This page aims at listing algorithms (with a short review) related to the following scenario:
A user queries a service provider (through available APIs), and tries to infer information about the algorithms in use for providing the results of those queries.
Related keywords include: transparency, bias, inference, API, queries, reverse engineering, black-box, algorithmic accountability.
| Algorithm/paper | Source | Description | Code | Test |
|---|---|---|---|---|
| Adversarial Learning | KDD (2005) | Reverse engineering of remote linear classifiers, using membership queries | Experimented (locally) on mail spam classifiers | |
| Measuring Personalization of Web Search | WWW (2013) | Develops a methodology for measuring personalization in Web search result | Experimented on Google Web Search | |
| XRay: Enhancing the Web’s Transparency with Differential Correlation | USENIX Security (2014) | Audits which user profile data were used for targeting a particular ad, recommendation, or price | Available here | Demonstrated using Gmail, Youtube, and Amazon recommendation services |
| Peeking Beneath the Hood of Uber | IMC (2015) | Infer implementation details of Uber's surge price algorithm | Four weeks of data from Uber (from 43 copies of the Uber app) | |
| Bias in Online Freelance Marketplaces: Evidence from TaskRabbit | dat workshop (2016) | Measures the TaskRabbit’s search algorithm rank | Crawled TaskRabbit website | |
| Stealing Machine Learning Models via Prediction APIs | Usenix Security (2016) | Aims at extracting machine learning models in use by remote services | Available here | Demonstrated on BigMl and Amazon Machine Learning services |
| Back in Black: Towards Formal, Black Box Analysis of Sanitizers and Filters | Security and Privacy (2016) | Black-box analysis of sanitizers and filters | ||
| Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems | Security and Privacy (2016) | Introduces measures that capture the degree of influence of inputs on outputs of the observed system | Tested inhouse on machine learning models on two datasets | |
| Uncovering Influence Cookbooks : Reverse Engineering the Topological Impact in Peer Ranking Services | CSCW (2017) | Aims at identifying which centrality metrics are in use in a peer ranking service | ||
| The topological face of recommendation: models and application to bias detection | Complex Networks (2017) | Proposes a bias detection framework for items recommended to users | Tested on Youtube crawls | |
| Membership Inference Attacks Against Machine Learning Models | Symposium on Security and Privacy (2017) | Given a machine learning model and a record, determine whether this record was used as part of the model’s training dataset or not | Tested using Amazon ML and Google Prediction API | |
| Adversarial Frontier Stitching for Remote Neural Network Watermarking | arXiv (2017) | Check if a remote machine learning model is a "leaked" one: through standard API requests to a remote model, extract (or not) a zero-bit watermark, that was inserted to watermark valuable models (eg, large deep neural networks) | ||
| Practical Black-Box Attacks against Machine Learning | Asia CCS (2017) | Understand how vulnerable is a remote service to adversarial classification attacks | Tested against Amazon and Google classification APIs |
- FAT 2018 (Conference on Fairness, Accountability, and Transparency) https://fatconference.org/2018/program.html
- DTL 2017 (Data Transparency Lab Conference) http://datatransparencylab.org/dtl-2017/program-dtl-2017/