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Keynote: Bias in Search and Recommender Systems
1.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost #TechSEOBoost | @CatalystSEM THANK YOU TO THIS YEARâS SPONSORS Keynote: Bias in Search and Recommender Systems Ricardo Baeza-Yates, NTENT
2.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Ricardo Baeza-Yates CTO, NTENT Biases in Search & Recommender Systems
3.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost NTENT ntent.com Marketing Engineering Operations International Applied Research ntent.com
4.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Prologue
5.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost A Bit of History Data Volume Complexity IR DB Two different points of view for data
6.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Data Understanding Data Query Unstructured Structured Explicit Information Retrieval (Relational) Databases Implicit Recommender Systems Unknown Data Mining
7.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost What is Bias? âą Statistical: significant systematic deviation from a prior (unknown) distribution; âą Cultural: interpretations and judgments phenomena acquired through our life; âą Cognitive: systematic pattern of deviation from norm or rationality in judgment;
8.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost So (Observational) Human Data has Bias âą Gender âą Racial âą Sexual âą Age âą Religious âą Social âą Linguistic âą Geographic âą Political âą Educational âą Economic âą Technological âȘ Gathering process âȘ Sampling process âȘ Validity (e.g. temporal) âȘ Completeness âȘ Noise, spam Many people extrapolate results of a sample to the whole population (e.g., social media analysis) In addition there is bias when measuring bias as well as bias towards measuring it! Attempt of an unbiased (personal) view on bias in Search & RS Cultural Biases Statistical Biases Cognitive Biases Self-selection
9.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Impact in Search and Recommender Systems âą Most web systems are optimized by using implicit user feedback âą However, user data is partly biased to the choices that these systems make âą Clicks can only be done on things that are shown to us âą As those systems are usually based in ML, they learn to reinforce their own biases, yielding self-fulfilled prophecies and/or sub-optimal solutions âą For example, personalization and filter bubbles for users âą but also echo chambers for (recommender) systems âą Moreover, sometimes these systems compete among themselves, learning also biases of other systems rather than real user behavior âą Even more, an improvement in one system might be just a degradation in another system that uses a different (even inversely correlated) optimization function âą For example, user experience vs. monetization
10.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost A Non-Technical Question Algorithm Biased Data Neutral? Fair? Same Bias Garbage In Garbage Out
11.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost What is being fair?
12.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost A Non-Technical Question Algorithm Biased Data Neutral? Fair? Same Bias Not Always! Debias the input Tune the algorithm Debias the output Bias awareness!
13.
Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost January 2017 ACM US Statement on Algorithm Transparency and Accountability 1. Awareness 2. Access and redress 3. Accountability 4. Explanation 5. Data Provenance 6. Auditability 7. Validation and Testing Systems do not need to be perfect, they just need to be better than us
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Biases Everywhere!
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Data bias Biases on Search & RS: Web Case Study Web Spam
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost [Baeza-Yates, Castillo & LĂłpez, Cybermetrics, 2005] Number of linked domains Exports(thousandsofUS$) Economic Bias in Links
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost 17 [Baeza-Yates & Castillo, WWW2006] Economic Bias in Links
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost 18 Minimal effortShameCultural Bias in Websites [Baeza-Yates, Castillo, Efthimiadis, TOIT 2007]
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Language Bias
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost [Bolukbasi at al, NIPS 2016] âą Word embeddingâs in w2vNEWS Yes, about 60 to 70% at work although at college is the inverse Gender Bias in Content Most journalists are men?
