without answering these questions, the statement begins to look less like analysis and more like a declaration of belief.²

Most of us want to be objective. Far fewer of us make visible the conditions under which that claim is made.

2. “Neutrality” Erases Information

Saying “I am neutral” sounds humble. In practice, a claim of neutrality can leave some of the most important information unstated.

Once neutrality is declared, questions like these can disappear from view:

• Which side is this closer to?
• According to which criteria is balance measured?
• What weight does this carry within the whole?

Neutrality is not the absence of position.

It can become a refusal to state one.

Writing that leaves its position and criteria unstated can offer less reference value when comparison or reuse is required—whether for human readers or within system-mediated interpretation.³

3. Numbers and Ratings Do Not Guarantee Objectivity

Stars. Scores. Rankings.

We often treat them as proof of objectivity.

They are not.

Numbers are outcomes of choices and assumptions:

• Which elements were scored?
• Why those elements?
• What was left unmeasured—and why?

Numbers may be precise. But precision alone does not tell us whether the underlying criteria were appropriate.⁴

Scores can be stored, ranked, and reused. But their meaning depends on the criteria, categories, and assumptions that produced them.

4. The Stronger the Claim of Objectivity, the Less Responsibility Remains

“I was just being objective.”

This sentence can have a convenient effect: responsibility becomes easier to avoid.

• No need to explain why this judgment was made
• No need to reveal the criteria used
• No need to consider alternative frameworks

When its criteria remain unstated, the language of objectivity can strengthen judgment while weakening explanation.⁵

AEP — AI Entity Profiler — does not treat a claim of neutrality as sufficient analysis.

5. We Are Never Positionless. That Is Why We Need Coordinates.

AEP — AI Entity Profiler — does not claim to eliminate position in the name of objectivity. Instead, it makes position explicit.

• This entity exists within this whole
• Within that whole, its role is this
• From this position, it functions in this way

This is not evaluation. It is not endorsement or rejection.

It is **coordinate-setting.**⁶

Instead of treating objectivity as the absence of position, we declare location.

That gives both human and system readers more explicit context for interpretation.

6. The Age of Objectivity Is Over

What is ending here is not the aspiration to objectivity itself, but the idea that neutrality can be claimed without making criteria and position visible.⁷

For a long time, “emotion-free judgment” could appear to be objectivity. In system-mediated environments, something else becomes increasingly important:

• Not emotionless writing
• But contextually explicit writing
• Writing with declared position

Objectivity remains an aspiration.

But invoking it without making criteria and position visible can become a form of avoidance.

Closing

We are never positionless.

And the moment we make that position visible,
we can become more precise.

AEP — AI Entity Profiler — does not use neutrality as an escape from position.

It records position.

Because in system-mediated environments, human language becomes easier to interpret and reuse when its position and criteria are explicit.

Footnotes

1. Objectivity in this chapter does not mean a perfectly positionless point of view. The term refers to a claim that becomes more defensible when its comparison group, scope, exclusions, and criteria are made explicit.

2. Position here includes the standpoint from which a comparison is made, the boundaries of the whole being considered, and the criteria used to distinguish one entity from another.

3. Reference value refers here to usefulness for comparison, interpretation, or reuse across contexts. It does not imply that writing without an explicit position has no human, literary, or informational value.

4. A numerical result may be precise within the system that produces it. Precision alone, however, does not explain why particular variables were selected, how they were weighted, or what remained outside the measurement.

5. Responsibility in this chapter refers to making the basis of judgment visible: the criteria selected, the alternatives considered, and the boundaries within which a conclusion applies.

6. Coordinate-setting refers here to making an entity’s whole, comparison criteria, role, and position explicit before evaluation.

7. The Age of Objectivity Is Over is a philosophical compression, not a claim that objective inquiry or rigorous methods are obsolete. It refers to the limits of invoking neutrality without disclosing the criteria and position from which analysis proceeds.

References

Foucault, M. Power/Knowledge. Pantheon Books, 1980.

Arendt, H. Eichmann in Jerusalem: A Report on the Banality of Evil. Viking Press, 1963.

Weber, M. The Methodology of the Social Sciences. Free Press, 1949.

Daston, L., & Galison, P. Objectivity. Zone Books, 2007.

Selbst, A. D., et al. “Fairness and Abstraction in Sociotechnical Systems.” Proceedings of FAT, 2019.

Mitchell, M. Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux, 2019.