arXiv subjects

Cohen Archbold

Publications and source records attributed to Cohen Archbold.

3 recordsLinked to original sources

arXiv2026

Not All Minorities Are Equal: Fairness Outcomes in Private Image Generation

Differential privacy (DP) provides formal guarantees for protecting individuals in artificial intelligence systems, but its consequences for fairness in generative models remain poorly understood. We investigate how privacy-preserving training affects the representation of demographic groups and visual attributes in generative models of human faces. Across two facial image datasets and multiple privacy levels, we find that stronger privacy can disproportionately degrade the quality and utility of some underrepresented groups, suggesting that privacy can amplify existing disparities. However, representation alone does not explain these effects: several well-represented attributes also experience substantial degradation, while some underrepresented attributes remain comparatively robust. These findings show that the impact of privacy-preserving learning cannot be explained solely by data representation and may depend on the characteristics and interactions of the information being learned. Our results highlight the need to consider both privacy and fairness when evaluating generative models.

cs.CV↗

arXiv2026

Protecting and Preserving Protest Dynamics for Responsible Analysis

Protest-related social media data are valuable for understanding collective action but inherently high-risk due to concerns surrounding surveillance, repression, and individual privacy. Contemporary AI systems can identify individuals, infer sensitive attributes, and cross-reference visual information across platforms, enabling surveillance that poses risks to protesters and bystanders. In such contexts, large foundation models trained on protest imagery risk memorizing and disclosing sensitive information, leading to cross-platform identity leakage and retroactive participant identification. Existing approaches to automated protest analysis do not provide a holistic pipeline that integrates privacy risk assessment, downstream analysis, and fairness considerations. To address this gap, we propose a responsible computing framework for analyzing collective protest dynamics while reducing risks to individual privacy. Our framework replaces sensitive protest imagery with well-labeled synthetic reproductions using conditional image synthesis, enabling analysis of collective patterns without direct exposure of identifiable individuals. We demonstrate that our approach produces realistic and diverse synthetic imagery while balancing downstream analytical utility with reductions in privacy risk. We further assess demographic fairness in the generated data, examining whether synthetic representations disproportionately affect specific subgroups. Rather than offering absolute privacy guarantees, our method adopts a pragmatic, harm-mitigating approach that enables socially sensitive analysis while acknowledging residual risks.

cs.CV↗

arXiv2023

Fine-Grained Property Value Assessment using Probabilistic Disaggregation

The monetary value of a given piece of real estate, a parcel, is often readily available from a geographic information system. However, for many applications, such as insurance and urban planning, it is useful to have estimates of property value at much higher spatial resolutions. We propose a method to estimate the distribution over property value at the pixel level from remote sensing imagery. We evaluate on a real-world dataset of a major urban area. Our results show that the proposed approaches are capable of generating fine-level estimates of property values, significantly improving upon a diverse collection of baseline approaches.

cs.CV↗