Audit Algorithms
91 items from erwanlemerrer/awesome-audit-algorithms ★122
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The Imitation Game: Algorithm Selectionby Exploiting Black-Box Recommender github.com
(Netys) (Code) Parametrize a local recommendation algorithm by imitating the decision of a remote and better trained one.
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Online Fairness Auditing through Iterative Refinement dl.acm.org
(KDD) Provides an adaptive process that automates the inference of probabilistic guarantees associated with estimating fairness metrics.
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Auditing Algorithms: On Lessons Learned and the Risks of DataMinimization dl.acm.org
(AIES) A practical audit for a well-being recommendation app developed by Telefónica (mostly on bias).
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Auditing radicalization pathways on dl.acm.org
(FAT*) Studies the reachability of radical channels from each others, using random walks on static channel recommendations.
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Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles dl.acm.org
(Transactions on Recommender Systems) What it takes to “burst the bubble,” i.e., revert the bubble enclosure from recommendations.
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Auditing Algorithmic Bias on Twitter dl.acm.org
(WebSci).
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Auditing the Personalization and Composition of Politically-Related Search Engine Results Pages dl.acm.org
(WWW) A Chrome extension to survey participants and collect the Search Engine Results Pages (SERPs) and autocomplete suggestions, for studying personalization and composition.
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Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment dl.acm.org
(CCS) Model inversion approach in the adversary setting based on training an inversion model that acts as aninverse of the original model. With no fullknowledge about the original training data, an accurate inversion is still possible by training the inversion model on…
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Practical Black-Box Attacks against Machine Learning dl.acm.org
(Asia CCS) Understand how vulnerable is a remote service to adversarial classification attacks.
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Peeking Beneath the Hood of Uber dl.acm.org
(IMC) Infer implementation details of Uber's surge price algorithm.
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Measuring Personalization of Web Search dl.acm.org
(WWW) Develops a methodology for measuring personalization in Web search result.
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Privacy Oracle: a System for Finding Application Leakswith Black Box Differential Testing dl.acm.org
(CCS) Privacy Oracle: a system that uncovers applications' leaks of personal information in transmissions to remoteservers.
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Adversarial Learning dl.acm.org
(KDD) Reverse engineering of remote linear classifiers, using membership queries.
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Uncovering Influence Cookbooks : Reverse Engineering the Topological Impact in Peer Ranking Services dl.acm.org
(CSCW) Aims at identifying which centrality metrics are in use in a peer ranking service.
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Bias in Online Freelance Marketplaces: Evidence from TaskRabbit datworkshop.org
(dat workshop) Measures the TaskRabbit's search algorithm rank.
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Auditing Black-Box Prediction Models for Data Minimization Compliance cs.bu.edu
(NeurIPS) Measures the level of data minimization satisfied by the prediction model using a limited number of queries.
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Auditing: Active Learning with Outcome-Dependent Query Costs cs.bgu.ac.il
(NIPS) Learns from a binary classifier paying only for negative labels.
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The Fair Game: Auditing & debiasing AI algorithms overtime cambridge.org
(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.
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The Fair Game: Auditing & debiasing AI algorithms over time cambridge.org
Jouarnal of Cambridge Forum on AI: Law and Governance
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Auditing Pay-Per-Token in Large Language Models arxiv.org
(arXiv) Develops an auditing framework based on martingale theory that enables a trusted third-party auditor who sequentially queries a provider to detect token misreporting.
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Robust ML Auditing using Prior Knowledge arxiv.org
(ICML) Formally establishes the conditions under which an auditor can prevent audit manipulations using prior knowledge about the ground truth.
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Hardware and software platform inference arxiv.org
(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.
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FairProof: Confidential and Certifiable Fairness for Neural Networks arxiv.org
(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.
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Fairness Auditing with Multi-Agent Collaboration arxiv.org
(ECAI) Considers multiple agents working together, each auditing the same platform for different tasks.
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(Arxiv) Systematic review of algorithm auditing studies and identification of trends in their methodological approaches.
