FACT-IR. Fairness, Accountability, Confidentiality and Transparency in Information Retrieval

FACT-IR. Fairness, Accountability, Confidentiality and Transparency in Information Retrieval

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  FACT-IR Fairness, Accountability, Confidentiality andTransparency in Information Retrieval Darío Garigliotti University of Stavanger, Norway December 4th, 2019
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  Outline • An introductionto responsible Information Retrieval • Fairness in IR • Accountability in IR • Confidentiality in IR • Transparency in IR • Responsible IR and the IR community 2
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  An introduction to responsible InformationRetrieval 3
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  Information Retrieval systems • InformationRetrieval (IR) systems deal with the organization, curation, and promotion of most of information being consumed today 4
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  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 5
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  The need forresponsible 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 6
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  FACT-IR • In thispresentation, we describe four focus areas or main concepts on responsible IR: - Fair IR - Accountable IR - Confidential IR - Transparent IR 7
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  Some examples 8
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  Some examples 9
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  FACT-IR • These examplesillustrate 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 10
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  Fairness in Information Retrieval 11
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  Fairness in IR •IR systems should avoid discrimination across people and communities 12
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  Fairness in IR 13
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  Fairness in IR 14
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  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 15
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  Fairness in IR •Defining fairness is a complex, multi-dimensional problem - Domain dependent - Time dependent - Stakeholder dependent 16
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  Fairness in IR •Research directions and challenges: - Definition of fairness - Metrics and evaluation - Intervention criteria - Data availability 17
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  Accountability in Information Retrieval 18
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  Accountability in IR •IR systems should be able to justify their actions, and to be reliable at all times 19
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  Accountability in IR 20
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  Accountability in IR 21
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  Accountability in IR •Research directions and challenges: - Definition and criteria - Intervention criteria - Metrics and evaluation - Agency, freedom 22
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  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 23
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  Accountability in IR •Interplay with other concepts: - Fair and harmless - Accountable and transparent - Tension with confidentiality 24
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  Confidentiality in Information Retrieval 25
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  Confidentiality in IR •The output or actions of IR systems should not reveal secrets 26
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  Confidentiality in IR 27
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  Confidentiality in IR 28
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  Confidentiality in IR •Research directions and challenges: - Protection scope - Confidentiality criteria beyond topical - Architecture and methods - Metrics and evaluation 29
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  Confidentiality in IR •Relation with other concepts: - Biases and sensitive data in different demographics - Confidentiality criteria in different stakeholders • Tension with transparency • Awareness 30
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  Confidentiality in IR 31
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  Confidentiality in IR 32
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  Confidentiality in IR •Impact: - By better protecting sensitive content, more information can be made available - Increase trust from individuals and organizations 33
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  Transparency in Information Retrieval 34
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  Transparency in IR •IR systems should be able to explain why and how the results are obtained 35
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  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 36
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  Transparency in IR •Research directions and challenges: - Definition, criteria, purpose - Intervention criteria - Metrics and evaluation - Tension with confidentiality 37
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  Transparency in IR •Impact: - Increase trust from individuals and organizations - Contribute to accountability - Contribute to reproducibility 38
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  Responsible IR and theIR community 39
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  Impact in IRcommunity • 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 40
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  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 41
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  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 42
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  Events in FACT-IRresearch • 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 43
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  Some references • JamesAllan 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. 44
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