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost [E. Graells-Garrido et al,. ACM Hypertextâ15] Systemic bias? Equal opportunity? Gender Bias in Content Wikipedia Partial information
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Data bias Activity bias Bias on Usage Actions People We are all in the long tail! [Goel et al., WSDM 2010]
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Popularity Bias in Recommender Systems Items Users Popular items Rest of items (long tail) âą Take care to recommended items that are not too popular âą Metrics âą Novelty enhancement âą Problem solved! âŠreally? đđđŁ đ = 1 â # ratings of đ # users Items #interactions More novel Less novel đ đ [Vargas & Castells, RecSys 2011]
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost A Self-fulfilling Prophecy? Popular items (short head) Rest of items (long tail) Observed user-item interaction Unobserved preference Items Users Ratings are missing not at random (MNAR)
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Test data (relevant items) Training data Unobserved preference Items Users Popular items (short head) Rest of items (long tail) avg P@đ ⌠+ đ Judgments are missing not at random (MNAR) Worse yet: user-system reinforcement loop (more later) A Self-fulfilling Prophecy? [Marlin et al., RecSys 2010] [Steck, RecSys 2010, 2011] [Fleder & Hossanagar, Management Sciences 2009]
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost 1.E-06 1.E-05 1.E-04 1.E-03 0 0.2 0.4 0.6 0.8 1 A Problem for IR Evaluation Methodology! 30 TREC collections Items / documents #ratings/judgments (infraction) [BellogĂn, Castells & Cantador, IRJ 2017] To how many queries is a document relevant? 25% queries can be answered with less than 1% of the URLs! [Baeza-Yates, Boldi, Chierichetti, WWW 2015]
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Get Rid of the Popularity Bias! âą In the rating split [BellogĂn, Castells & Cantador, IRJ 2017] âą In the metrics âą Stratified recall [Steck, RecSys 2011] âą Importance propensity scoring [Yang et al., RecSys 2018] âą In the algorithms [Steck, RecSys 2011] [Lobato et al., ICML 2014] [Jannach et al., UMUAI 2015] [Cañamares & Castells, SIGIR 2018, best paper award] Test data (relevant items) Training data Unobserved preference Items Items #ratings Flat test Popularity strata #ratings Time Temporal split
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Activity Bias also Affects Content [Baeza-Yates & Saez-Trumper, ACM Hypertext 2015] Most users are passive (i.e., more than 90% are lurkers) Then, which percentage of active users produce 50% of the content?
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost October 2015
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost [Baeza-Yates & Saez-Trumper, ACM Hypertext 2015] Which percentage of active users produce 50% of the content? Wisdom of crowds is a partial illusion Activity Bias: The Wisdom of a Few
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Content Never Seen [Baeza-Yates & Saez-Trumper, ACM Hypertext 2015] The Digital Desert
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Data bias Activity bias Sampling (size) bias Algorithmic bias Search or Recommender System Bias on the Web
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost [E. Graells-Garrido & M. Lalmas, ACM Hypertextâ14] Geographical Bias in Recommender Systems
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost âą If we want to estimate the frequency of queries that appear with probability at least p with a certain relative error, â, we can use the standard binomial error formula which works well for p near Âœ but not for p near 0 âą Better is the Agresti-Coull technique (also called Take 2) which gives: where Z is the inverse of the standard normal distribution, 1 â đŒ is the confidence interval and âą If p = 0.1, 1 â đŒ is 80% and â is 10%, the standard formula gives n = 900, while with A-C we get n = 2342. [Brown, Cai & DasGupta, Statistical Science, 2001] [Baeza-Yates, SIGIR 2015, Industry track] Sample Size?
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost âą Standard technique: âą A good sample should cover well all the items distribution, but this does not work with very skewed distributions. 10 0 10 1 10 2 10 3 Rank 10 0 10 1 10 2 10 3 10 4 10 5 Frequency 50M 10M 100K 1K 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 Rank 10 0 10 1 10 2 10 3 10 4 10 5 Frequency 50M 10M 100K 1K [Zaragoza et al, CIKM 2010] Sampling Queries
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost 36 Stratified Sampling Example [Baeza-Yates, SIGIR 2015, Industry track]
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Data bias Activity bias Sampling bias Interaction bias (Self) selection bias Bias in the User Interaction Search or Recommender System
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Position bias Ranking bias Presentation or exposure bias Social bias Interaction bias Bias in the Interaction Amazon.com
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Position bias Presentation bias Social bias Interaction bias Ranking bias Click bias Scrolling bias Mouse movement bias Data and algorithmic bias Self-selection bias Dependencies: A Cascade of Biases!
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Ranking Bias in Web Search [Mediative Study, 2014]
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Ranking Bias: Click Bias in Web Search âą Ranking & next page bias Navigational queries
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost CTR (log) 1 11 21 Rank Learning to Rank with bias [Joachims et al., WSDM 2017, best paper] + many other papers Fair rankings [Zehlike et al., CIKM 2017] Clicks as implicit positive user feedback Debiasing Search Clicks and Other Biases [Dupret & Piwowarski, SIGIR 2008] [Chapelle & Zhang, WWW 2009] [Dupret & Liao, WSDM 2010, best paper] Debias the input Tune the algorithm Debias the output
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Data bias Activity bias Sampling bias Interaction bias (Self) selection bias Second-order bias Vicious Cycle of Bias Search or Recommender System
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost [Baeza-Yates, Pereira & Ziviani, WWW 2008] Person Web content is redundant (> 20%) Query Ranking bias in new content Redundancy grows (35%) Search results New page Second Order Bias in Web Content [Fortunato, Flammini, Menczer & Vespignani. PNAS 2006]
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Data bias Activity bias Sampling bias Interaction bias (Self) selection bias Vicious Cycle of Bias Search or Recommender System Feedback loop bias
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Bias due to Personalization âą The effect of self-selection bias âą Avoid the rich get richer and poor get poorer syndrome âą Avoid the echo chamber by empowering the tail Cold start problem solution: Explore & Exploit Partial solutions: âą Diversity âą Novelty âą Serendipity âą My dark side Wikipedia [Eli Pariser, Penguin 2011]
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Usersâ Eco Chambers in Recommender Systems âȘ Filter bubbles âȘ Degenerate feedback loops (e.g., YouTube autoplay) [Jiang et al., AAAI 2019]
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Eco Chamber of the Recommender System âą Short-term greedy optimization, partial knowledge of the world âą Long-term revenue optimization is not achieved âą Views from new users should balance the exploration for new items âą Disparate impact: unfair ecommerce/information markets [Baeza-Yates & Ribaudo, to appear]
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Epilogue
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Recap Bias Type Statistical Cultural Cognitive Algorithmic ïš ? ? Presentation ïš Position ïš ïš ïš Data ïš ïš Sampling ïš ïš ïš Activity ïš Self-selection ïš ïš Interaction ïš ïš Social ïš ïš Second order ïš ïš ïš
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost [Silberzahn et al., COS, Univ. of Virginia, 2015] Professional Bias? â 61 analysts, 29 teams: 20 yes and 9 no
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost If Systems Reflects Its Designers: What we can/should do? âȘ Data âȘ Analyze for known and unknown biases, debias when possible/needed âȘ Recollect more data for difficult/sparse regions of the problem âȘ Delete attributes associated directly/indirectly with harmful bias âȘ Interaction âȘ Make sure that the user is aware of the biases all the time âȘ Give more control to the user âȘ Design and Implementation âą Let experts/colleagues/users contest every step of the process âȘ Evaluation âą Do not fool yourself!
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Final Take-Home Message âȘ Systems are a mirror of us, the good, the bad and the ugly âȘ The Web amplifies everything, but always leaves traces âȘ We need to be aware of our own biases! âȘ We must be aware of the biases and contrarrest them to stop the vicious bias cycle âȘ Plenty of open (research) problems! Big Data of People is hugeâŠ.. âŠ.. but it is tiny compared to the future Big Data of the Internet of Things (IoT) No activity bias!
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Thank You! Any Questions? â rbaeza@acm.org | http://www.baeza.cl/ | http://fairness-measures.org Biased Questions? ASIST 2012 Book of the Year Award (Biased Ad)
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Ricardo Baeza-Yates ©
| @polarbearby | #TechSEOBoost Thanks for Viewing the Slideshare! â Watch the Recording: https://youtube.com/session-example Or Contact us today to discover how Catalyst can deliver unparalleled SEO results for your business. https://www.catalystdigital.com/
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