Anonymization: The imperfect science of using data while preserving privacy

A Gadotti, L Rocher, F Houssiau, AM Creţu… - Science …, 2024 - science.org
Information about us, our actions, and our preferences is created at scale through surveys or
scientific studies or as a result of our interaction with digital devices such as smartphones …

Discrimination through optimization: How Facebook's Ad delivery can lead to biased outcomes

M Ali, P Sapiezynski, M Bogen, A Korolova… - Proceedings of the …, 2019 - dl.acm.org
The enormous financial success of online advertising platforms is partially due to the precise
targeting features they offer. Although researchers and journalists have found many ways …

Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns

H Felzmann, EF Villaronga, C Lutz… - Big Data & …, 2019 - journals.sagepub.com
Transparency is now a fundamental principle for data processing under the General Data
Protection Regulation. We explore what this requirement entails for artificial intelligence and …

Building and auditing fair algorithms: A case study in candidate screening

C Wilson, A Ghosh, S Jiang, A Mislove… - Proceedings of the …, 2021 - dl.acm.org
Academics, activists, and regulators are increasingly urging companies to develop and
deploy sociotechnical systems that are fair and unbiased. Achieving this goal, however, is …

Auditing for discrimination in algorithms delivering job ads

B Imana, A Korolova, J Heidemann - Proceedings of the web conference …, 2021 - dl.acm.org
Ad platforms such as Facebook, Google and LinkedIn promise value for advertisers through
their targeted advertising. However, multiple studies have shown that ad delivery on such …

On microtargeting socially divisive ads: A case study of russia-linked ad campaigns on facebook

FN Ribeiro, K Saha, M Babaei, L Henrique… - Proceedings of the …, 2019 - dl.acm.org
Targeted advertising is meant to improve the efficiency of matching advertisers to their
customers. However, targeted advertising can also be abused by malicious advertisers to …

Privacy-and utility-preserving textual analysis via calibrated multivariate perturbations

O Feyisetan, B Balle, T Drake, T Diethe - … on web search and data mining, 2020 - dl.acm.org
Accurately learning from user data while providing quantifiable privacy guarantees provides
an opportunity to build better ML models while maintaining user trust. This paper presents a …

Efficient deep learning on multi-source private data

N Hynes, R Cheng, D Song - arXiv preprint arXiv:1807.06689, 2018 - arxiv.org
Machine learning models benefit from large and diverse datasets. Using such datasets,
however, often requires trusting a centralized data aggregator. For sensitive applications like …

What makes a “bad” ad? user perceptions of problematic online advertising

E Zeng, T Kohno, F Roesner - Proceedings of the 2021 CHI Conference …, 2021 - dl.acm.org
Online display advertising on websites is widely disliked by users, with many turning to ad
blockers to avoid “bad” ads. Recent evidence suggests that today's ads contain potentially …

Ad delivery algorithms: The hidden arbiters of political messaging

M Ali, P Sapiezynski, A Korolova, A Mislove… - Proceedings of the 14th …, 2021 - dl.acm.org
Political campaigns are increasingly turning to targeted advertising platforms to inform and
mobilize potential voters. The appeal of these platforms stems from their promise to …