Multi-Objective Fairness Evaluation of Multimodal Models Across Visual-Linguistic Benchmarks

Abstract

Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: Can the multi-objective evaluation framework be extended to assess multimodal models (e.g., CLIP or Flamingo) for fairness across visual-linguistic benchmarks like VCR or VQAv2, and how does the. 13 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

Can the multi-objective evaluation framework be extended to assess multimodal models (e.g., CLIP or Flamingo) for fairness across visual-linguistic benchmarks like VCR or VQAv2, and how does the trade-off between utility and fairness compare to text-only models?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance