This document discusses various types of biases that can occur in search engines and recommender systems. It begins by defining statistical, cultural, and cognitive biases. It then explains how user data collected by these systems is biased based on the choices the systems present, and how the systems can learn to reinforce their own biases. The document discusses how personalization can lead to filter bubbles and sub-optimal solutions. It also notes that improvements in one system may degrade another system using a different optimization function. The document emphasizes that biases can occur at many levels, from the data collected to algorithms, rankings, interactions and beyond. It concludes by noting that while biases are problematic, the web itself functions thanks to certain biases like caching and popularity effects.

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Search
Biases, Semantics & Conversations
Ricardo Baeza-Yates
UXDay, November 2018
What is Bias?
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• Statistical: significant systematic
deviation from a prior (unknown)
distribution;
• Cultural: interpretations and judgments
phenomena acquired through our life;
• Cognitive: systematic pattern of deviation
from norm or rationality in judgment;
[ B. Friedman, and H. Nissenbaum. Bias in computer systems.
ACM Transactions on Information Systems, 1996]

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Motivation: Impact in Search and Recommender Systems
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• Many web systems are optimized by using implicit user feedback
• However, user data is partly biased to the choices that these systems make
• Clicks can only be done on things that are shown to us
• As those systems are usually based in ML, they learn to reinforce their own biases,
yielding self-fulfilled prophecies and/or sub-optimal solutions
• For example, personalization and the filter bubble
• Moreover, sometimes these systems compete among themselves, learning also biases
of other systems rather than real user behavior
• Even more, an improvement in one system might be just a degradation in another
system that uses a different (inversely correlated) optimization function
• For example, user experience vs. monetization
Who Should Care?
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• Data scientists
• Frontend developers
• All developers
• Web designers
• Every website is an information market!
• Entrepreneurs
• Everybody
Content
Interaction Incentives

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So (Observational) Human Data has Bias
o Gender
o Racial
o Sexual
o Age
o Religious
o Social
o Linguistic
o Geographic
o Political
o Educational
o Economic
o Technological
§ Gathering process
§ Sampling process
§ Validity (e.g. temporal)
§ Completeness
§ 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 the Web
Cultural Biases Statistical Biases Cognitive Biases
Self-selection
A Non-Technical Question
Algorithm
Biased
Data
Neutral?
Fair?
Same
Bias

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What is being fair?
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A Non-Technical Question
Algorithm
Biased
Data
Neutral?
Fair?
Same
Bias
Not
Always!
Debias the input
Tune the algorithm
Debias the output
Bias awareness!
[Baeza-Yates, CACM, Jun 2018]

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ACM US Statement on Algorithm Transparency and Accountability
(Jan 2017)
11
1. Awareness
2. Access and redress
3. Accountability
4. Explanation
5. Data Provenance
6. Auditability
7. Validation and Testing
Data bias
Bias in the Web
Web Spam

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[Bolukbasi at al, NIPS 2016]
Most journalists are men?
• Word embedding’s in w2vNEWS
Yes, about 60 to 70% at work
although at college is the inverse
Gender Bias in Content
Gender Bias in Translation

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[E. Graells-Garrido et al,. “First Women, Second Sex: Gender Bias in Wikipedia”,
ACM Hypertext’15]
Systemic bias?
Equal opportunity?
Gender Bias in Content
Wikipedia
Partial
information
Data bias
Activity bias
Bias in the Web

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[Baeza-Yates & Saez-Trumper, ACM Hypertext 2015]
Activity Bias
Most users are passive (i.e., more than 90%) – wisdom of crowds is a partial illusion
Which percentage of active users produce 50% of the content?
October 2015

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[Baeza-Yates & Saez-Trumper, ACM Hypertext 2015]
Content that is never seen: Digital Desert
Data bias
Activity bias
Sampling
bias
Algorithmic bias
Algorithm

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Extreme Algorithmic Bias
Data bias
Activity bias
Sampling
bias
Algorithmic bias
Interaction bias
(Self) selection bias
Privacy
Algorithm
Bias in the Web

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Position bias
Ranking bias
Presentation bias
Social bias
Interaction bias
Bias in the Interaction
Amazon.com
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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Ranking Bias in Web Search
[Mediative Study, 2014]
o Ranking & next page bias
Navigational queries
Click Bias in Web Search

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CTR
(log)
1 11 21 Rank
Learning to Rank with bias
[Joachims et al, WSDM 2017, best paper]
Fair rankings
[Zehlike et al, CIKM 2017]
Clicks as implicit positive user feedback
Debiasing Search Clicks
[Dupret & Piwowarski, SIGIR 2008]
[Chapelle & Zhang, WWW 2009]
Data bias
Activity bias
Sampling
bias
Algorithmic bias
Interaction bias
(Self) selection bias
Second-order bias
Sparsity
Privacy
Algorithm
Bias in the Web

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The Filter “Bubble”, Eli Pariser (2011)
• The effect of self selection bias
• Avoid the poor get poorer syndrome
• Avoid the echo chamber
• Empower the tail
Cold start problem solution: Explore & Exploit
Partial solutions:
• Diversity
• Novelty
• Serendipity
• My dark side
Avoid Second Order Bias due to Personalization
How much exploration is needed for
presentation bias?
Wikipedia
• Exploit the context (and deep learning!)
91% accuracy to predict the next app you will use
[Baeza-Yates et al, WSDM 2015]
• Personalization vs. Contextualization
Recall that user interaction is another long tail
Persons
Tasks
Aggregating in the Tail

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[De Choudhury et al, ACM HT 2010]
[Baeza-Yates, Pereira & Ziviani,
Genealogical Trees in the Web, WWW 2008]
Person
Web content is redundant (> 20%)
Clicks in results are biased to
the ranking and the interaction
Query
Ranking bias in new content
Redundancy grows (35%)
Search results
New
Second Order Bias in Web Content
[Fortunato, Flammini, Menczer & Vespignani. Topical interests and
the mitigation of search engine bias. PNAS 2006]

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The Web Works Thanks to Bias!
§ Web traffic
• Local caching
• Proxy/network caching
§ Search engines
• Answer caching
• Essential web pages
› 25% queries can be answered with less than 1% of the URLs!
[Baeza-Yates, Boldi, Chierichetti, WWW 2015]
§ E-Commerce
• Large fraction of revenue comes from few popular items
Activity bias
(Self) selection bias
Recap
Bias Type Statistical Cultural Cognitive
Algorithmic ¨ ? ?
Presentation ¨
Position ¨ ¨ ¨
Data ¨ ¨
Sampling ¨ ¨ ¨
Activity ¨
Self-selection ¨ ¨
Interaction ¨ ¨
Social ¨ ¨
Second order ¨ ¨ ¨

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è 61 analysts, 29 teams: 20 yes and 9 no (Univ. of Virginia, COS)
It’s Hard to Get the Truth from Data (Professional Bias)
Semantic Search

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Generic
Mammal
Intermediate
Feline
Specific
Cheetah
Semantic Resources
o Composed of Four Parts
• Ontology (LI)
• Lexicon (LD)
• Onomasticon (mostly LI)
• Linguistic Rules (SemReps, Case Frames)
Generic
People
Intermediate
Singer
Specific
Lady Gaga
Mobile Search Results – Beyond Blue Links
40

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Conversational Search
KB
User
Model State & Context
Voice(s)
Action(s)
The Future?
“The real problem with robots is not their AI intelligence but
the natural stupidity and cruelty of their human masters” [Harari, 2018]