Date: December 4, 2019 Venue: UiS Please cite, link to or credit this presentation when using it or part of it in your work.

1. FACT-IR
Fairness, Accountability, Confidentiality
and Transparency in Information Retrieval
Darío Garigliotti
University of Stavanger, Norway
December 4th, 2019

2. Outline
• An introduction to responsible Information Retrieval
• Fairness in IR
• Accountability in IR
• Confidentiality in IR
• Transparency in IR
• Responsible IR and the IR community
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3. An introduction to
responsible
Information Retrieval
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4. Information Retrieval
systems
• Information Retrieval (IR) systems deal with the
organization, curation, and promotion of most of
information being consumed today
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5. Information, information,
information everywhere!
• IR systems connect people with information
• IR systems reflect the content and interaction data,
and the impact where they are used
• IR systems are shaping not only the information
consumption patterns, but also the social
interactions
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6. The need for responsible IR
• Due to these social and political implications, it is becoming
evident that many issues are concerning all aspects of IR
system development and deployment
- Gaps in information access across communities
- Misinformation, polarization
- Collection of personal data
- Opaque methods for decision making
• Since nowadays a variety of IR systems are used everywhere,
those issues have potentially a wide ranging impact
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7. FACT-IR
• In this presentation, we describe four focus areas
or main concepts on responsible IR:
- Fair IR
- Accountable IR
- Confidential IR
- Transparent IR
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8. Some examples
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9. Some examples
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10. FACT-IR
• These examples illustrate concerns about bias,
unfair representation, harm, misinformation, privacy
• F,A,C,T concepts are multi-dimensional
• In particular, they depend on multiple stakeholders
• The concepts are often related in a given scenario
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11. Fairness in
Information Retrieval
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12. Fairness in IR
• IR systems should avoid discrimination across
people and communities
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13. Fairness in IR
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14. Fairness in IR
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15. Fairness in IR
• IR systems should avoid discrimination across
people and communities
- Avoid unfair conclusions even if they appear true
- Avoid discrimination even when sensitive attributes are
removed
- Avoid bias even under vox populi, ensure diversity
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16. Fairness in IR
• Defining fairness is a complex, multi-dimensional
problem
- Domain dependent
- Time dependent
- Stakeholder dependent
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17. Fairness in IR
• Research directions and challenges:
- Definition of fairness
- Metrics and evaluation
- Intervention criteria
- Data availability
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18. Accountability in
Information Retrieval
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19. Accountability in IR
• IR systems should be able to justify their actions,
and to be reliable at all times
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20. Accountability in IR
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21. Accountability in IR
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22. Accountability in IR
• Research directions and challenges:
- Definition and criteria
- Intervention criteria
- Metrics and evaluation
- Agency, freedom
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23. Accountability in IR
• Impact:
- Short- and long-term influence of information in users
- Representation of people in the information
- Emotional impact in developers, content moderators,
data annotators
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24. Accountability in IR
• Interplay with other concepts:
- Fair and harmless
- Accountable and transparent
- Tension with confidentiality
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25. Confidentiality in
Information Retrieval
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26. Confidentiality in IR
• The output or actions of IR systems should not
reveal secrets
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27. Confidentiality in IR
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28. Confidentiality in IR
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29. Confidentiality in IR
• Research directions and challenges:
- Protection scope
- Confidentiality criteria beyond topical
- Architecture and methods
- Metrics and evaluation
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30. Confidentiality in IR
• Relation with other concepts:
- Biases and sensitive data in different demographics
- Confidentiality criteria in different stakeholders
• Tension with transparency
• Awareness
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31. Confidentiality in IR
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32. Confidentiality in IR
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33. Confidentiality in IR
• Impact:
- By better protecting sensitive content, more information
can be made available
- Increase trust from individuals and organizations
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34. Transparency in
Information Retrieval
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35. Transparency in IR
• IR systems should be able to explain why and how
the results are obtained
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36. Transparency in IR
• Explaining decisions made by the system, like
retrieved search results, recommendations,
answers
- In particular for sensitive decision making
• Tracing origin of results
• Clarifying results such that they are trustworthy
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37. Transparency in IR
• Research directions and challenges:
- Definition, criteria, purpose
- Intervention criteria
- Metrics and evaluation
- Tension with confidentiality
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38. Transparency in IR
• Impact:
- Increase trust from individuals and organizations
- Contribute to accountability
- Contribute to reproducibility
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39. Responsible IR
and the IR community
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40. Impact in IR community
• Definitions, criteria, and methodologies for the
F,A,C,T concepts should consider the multiple
stakeholders
• IR research community should aim for diversity
• Transparency could improve teaching and
reproducibility in IR
• There may be resistance from some stakeholders
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41. Broadening the IR
community
• IR community needs to promote the collaboration
with other disciplines, like social sciences,
psychology, economics, and law
• Also, there should be collaborations with
governmental institutions and organizations,
regarding ethical frameworks and regulations
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42. General Data Protection
Regulation (EU) 2016/679 (GDPR)
• A regulation in EU law on data protection and privacy for
all individual citizens of the European Union (EU) and the
European Economic Area (EEA)
• Some key parts:
- Clear, easy conditions to give and to freely withdraw consent
- Right to data access
- Right to erasure of personal information
- Timely notification in case of security breach
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43. Events in FACT-IR research
• Some workshops related with FACT-IR (recently co-
located with the SIGIR 2019 Conference):
- FACTS-IR 2019 Workshop on fairness, accountability,
confidentiality, transparency, and safety in IR
- EARS 2019 - The 2nd International Workshop on
ExplainAble Recommendation and Search
- NewsIR'19 - 3rd International Workshop on Recent
Trends in News Information Retrieval
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44. Some references
• James Allan et al. Report from the third strategic workshop on information retrieval in
Lorne (SWIRL 2018). SIGIR Forum, 52:34–90, 2018.
• Ricardo Baeza-Yates. Bias on the web. Communications of the ACM, 61(6), 2018.
• EU. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 april
2016 on the protection of natural persons with regard to the processing of personal
data and on the free movement of such data, and repealing directive 95/46/ec (General
Data Protection Regulation). Official Journal of the European Union, L119:1–88, 2016.
• Rishabh Mehrotra et al. Auditing search engines for differential satisfaction across
demographics. In Proceedings of the The Web Conference, pp. 626–633, 2017.
• Alexandra Olteanu et al. (Eds.) FACTS-IR: Fairness, Accountability, Confidentiality,
Transparency, and Safety in Information Retrieval. To appear in SIGIR Forum, 2019.
• Terrance DeVries et al. Does Object Recognition Work for Everyone? In Proceedings of
CVPR Workshops, pp. 52-59, 2019.
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