update lists

readmes/auditalgorithms.md

@@ -9,19 +9,37 @@ Nowadays, many algorithms (recommendation, scoring, classification) are operated
 
 ## Contents
 - [Papers](#papers)
-- [Related Events](#related-events)
+- [Related Events (conferences/workshops)](#related-events)
 
 ## Papers
+
+### 2025
+- [P2NIA: Privacy-Preserving Non-Iterative Auditing](https://arxiv.org/abs/2504.00874) - (ECAI) *Proposes a mutually beneficial collaboration for both the auditor and the platform: a privacy-preserving and non-iterative audit scheme that enhances fairness assessments using synthetic or local data, avoiding the challenges associated with traditional API-based audits.*
+- [The Fair Game: Auditing & debiasing AI algorithms overtime](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/9E8408C67F7CE30505122DD1586D9FA2/S3033373325000080a.pdf/the-fair-game-auditing-and-debiasing-ai-algorithms-over-time.pdf) - (Cambridge Forum on AI: Law and Governance) *Aims to simulate the evolution of ethical and legal frameworks in the society by creating an auditor which sends feedback to a debiasing algorithm deployed around an ML system.*
+- [Robust ML Auditing using Prior Knowledge](https://arxiv.org/pdf/2505.04796) - (ICML) *Formally establishes the conditions under which an auditor can prevent audit manipulations using prior knowledge about the ground truth.*
+- [CALM: Curiosity-Driven Auditing for Large Language Models](https://arxiv.org/abs/2501.02997) - (AAAI) *Auditing as a black-box optimization problem where the goal is to automatically uncover input-output pairs of the target LLMs that exhibit illegal, immoral, or unsafe behaviors.*
+- [Queries, Representation & Detection: The Next 100 Model Fingerprinting Schemes](https://arxiv.org/abs/2412.13021) - (AAAI) *Divides model fingerprinting into three core components, to identify ∼100 previously unexplored combinations of these and gain insights into their performance.*
+
 ### 2024
-- [FairProof: Confidential and Certifiable Fairness for Neural Networks](https://arxiv.org/pdf/2402.12572v1.pdf) - *Proposes an alternative paradigm to traditional auditing using crytographic tools like Zero-Knowledge Proofs; gives a system called FairProof for verifying fairness of small neural networks.*
+- [Hardware and software platform inference](https://arxiv.org/pdf/2411.05197) - (arXiv) *A method for identifying the underlying GPU architecture and software stack of a black-box machine learning model solely based on its input-output behavior.*
+- [Auditing Local Explanations is Hard](https://arxiv.org/abs/2407.13281) - (NeurIPS) *Gives the (prohibitive) query complexity of auditing explanations.*
+- [LLMs hallucinate graphs too: a structural perspective](https://arxiv.org/abs/2409.00159) - (complex networks) *Queries LLMs for known graphs and studies topological hallucinations. Proposes a structural hallucination rank.*
+- [Fairness Auditing with Multi-Agent Collaboration](https://arxiv.org/pdf/2402.08522) - (ECAI) *Considers multiple
+agents working together, each auditing the same platform for different tasks.*
+- [Mapping the Field of Algorithm Auditing: A Systematic Literature Review
+Identifying Research Trends, Linguistic and Geographical Disparities](https://arxiv.org/pdf/2401.11194) - (Arxiv) *Systematic review of algorithm
+auditing studies and identification of trends in their methodological approaches.*
+- [FairProof: Confidential and Certifiable Fairness for Neural Networks](https://arxiv.org/pdf/2402.12572v1.pdf) - (Arxiv) *Proposes an alternative paradigm to traditional auditing using crytographic tools like Zero-Knowledge Proofs; gives a system called FairProof for verifying fairness of small neural networks.*
 - [Under manipulations, are some AI models harder to audit?](https://grodino.github.io/projects/manipulated-audits/preprint.pdf) - (SATML) *Relates the difficulty of black-box audits
 to the capacity of the targeted models, using the Rademacher complexity.*
