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Documents authored by Stoyanovich, Julia

Document
DOI: 10.4230/LIPIcs.FORC.2021.7
Causal Intersectionality and Fair Ranking

Authors: Ke Yang, Joshua R. Loftus, and Julia Stoyanovich

Published in: LIPIcs, Volume 192, 2nd Symposium on Foundations of Responsible Computing (FORC 2021)

Abstract
In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings. Rankings are used in many contexts, ranging from Web search to college admissions, but causal inference for fair rankings has received limited attention. Additionally, the growing literature on causal fairness has directed little attention to intersectionality. By bringing these issues together in a formal causal framework we make the application of intersectionality in algorithmic fairness explicit, connected to important real world effects and domain knowledge, and transparent about technical limitations. We experimentally evaluate our approach on real and synthetic datasets, exploring its behavior under different structural assumptions.
Cite as

Ke Yang, Joshua R. Loftus, and Julia Stoyanovich. Causal Intersectionality and Fair Ranking. In 2nd Symposium on Foundations of Responsible Computing (FORC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 192, pp. 7:1-7:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{yang_et_al:LIPIcs.FORC.2021.7,
  author =	{Yang, Ke and Loftus, Joshua R. and Stoyanovich, Julia},
  title =	{{Causal Intersectionality and Fair Ranking}},
  booktitle =	{2nd Symposium on Foundations of Responsible Computing (FORC 2021)},
  pages =	{7:1--7:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-187-0},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{192},
  editor =	{Ligett, Katrina and Gupta, Swati},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2021.7},
  URN =		{urn:nbn:de:0030-drops-138756},
  doi =		{10.4230/LIPIcs.FORC.2021.7},
  annote =	{Keywords: fairness, intersectionality, ranking, causality}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.ICDT.2021.2
Comparing Apples and Oranges: Fairness and Diversity in Ranking (Invited Talk)

Authors: Julia Stoyanovich

Published in: LIPIcs, Volume 186, 24th International Conference on Database Theory (ICDT 2021)

Abstract
Algorithmic rankers take a collection of candidates as input and produce a ranking (permutation) of the candidates as output. The simplest kind of ranker is score-based; it computes a score of each candidate independently and returns the candidates in score order. Another common kind of ranker is learning-to-rank, where supervised learning is used to predict the ranking of unseen candidates. For both kinds of rankers, we may output the entire permutation or only the highest scoring k candidates, the top-k. Set selection is a special case of ranking that ignores the relative order among the top-k. In the past few years, there has been much work on incorporating fairness and diversity requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities. In my talk I will offer a broad perspective that connects formalizations and algorithmic approaches across subfields, grounding them in a common narrative around the value frameworks that motivate specific fairness- and diversity-enhancing interventions. I will discuss some recent and ongoing work, and will outline future research directions where the data management community is well-positioned to make lasting impact, especially if we attack these problems with our rich theory-meets-systems toolkit.
Cite as

Julia Stoyanovich. Comparing Apples and Oranges: Fairness and Diversity in Ranking (Invited Talk). In 24th International Conference on Database Theory (ICDT 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 186, p. 2:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{stoyanovich:LIPIcs.ICDT.2021.2,
  author =	{Stoyanovich, Julia},
  title =	{{Comparing Apples and Oranges: Fairness and Diversity in Ranking}},
  booktitle =	{24th International Conference on Database Theory (ICDT 2021)},
  pages =	{2:1--2:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-179-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{186},
  editor =	{Yi, Ke and Wei, Zhewei},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2021.2},
  URN =		{urn:nbn:de:0030-drops-137104},
  doi =		{10.4230/LIPIcs.ICDT.2021.2},
  annote =	{Keywords: fairness, diversity, ranking, set selection, responsible data management}
}
Document
DOI: 10.4230/DagMan.7.1.1
Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)

Authors: Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, and Ke Yi

Published in: Dagstuhl Manifestos, Volume 7, Issue 1 (2018)

Abstract
The area of Principles of Data Management (PDM) has made crucial contributions to the development of formal frameworks for understanding and managing data and knowledge. This work has involved a rich cross-fertilization between PDM and other disciplines in mathematics and computer science, including logic, complexity theory, and knowledge representation. We anticipate on-going expansion of PDM research as the technology and applications involving data management continue to grow and evolve. In particular, the lifecycle of Big Data Analytics raises a wealth of challenge areas that PDM can help with. In this report we identify some of the most important research directions where the PDM community has the potential to make significant contributions. This is done from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term.
Cite as

Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, and Ke Yi. Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151). In Dagstuhl Manifestos, Volume 7, Issue 1, pp. 1-29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{abiteboul_et_al:DagMan.7.1.1,
  author =	{Abiteboul, Serge and Arenas, Marcelo and Barcel\'{o}, Pablo and Bienvenu, Meghyn and Calvanese, Diego and David, Claire and Hull, Richard and H\"{u}llermeier, Eyke and Kimelfeld, Benny and Libkin, Leonid and Martens, Wim and Milo, Tova and Murlak, Filip and Neven, Frank and Ortiz, Magdalena and Schwentick, Thomas and Stoyanovich, Julia and Su, Jianwen and Suciu, Dan and Vianu, Victor and Yi, Ke},
  title =	{{Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)}},
  pages =	{1--29},
  journal =	{Dagstuhl Manifestos},
  ISSN =	{2193-2433},
  year =	{2018},
  volume =	{7},
  number =	{1},
  editor =	{Abiteboul, Serge and Arenas, Marcelo and Barcel\'{o}, Pablo and Bienvenu, Meghyn and Calvanese, Diego and David, Claire and Hull, Richard and H\"{u}llermeier, Eyke and Kimelfeld, Benny and Libkin, Leonid and Martens, Wim and Milo, Tova and Murlak, Filip and Neven, Frank and Ortiz, Magdalena and Schwentick, Thomas and Stoyanovich, Julia and Su, Jianwen and Suciu, Dan and Vianu, Victor and Yi, Ke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagMan.7.1.1},
  URN =		{urn:nbn:de:0030-drops-86772},
  doi =		{10.4230/DagMan.7.1.1},
  annote =	{Keywords: database theory, principles of data management, query languages, efficient query processing, query optimization, heterogeneous data, uncertainty, knowledge-enriched data management, machine learning, workflows, human-related data, ethics}
}
Document
DOI: 10.4230/DagRep.6.7.42
Data, Responsibly (Dagstuhl Seminar 16291)

Authors: Serge Abiteboul, Gerome Miklau, Julia Stoyanovich, and Gerhard Weikum

Published in: Dagstuhl Reports, Volume 6, Issue 7 (2016)

Abstract
Big data technology promises to improve people's lives, accelerate scientific discovery and innovation, and bring about positive societal change. Yet, if not used responsibly, large-scale data analysis and data-driven algorithmic decision-making can increase economic inequality, affirm systemic bias, and even destabilize global markets. While the potential benefits of data analysis techniques are well accepted, the importance of using them responsibly - that is, in accordance with ethical and moral norms, and with legal and policy considerations - is not yet part of the mainstream research agenda in computer science. Dagstuhl Seminar "Data, Responsibly" brought together academic and industry researchers from several areas of computer science, including a broad representation of data management, but also data mining, security/privacy, and computer networks, as well as social sciences researchers, data journalists, and those active in government think-tanks and policy initiatives. The goals of the seminar were to assess the state of data analysis in terms of fairness, transparency and diversity, identify new research challenges, and derive an agenda for computer science research and education efforts in responsible data analysis and use. While the topic of the seminar is transdisciplinary in nature, an important goal of the seminar was to identify opportunities for high-impact contributions to this important emergent area specifically from the data management community.
Cite as

Serge Abiteboul, Gerome Miklau, Julia Stoyanovich, and Gerhard Weikum. Data, Responsibly (Dagstuhl Seminar 16291). In Dagstuhl Reports, Volume 6, Issue 7, pp. 42-71, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@Article{abiteboul_et_al:DagRep.6.7.42,
  author =	{Abiteboul, Serge and Miklau, Gerome and Stoyanovich, Julia and Weikum, Gerhard},
  title =	{{Data, Responsibly (Dagstuhl Seminar 16291)}},
  pages =	{42--71},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{6},
  number =	{7},
  editor =	{Abiteboul, Serge and Miklau, Gerome and Stoyanovich, Julia and Weikum, Gerhard},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.6.7.42},
  URN =		{urn:nbn:de:0030-drops-67644},
  doi =		{10.4230/DagRep.6.7.42},
  annote =	{Keywords: Data responsibly, Big data, Machine bias, Data analysis, Data management, Data mining, Fairness, Diversity, Accountability, Transparency, Personal information management, Ethics, Responsible research, Responsible innovation, Data science education}
}
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