Title: Fairness-aware Anomaly Detection via Fair Projection
Venue: NeurIPS 2025
Accepted: yes
Keywords: anomaly detection

group fairnesss

deep learning
Abstract: Unsupervised anomaly detection is a critical task in many high-social-impact applications such as finance, healthcare, social media, and cybersecurity, where demographics involving age, gender, race, disease, etc. are used frequently. In these scenarios, possible bias from anomaly detection systems can lead to unfair treatment for different groups and even exacerbate social bias. In this work, first, we thoroughly analyze the feasibility and necessary assumptions for ensuring group fairness in unsupervised anomaly detection. Second, we propose a novel fairness-aware anomaly detection method FairAD. From the normal training data, FairAD learns a projection to map data of different demographic groups to a common target distribution that is simple and compact, and hence provides a reliable base to estimate the density of the data. The density can be directly used to identify anomalies while the common target distribution ensures fairness between different groups. Furthermore, we propose a threshold-free fairness metric that provides a global view for model's fairness, eliminating dependence on manual threshold selection. Experiments on real-world benchmarks demonstrate that our method achieves an improved trade-off between detection accuracy and fairness under both balanced and skewed data across different groups.


Peer reviews:
comments: SUMMARY:
This paper presents analysis and methodology for anomaly detection in an unsupervised setting while ensuring group fairness. The paper presents basic definitions and feasibility for fairness in this setting and a method to obtain a common target distribution and overall fairness metric without requiring a fixed threshold.  The paper also presents computational results using practical benchmarks.

STRENGTHS AND WEAKNESSES:
The strengths of the paper are in its overall framing of fairness issues in this setting and in its introduction of an effective method. Given the relatively little work on the topic, this is significant.  The weaknesses are relatively limited theoretical bases for the method (especially relative to alternatives) and limited consideration of additional factors that appear in other studies (e.g., network effects).

QUESTIONS:
1) What is the best possible in terms of the fairness objective in the unsupervised setting?
2) What is the worst-case complexity for achieving the optimal objective value? 
3) What is the worst case impact on anomaly detection with a fairness constraint?

LIMITATIONS:
The authors have considered the limitations to some extent but they could explore the overall impact on anomaly detection to a greater extent.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-08

