On the Apparent Conflict Between Individual and Group Fairness

In plain words

Fairness researchers in machine learning often use two kinds of rule for checking a model. One says the model should treat groups, such as men and women, about equally. The other says two people who are alike in the ways that matter should get the same result. When the group rule pushes a model to accept someone it would otherwise reject, the second rule can seem broken, and the two look like they are at war.

This paper argues that the war is mostly a misunderstanding. Both rules can be read as expressions of the same moral ideas about luck, effort and consistency. The real disagreement lies in unstated beliefs about where unfair differences come from: from unjust social structures, or from choices people make. Once those beliefs are made explicit, a team can pick a measure that matches them, whichever family it belongs to.

TL;DR

• The paper argues that the supposed conflict between group and individual fairness is not a conflict of principles §3 §6.
• Both families can be defended from consistency and egalitarian justice, and neither satisfies the idea of individual justice §3 §4 §6.
• Real conflicts come from pairing variants that rest on opposite assumptions about the cause of disparities, such as statistical parity with raw similarity §5.3 tab. 2.
• The paper contains no experiments, algorithms or code. Use it for framing decisions, not for implementation details.

Problem

Group fairness measures, such as statistical parity, require similar proportions of each protected group in each outcome class §2.1.1. Individual fairness requires that similar individuals receive similar outcomes, where similarity is defined by a task-relevant distance metric §2.1.2. Applying a group measure can therefore give different outcomes to two people who look alike on the task, and that is the apparent conflict §1 §2.1.3.

The paper asks whether this conflict is fundamental. It tests this against philosophical accounts of justice and against two worked examples §1 §3 §5.

Method

This is a conceptual argument, not a technique. The steps below are the paper's own argument, restated in our words.

1. Map the two measure families to moral principles. Group fairness corresponds naturally to egalitarianism, and individual fairness to consistency ("like cases should be treated alike") §3.1 §3.2 tab. 1.
2. Show the mapping is not exclusive. Consistency can support group fairness, because the choice of which features count as relevant is itself normative. Egalitarianism can support individual fairness, because the distance metric can be adjusted for disadvantage that people did not choose §3.1 §3.2.
3. Test individual justice. Schauer's "particularised" justice requires assessing each case on its own merits. Individually fair models still generalise over people who share the same features, so neither family meets this standard §4.1 §4.2.
4. Locate the real conflict in worldviews. Measures conflict only when they rest on opposite assumptions. A raw similarity metric, which treats observed differences as choices, conflicts with statistical parity, which treats them as unjust structure. Reversing both (group-adjusted similarity against equal calibration) reverses the conflict §5.3 tab. 2.
5. Apply the argument to two cases. College admissions §5.1 and lending §5.2 are each analysed under a "structural" and a "choice" assumption. Under each assumption, both families offer a fitting measure.
6. Name the questions a team must answer. Purpose of the system, how the data were generated, and which unfairness is at issue §6.

The paper's recommended sequence can be restated as follows. It is a summary of §5.3, tab. 2 and §6, not an algorithm from the paper.

# Choosing a fairness variant from stated assumptions  [§5.3] [tab. 2] [§6]
# Input:  purpose, data-generating story, suspected source of disparities
# Output: one measure, from either family

def choose_fairness_measure(purpose, causal_story):
    # 1. Fix what the system is for before choosing a measure  [§6]
    objectives = state_objectives(purpose)
    # 2. Decide whether observed gaps come from unjust structure  [§5.1] [§5.2]
    structural = gaps_reflect_unjust_structure(causal_story)
    if structural:
        # 3a. Structural view: statistical parity, or a group-adjusted
        #     similarity metric  [tab. 2]
        return pick_variant(objectives, family_any, view="structural")
    # 3b. Choice view: raw similarity metric, or equal calibration  [tab. 2]
    return pick_variant(objectives, family_any, view="choice")

Notes on the sketch:

• The paper does not define state_objectives, gaps_reflect_unjust_structure or pick_variant. They name the questions in §6 and the options in tab. 2. Any implementation must supply its own answers.
• The step 2 test is the hard part. The paper says it needs "contestable empirical and normative assumptions" and that the choice may be politically difficult §5.3.1.

Hyperparameters

No model is trained and no algorithm is run, so there are no learning rates, optimisers, epochs or thresholds. The only numbers are illustrative values in the two worked examples.

Results

No experiments. The paper's results are conceptual conclusions, author-reported and not measured. Setup: argument from philosophy and legal theory, plus two hypothetical examples §1 §5.

Paper vs code

No official code. The paper links no code, and a GitHub repository search for "individual group fairness binns" returned no results (run 2026-11-01).

Pitfalls

• Not an implementation guide. The paper gives no definition of task-relevant similarity and no algorithm. An implementer must supply both, and the paper says the choice is normative §2.1.2 §5.3.1.
• Practical differences remain. The paper says the conflict is between variants, not families §5.3. It also says the families differ in practice: group measures are easy to implement, and individual fairness needs a similarity metric that has no clear process for being set §5.3. The choice of family still matters for implementation.
• Individual justice is a philosophical notion (particularised justice, Einzelfallgerechtigkeit) §4.1. The paper's claim that individually fair models fall short of it is an argument, not a measured result §4.2.
• Worked examples are hypothetical. The 100-point adjustment and the SAT pairs are illustrative §5.1. The -11% figure comes from a footnote analysing one dataset §5.2.
• Impossibility results are cited, not proven here. The COMPAS point that false positive equality and calibration cannot both hold is attributed to prior work §5.3.
• Equal calibration is not the same as individual fairness. The paper uses equal calibration as a group measure that tolerates base-rate differences §5.1 tab. 2.
• Unresolved by the paper: what to do when the causal story is uncertain. It says the question must be answered "in practice" by stakeholders, and allows that the answer may be to reject ML altogether §5.3.1 §6.

Lineage

• Builds on: the individual fairness definition of Dwork et al. (2012) §2.1.2 and the statistical parity and error-rate measures in prior work §2.1.1.
• Builds on: Friedler et al.'s "What You See" and "We're All Equal" worldviews, which this paper reframes as the source of conflict §5.3.2.
• Compared against: learned individual-fairness methods (Zemel et al. 2013, Lahoti et al. 2019, Jung et al. 2019) and their claimed group-fairness side effects §2.1.2 §2.1.3.
• Superseded by: not stated.