 - [Improved Membership Inference Attacks Against Language Classification Models](https://arxiv.org/pdf/2310.07219.pdf) - (ICLR) *Presents a framework for running membership inference attacks against classifier, in audit mode.*
 - [Auditing Fairness by Betting](https://arxiv.org/pdf/2305.17570.pdf) - (Neurips) [[Code]](https://github.com/bchugg/auditing-fairness) *Sequential methods that allows for the continuous monitoring of incoming data from a black-box classifier or regressor.*
 ### 2023
-- [XAudit : A Theoretical Look at Auditing with Explanations](https://arxiv.org/pdf/2206.04740.pdf) - *Formalizes the role of explanations in auditing and investigates if and how model explanations
+- [Privacy Auditing with One (1) Training Run](https://neurips.cc/virtual/2023/poster/70925) - (NeurIPS - best paper) *A scheme for auditing differentially private machine learning systems with a single training run.*
+- [Auditing fairness under unawareness through counterfactual reasoning](https://www.sciencedirect.com/science/article/pii/S0306457322003259) - (Information Processing & Management) *Shows how to unveil whether a black-box model, complying with the regulations, is still biased or not.*
+- [XAudit : A Theoretical Look at Auditing with Explanations](https://arxiv.org/pdf/2206.04740.pdf) - (Arxiv) *Formalizes the role of explanations in auditing and investigates if and how model explanations
 can help audits.*
-- [Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models](https://arxiv.org/pdf/2305.12620.pdf) - *Proposes a way to extend the shelf-life of auditing datasets by using language models themselves; also finds problems with the current bias auditing metrics and proposes alternatives -- these alternatives highlight that model brittleness superficially increased the previous bias scores.*
+- [Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models](https://arxiv.org/pdf/2305.12620.pdf) - (Arxiv) *Proposes a way to extend the shelf-life of auditing datasets by using language models themselves; also finds problems with the current bias auditing metrics and proposes alternatives -- these alternatives highlight that model brittleness superficially increased the previous bias scores.*
 - [Online Fairness Auditing through Iterative Refinement](https://dl.acm.org/doi/pdf/10.1145/3580305.3599454) - (KDD) *Provides an adaptive process that automates the inference of probabilistic guarantees associated with estimating fairness metrics.*
 - [Stealing the Decoding Algorithms of Language Models](https://people.cs.umass.edu/~amir/papers/CCS23-LM-stealing.pdf) - (CCS) *Steal the type and hyperparameters of the decoding algorithms of a LLM.*
 - [Modeling rabbit‑holes on YouTube](https://link.springer.com/epdf/10.1007/s13278-023-01105-9?sharing_token=h-O-asHI49VUWS9FxN1Gsve4RwlQNchNByi7wbcMAY6I98PKW1PqhFQJ_JqQyk3TrB05qDb3LUzMDmKOgrupccQliViDle-rwKEi2MZ8xBViaAQhyN41oZBKLLeXchoeIW2kklVHC094I5KD8pxja4-if6-iB0uAI1FnqnYoxjU%3D) - (SNAM) *Models the trapping dynamics of users in rabbit holes in YouTube, and provides a measure of this enclosure.*
@@ -110,5 +128,19 @@ ranking and review recommendation systems, with demographic parity, exposure, an
 - [Adversarial Learning](https://dl.acm.org/citation.cfm?id=1081950) - (KDD) *Reverse engineering of remote linear classifiers, using membership queries.*
 
 ## Related Events
+
+### 2025
+* [AIMLAI at ECML/PKDD 2025](https://project.inria.fr/aimlai/)
+* [AAAI workshop on AI Governance: Alignment, Morality, and Law](https://aaai.org/conference/aaai/aaai-25/workshop-list/#ws06)
+
+### 2024
+* [1st International Conference on Auditing and Artificial Intelligence](https://www.ircg.msm.uni-due.de/ai/)
+* [Regulatable ML Workshop (RegML'24)](https://regulatableml.github.io/)
+
+### 2023
+* [Supporting User Engagement in Testing, Auditing, and Contesting AI (CSCW User AI Auditing)](https://cscw-user-ai-auditing.github.io/)
 * [Workshop on Algorithmic Audits of Algorithms (WAAA)](https://algorithmic-audits.github.io)
 * [Regulatable ML Workshop (RegML'23)](https://regulatableml.github.io/)
+
+[auditalgorithms.md Github](https://github.com/erwanlemerrer/awesome-audit-algorithms
+)