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Improved Membership Inference Attacks Against Language Classification Models arxiv.org
(ICLR) Presents a framework for running membership inference attacks against classifier, in audit mode.
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Auditing Yelp’s Business Ranking and Review Recommendation Through the Lens of Fairness arxiv.org
(Arxiv) Audits the fairness of Yelp’s business ranking and review recommendation systems, with demographic parity, exposure, and statistical tests such as quantile linear and logistic regression.
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Auditing Fairness by Betting arxiv.org
(Neurips) [Code] Sequential methods that allows for the continuous monitoring of incoming data from a black-box classifier or regressor.
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Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models arxiv.org
(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…
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(ICLR) Considers backdoor detection under the black-box setting in machine learning as a service (MLaaS) applications.
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XAudit : A Theoretical Look at Auditing with Explanations arxiv.org
(Arxiv) Formalizes the role of explanations in auditing and investigates if and how model explanations can help audits.
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Your Echos are Heard: Tracking, Profiling, and Ad Targeting in the Amazon Smart Speaker Ecosystem arxiv.org
(arxiv) Infers a link between the Amazon Echo system and the ad targeting algorithm.
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(CHI) Makes the case for "everyday algorithmic auditing" by users.
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(FAccT) Do Amazon private label products get an unfair share of recommendations and are therefore advantaged compared to 3rd party products?
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Extracting Training Data from Large Language Models arxiv.org
(USENIX Security) Extract verbatim text sequences from the GPT-2 model’s training data.
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Extracting Training Data from Large Language Models arxiv.org
(arxiv) Performs a training data extraction attack to recover individual training examples by querying the language model.
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Making targeted black-box evasion attacks effective andefficient arxiv.org
(arXiv) Investigates how an adversary can optimally use its query budget for targeted evasion attacks against deep neural networks.
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Neural Network Model Extraction Attacks in Edge Devicesby Hearing Architectural Hints arxiv.org
(arxiv) Through the acquisition of memory access events from bus snooping, layer sequence identification bythe LSTM-CTC model, layer topology connection according to the memory access pattern, and layer dimension estimation under data volume constraints, it demonstrates one can…
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Stealing Neural Networks via Timing Side Channels arxiv.org
(arXiv) Stealing/approximating a model through timing attacks usin queries.
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xGEMs: Generating Examplars to Explain Black-Box Models arxiv.org
(arXiv) Searches bias in the black box model by training an unsupervised implicit generative model. Thensummarizes the black-box model behavior quantitatively by perturbing data samples along the data manifold.
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Learning Networks from Random Walk-Based Node Similarities arxiv.org
(NIPS) Reversing graphs by observing some random walk commute times.
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“Why Should I Trust You?”Explaining the Predictions of Any Classifier arxiv.org
(arXiv) (Code) Explains a blackbox classifier model by sampling around data instances.
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P2NIA: Privacy-Preserving Non-Iterative Auditing arxiv.org
(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.
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CALM: Curiosity-Driven Auditing for Large Language Models arxiv.org
(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.
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Queries, Representation & Detection: The Next 100 Model Fingerprinting Schemes arxiv.org
(AAAI) Divides model fingerprinting into three core components, to identify ∼100 previously unexplored combinations of these and gain insights into their performance.
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LLMs hallucinate graphs too: a structural perspective arxiv.org
(complex networks) Queries LLMs for known graphs and studies topological hallucinations. Proposes a structural hallucination rank.
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Auditing Local Explanations is Hard arxiv.org
(NeurIPS) Gives the (prohibitive) query complexity of auditing explanations.
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Setting the Record Straighter on Shadow Banning arxiv.org
(INFOCOM) (Code) Considers the possibility of shadow banning in Twitter (ie, the moderation black-box algorithm), and measures the probability of several hypothesis.
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Adversarial Model Extraction on Graph Neural Networks arxiv.org
(AAAI Workshop on Deep Learning on Graphs: Methodologies and Applications) Introduces GNN model extraction and presents a preliminary approach for this.
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