Title: The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented Intervention
Benchmark: DoFaiR (DemOgraphic FActualIty Representation)
Summary: This paper examines the question of whether prompt-based diversity interventions for text-to-image models result in non-factual demographic distribution. The authors introduce DoFaiR, a benchmark to systematically analyze this question, finding that diversity-oriented instructions indeed lead to historically less accurate demographic distributions. They also propose a method to mitigate this factuality tax.
Phenomenon type: Specific form of bias
Target phenomenon: They want to measure whether prompt-based diversity interventions impair demographic factuality in text-to-image generations.
Phenomenon defined by authors: Yes
Phenomenon definition: "Would diversity interventions impair demographic factuality in text-to-image generations? Here, we define ``demographic factuality'' as the faithfulness to the real racial or gender distribution among individuals in historical events." (p. 9082-9083)
Definition scope: Subset
Definition integrity: Composite phenomenon
Definition integrity detail: Yes
Phenomenon contested: Widely-agreed
Task definition: The task is to generate an image depicting the faces of participants in a historical event. The generated image is then evaluated with respect to its demographic factuality and diversity.
Task item definition: Each item consists of a tuple of ground truths about a participant class in real historical events, and the demographic distribution among them (event name, role, dominant race/genders, involved race/genders).
Task source: Procedurally-generated task examples (e.g. Creating instances from a template), LLM-generated task examples (e.g. Filtered from responses to a prompt)
Task ecology: Representative task (e.g. answering medical licensing exam questions)
Dataset size: 756
Dataset metadata available: No
Sampling method: Random sample (creators defined a task space and sampled from it), Targeted items (creators defined a task space and chose tasks within it strategically)
Split reviewed: Test
Response format: image
Metric definition: Exact Match (accuracy, F1, precision, recall), LLM-as-a-Judge (text quality, preferences, NOT extracting answers for other metrics), Factual Diversity Divergence (quantifies the divergence in the level of demographic diversity in model generations compared with the factual ground truth)
Metric definition detail: Three exact match metrics:
- Dominant Demographic Accuracy (accuracy of the dominant demographic  groups in generated images, compared with the ground truth)
- Involved Demographic Accuracy (accuracy of the depicted demographic groups in generated images)
- Involved Demographic F-1 (weighted F-1 score for involved and non-involved demographic groups)

Race and gender of generated faces is determined using the pretrained FairFace classifier.
Metric aggregation: Simple Mean
Metric statistics: simple mean
Metric subscores: Yes
Metric subscores detail: two demographic categories (race, gender)
Metric access: Outputs alone
Few-shot evaluation: No
Authorship: Academia
Benchmark availability: Yes
Task face validity: Yes
Metric face validity: Yes
Results interpreted by authors: Yes
Results compared: No
Results comparison explanation: No comparisons made
Results realism: No
Human baseline: No
Authors discuss validity: Yes
Author validity detail: They conduct a human verification of DoFaiR items.
Taxonomy: Alignment / Bias
New taxonomy: General Capability / Bias