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose FairAD, a novel method addressing group fairness in unsupervised anomaly detection. The approach maps demographic groups into a common target distribution, ensuring fairness without explicit constraints. The paper also introduces ADPD, a new threshold-free fairness metric compared to previous threshold-sensitive metrics, and demonstrates superior accuracy-fairness trade-offs empirically through comprehensive experiments.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n- This paper provided theoretical foundations clearly articulate necessary conditions for fairness in unsupervised AD settings.\n- The proposed method is novel, effectively combining anomaly detection and fairness via distribution alignment.\n- Comprehensive empirical validation across diverse datasets convincingly demonstrates the method's effectiveness.\n- Introduction of the ADPD metric mitigates the limitations of current threshold-sensitive metrics.\n\n### Weaknesses\n- Evaluation predominantly focused on tabular data (only one image dataset); potential applicability to other domains remains underexplored.\n- The evaluation does not consider scenarios involving multiple protected attributes; exploring how FairAD and the ADPD metric generalize to such multi-attribute fairness settings would further strengthen the work.\n\nQUESTIONS:\n1. Given that your fairness method relies on mapping demographic groups to a common distribution, how would extreme demographic imbalances (e.g., very small minority groups) affect the quality of this mapping?\n  \n2. The current work primarily validates performance on tabular data. Can you theoretically or empirically discuss how the compact-distribution mapping might behave differently on high-dimensional data such as images or sequential data like time series?\n\n3. Your fairness metric, ADPD, is threshold-free and well-suited for binary group scenarios. Can you extend or elaborate on its applicability and theoretical robustness when dealing with multiple protected attributes?\n\nLIMITATIONS:\nThe authors have adequately addressed methodological assumptions and limitations.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the problem of fairness in unsupervised anomaly detection, particularly in high-stakes domains like finance and healthcare. The authors introduce two new methods (Im-FairAD and Ex-FairAD) that map data from different demographic groups to a shared, compact target distribution to ensure group fairness while maintaining high detection accuracy. A threshold-free fairness metric (ADPD) is also proposed for a more holistic fairness evaluation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe paper makes significant theoretical and empirical contributions by rigorously analyzing fairness feasibility in UAD, introducing strong foundational assumptions, and validating them experimentally. Im-FairAD is performed equal or better than prior methods in balancing detection accuracy and fairness across multiple real-world datasets. The proposed threshold-free ADPD metric is another important contribution, addressing key limitations in previous fairness evaluation techniques.\n\nWeaknesses:\nWhile the theoretical framework is well-motivated, the practical complexity of computing Sinkhorn distances and training fairness-aware projections could have some scalability issues. Moreover, the proposed methods assume the availability of well-defined demographic groupings and rely on reasonably fair target distributions, which might not generalize well across all domains.\n\nQUESTIONS:\n- You rely heavily on Assumptions 1–3 (learnable abnormality, transferable fairness, and generalizable parity). Could you elaborate on how these assumptions can be validated or approximated in practice, especially in domains where abnormal samples may differ significantly from normal data?\n- The method hinges on mapping data to a shared, compact target distribution (a truncated Gaussian). How sensitive is the model performance to this specific choice? Have you explored other target distributions, and if so, how do they compare?\n- The use of Sinkhorn distance and deep architectures may raise concerns about scalability. How will this work for bigger datasets?\n- The approach focuses on group fairness across single protected attributes (e.g., race or gender). Have you considered how FairAD performs under intersectional fairness settings involving multiple overlapping attributes? If not, what challenges do you foresee in extending your method to this scenario?\n\nLIMITATIONS:\nA limitation can be that the fairness guarantees rest on assumptions (e.g. learnable abnormality and transferable fairness). These are plausible but I cannot be verified or enforced in all scenarios. Also since no anomalous samples are used during training, the fairness on unseen anomalies cannot be theoretically guaranteed without further assumptions.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses group fairness in unsupervised anomaly detection by proposing FairAD (Im-FairAD and Ex-FairAD), which maps data from different demographic groups to a shared compact distribution using density estimation and Sinkhorn distances, ensuring fairness without explicit regularization. A threshold-free fairness metric is introduced. Experiments on real datasets show better accuracy-fairness trade-offs under both balanced and skewed splits, transferring fairness to unseen anomalies while training only on normal data.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Addresses a critical gap in fairness within UAD with clear problem formulation.\n- Solid theoretical framework, clarifying feasibility, necessary assumptions, and fairness transferability in UAD.\n- Introduction of ADPD metric is valuable for fair evaluation without arbitrary thresholds.\n- Extensive empirical validation with competitive baselines and realistic skew/balance conditions.\n- Reproducibility appears high with clear method descriptions.\n\nWeaknesses:\n- The reliance on Sinkhorn distances and density estimation could pose scalability concerns on large-scale high-dimensional data.\n- The choice of target distribution (truncated Gaussian) may limit generalizability if data manifolds are highly complex.\n\nQUESTIONS:\n- How does FairAD (especially with Sinkhorn) scale with higher dimensions and larger datasets ?\n- How sensitive is performance to the choice of the truncated Gaussian target distribution? Would learning a target distribution (e.g., via normalizing flows) yield further improvements while maintaining fairness?\n- Could the authors clarify how ADPD behaves under highly imbalanced groups or severe covariate shifts across groups? Are there conditions under which ADPD might fail to capture fairness violations?\n\nLIMITATIONS:\nYes, the authors have adequately discussed limitations, assumptions, and potential societal impacts. They explicitly address the limitations of fairness guarantees in UAD, state the necessary assumptions for fairness transfer, and note that individual fairness is not tackled.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles group fairness in unsupervised anomaly detection. The authors first discuss the limitations of classical fairness notions and introduce two mild assumptions—transferable fairness and generalizable parity. They then propose FairAD, which learns a projection that maps each protected group onto the same latent distribution. Together with a threshold-free metric, ADPD, extensive experiments on public datasets show that FairAD achieves higher task accuracy and better fairness than baseline methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- This work begins with a comprehensive discussion of fairness definitions and evaluation methods, providing fresh insights for the field\n- The proposed method is novel and supported by sound theoretical grounding\n- The new metric and evaluation protocol are practical and thorough, reflecting rigorous research\n\nWeaknesses:\n- The analysis relies on assumptions—e.g., learnable abnormality, transferable fairness, and generalizable parity—that may fail when anomalies are structurally different (e.g., time-series faults or adversarial attacks)\n- The datasets do not cover sequential, graph, or large-scale image/video benchmarks\n- The impact of β (reconstruction weight) and λ (Ex-FairAD) on the fairness–accuracy trade-off is under-explored; more guidance on their influence and selection would be helpful\n- Computational overhead is not fully addressed—for example, empirical run-time versus sample size for the Sinkhorn step is missing\n\nQUESTIONS:\n- Is the ideal method theoretically achievable if we can perfectly select the projection or based on any other oracles?\n- Section H hints at experiments with minor anomaly contamination; could you quantify how much contamination degrades fairness?\n- For multi-attribute fairness, can the projection be shared across combinations of sensitive attributes (e.g., race × gender) without exploding Sinkhorn cost?\n\nLIMITATIONS:\nSee weaknesses", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}