🧑‍🔬 Alexandra Olteanu

🏛️ Affiliations

🏢Microsoft Research MontréalMontreal · Canada2019–2026 · 16 paperscurrent🔬Mila - Quebec Artificial Intelligence InstituteMontreal · Canada2026 · 4 paperscurrent🏢MicrosoftRedmond · United States2020–2025 · 31 papers🎓École Polytechnique Fédérale de LausanneLausanne · Switzerland2011–2021 · 12 papers🏢Microsoft Research New York CityNew York · United States2019–2023 · 4 papers🏢IBMArmonk · United States2017–2018 · 4 papers🏢MicrosoftMississauga · Canada2022–2024 · 3 papers🔬IBM Research - Thomas J. Watson Research CenterYorktown Heights · United States2017–2018 · 3 papers🎓Universitatea Națională de Știință și Tehnologie Politehnica BucureștiBucharest · Romania2010–2012 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

2010s

📄 Papers

The most cited first (OpenAlex and our library).

1. FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity (2018 · 437 citations)
   The paper looks at how to build trust in AI services. These are cloud-based functions accessed through an API, and they often combine several models and datasets. The authors, from IBM Research, argue that where errors are costly (lending, the courts, medicine), accuracy alone is not enough. Consumers need to know…
2. Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective (2024 · 10 citations)
   The paper asks whether multi-dataset evaluations of coreference models really measure generalization. It borrows the measurement-modelling framework of the social sciences: a construct (what is measured, here coreference) is separated from its operationalization (how the annotation guidelines implement it). Seven…

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. FactSheets: Increasing Trust in AI Services through Supplier's Declarations of ConformityarXiv:1808.07261Matthew Arnold, Rachel K. E. Bellamy, Michael Hind, et al.2018 · 437 citations
2. Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetSu Lin Blodgett et al.2021 · 331 citations
3. Measuring algorithmically infused societies ↗Nature · no summary yetClaudia Wagner et al.2021 · 145 citations
4. Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling PerspectiveIan Porada, Alexandra Olteanu, Kaheer Suleman, et al.English · 10 citations

🤝 Co-authors here

8 co-authors with a page, the most papers together first.

• Adam Trischler
• David Piorkowski
• Ian Porada
• Jackie Chi Kit Cheung
• Kaheer Suleman
• Karthikeyan Natesan Ramamurthy
• Matthew Arnold
• Michael Hind

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2024
• Nature
• Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

🔗 Cited by

Papers of our library cite their works 213 times (of the 149,439 papers whose bibliographies we read); 46 citing papers with a page here →.

Updated 3 November 2026

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