Making Information Systems Good for People ⓒ
Chained to the Rhythm
Learning Analogies
Analogies Run Amok
What Happened?
What We Do • • • • • •
How Do They Work?
Finding Patterns
Recommender Vocabulary 🌾 🍖 ⚗ 🎂 🚳 👹 🙌 🕵 👰 👾 🌠 💜 👎💳
Recommender Architecture
User-Based Recommendations
Item-Based Recommendations
Matrix Factorization
Other Techniques • • • •
How Do We Know It Worked? Offline evaluation Online evaluation (A/B testing) Lab-style user studies
Experimental Protocol
building researching learning about
LensKit in Use • • • • • • • •
When Recommenders Fail Ekstrand and Riedl, RecSys 2012 😑 🙃 ☹
User-Perceived Differences Ekstrand et al., RecSys 2014
Experiment Features
Results in Differences • • • • • •
Problems with Evaluation Ekstrand and Mahant, FLAIRS 2017 • • ☒ •
Misclassified Decoys
Sturgeon’s Law
Sturgeon’s Decoys
Who Benefits from Recommendations?
Our Question
Fairness in Recommendation and Search Consumers Producers 🕻💄🤷🐷 🧕👶🎆 🧜👹🧚 Individuals Groups
Data • • • • • •
Gender
Age
Differences Exist • • ⇒ • •
Reciprocity [Franklin, 1989]
Giving Users a Voice
LITERATE
Sample of Ethical Issues • • • • • • • •
ACM Code of Ethics
Propagating Bias? (Under Review)
Feedback Loops (Future Work)
Promote Misinformation • • •
Segment Society
Promote Clickbait
Limits of Behavioral Observation
Information Disclosure
In Search of James Comey • • •
Fair Privacy (w/ Hoda Mehrpouyan, FAT* 2018) • • •
Beyond Recommenders • • • • • • •
The Real World of Technology • • • •
Paths Forward • • • • •
Thank You
Recommend
More recommend
