SLIDE 1 Making Information Systems Good for People
β
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Chained to the Rhythm
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Learning Analogies
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Analogies Run Amok
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What Happened?
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What We Do
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How Do They Work?
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Finding Patterns
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Recommender Vocabulary
πΎ π β π π³ πΉ π π΅ π° πΎ π π ππ³
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Recommender Architecture
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User-Based Recommendations
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Item-Based Recommendations
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Matrix Factorization
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Other Techniques
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How Do We Know It Worked?
Offline evaluation Online evaluation (A/B testing) Lab-style user studies
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Experimental Protocol
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building researching learning about
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LensKit in Use
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When Recommenders Fail
Ekstrand and Riedl, RecSys 2012
π π βΉ
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User-Perceived Differences
Ekstrand et al., RecSys 2014
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Experiment Features
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Results in Differences
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Problems with Evaluation
Ekstrand and Mahant, FLAIRS 2017
β
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Misclassified Decoys
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Sturgeonβs Law
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Sturgeonβs Decoys
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Who Benefits from Recommendations?
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Our Question
SLIDE 36 Fairness in Recommendation and Search
Consumers Producers Groups π»ππ€·π· π§πΆπ π§πΉπ§ Individuals
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Data
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Gender
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Age
SLIDE 40 Differences Exist
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Reciprocity [Franklin, 1989]
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Giving Users a Voice
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LITERATE
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Sample of Ethical Issues
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ACM Code of Ethics
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Propagating Bias?
(Under Review)
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Feedback Loops
(Future Work)
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Promote Misinformation
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Segment Society
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Promote Clickbait
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Limits of Behavioral Observation
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Information Disclosure
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Fair Privacy
(w/ Hoda Mehrpouyan, FAT* 2018)
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Beyond Recommenders
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The Real World of Technology
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Paths Forward
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Thank You