1. Hands on Data and Algorithmic
Bias in Recommender Systems
ACM UMAP2020
28th
Conference on User Modeling, Adaptation and Personalization
July 12, 2020 – ONLINE from GENOA
2. About us
2
Ludovico Boratto
Senior Research Scientist
Data Science and Big Data Analytics
EURECAT - Centre Tecnológic de Catalunya
Barcelona, Spain
ludovicoboratto.com
ludovico.boratto@acm.org
Mirko Marras
Postdoctoral Researcher
Department of Mathematics and Computer Science
University of Cagliari
Cagliari, Italy
mirkomarras.com
mirko.marras@unica.it
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
3. Learning objectives
● Raise awareness on the importance and the relevance of considering data and
algorithmic bias issues in recommendation
● Play with recommendation pipelines and conduct exploratory analysis aimed at
uncovering sources of bias along them
● Showcase approaches that mitigate bias along with the recommendation pipeline
and assess their influence on stakeholders
● Provide an overview on the current trends and challenges in bias-aware research
and identify new research directions
3Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
4. Outline and scheduling
● 10:10 - 11:30 Session I: Foundations
○ 10:10 - 10:20 Recommendation Principles
○ 10:20 - 11:30 Data and Algorithmic Bias Fundamentals
● 11:30 - 12:00 Zoom Breakout Room + Q&A
● 12:00 - 13:10 Session II: Hands-on Case Studies
○ 12:00 - 12:15 Recommender Systems in Practice
○ 12:15 - 12:40 Investigation on Item Popularity Bias
○ 12:40 - 13:10 Investigation on Item Provider Fairness
● 13:10 - 13:20 Research Challenges and Emerging Opportunities
● 13:20 - 14:00 Open Discussion + Q&A
All times are displayed in conference local time (UTC+00:00)
4Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
11. Capitalizing on recommender systems
A recommender system suggests items that might be relevant for a user
11Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Recommendation principles
12. The recommendation ranking task
12
● Given:
○ a set of consumers C = {c1
, c2
, ..., cM
}
○ a set of items I = {i1
, i2
, ..., iN
}
● Let R ⊆ R M ×N
be the consumer-item feedback matrix:
○ R(c,i) ≥ 0 if consumer c expressed interest in item i
○ R(c,i) = 0 otherwise
● The objective is to predict unobserved consumer-item feedback R(c,i) = f(c,i | θ) in R:
○ θ denotes model parameters
○ f denotes the function that maps model parameters to the predicted relevance
● Given a consumer c, items not rated by c are ranked by decreasing relevance:
i* = arg max f(c,j | θ)
j ∈ I Ic
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Recommendation principles
13. Modes of optimization
13
● Pointwise optimization
point-wise approaches take a user-item pair and predict how relevant the item is for that user
● Pairwise optimization
pair-wise approaches digest a triplet of user, observed item, and unobserved item, and minimize
the cases when the unobserved item is more relevant than the observed item for that user
● Listwise optimization
list-wise approaches look at the entire list and build the optimal ordering for that user
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Recommender systems in practice
14. Core recommendation techniques
Adapted from [Ricci et al. 2015]
14
Technique Background Input Process
Collaborative Ratings from C of items in I Ratings from c of items in I Identify users in C similar to c and
extrapolate from their preferences of i
Content-based Features of items in I Ratings from c of items in I Generate a classifier that fits c's rating
behavior and use it on i
Demographic Demographic information on C and
their ratings of items in I
Demographic information on c Identify users that are demographically
similar to c and extrapolate from their
preferences of i
Utility-based Features of items in I A utility function over items in I that
describes c's preferences
Apply the function to the items and
determine i's rank
Knowledge-based Features of items in I and knowledge of
how these items meet a user's need
A description of c's needs or interests Infer a match between i and c's needs or
interests
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Recommendation principles
15. Core stakeholders in recommendation
[Abdollahpouri et al. 2020]
A recommendation stakeholder is any group or individual that can affect, or is affected
by, the delivery of recommendations to users
15
Consumers Providers System
C P S
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Recommendation principles
16. Multi-sided recommendation aspects
[Abdollahpouri et al. 2020]
16
Aspect Definition
Multi-stakeholder design A multistakeholder design process is one in which different recommendation stakeholder groups are
identified and consulted in the process of system design
Multi-stakeholder algorithm A multistakeholder recommendation algorithm takes into account the preferences of multiple parties when
generating recommendations, especially when these parties are on different sides of the recommendation
interaction
Multi-stakeholder evaluation A multistakeholder evaluation is one in which the quality of recommendations is assessed across multiple
groups of stakeholders, in addition to a point estimate over the full user population
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Recommendation principles
17. A sample multi-sided scenario
17
Consumers
Students
Providers
Teachers
System
Online Course Platform
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Recommendation principles
19. Motivating example in music
[Mehrotra et al. 2018]
● People frequently listen to music online
● Ratings and frequencies often used to learn patterns
● 1/3 of users listen to at least 20% of unpopular artists
● Why are popular artists favoured?
● Why do users who tend to interact with niche artists
receive the worst recommendations?
19Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentals > Motivating examples
20. Motivating example in education
[Boratto et al. 2019]
● Online course platforms are receiving great attention
● Student's preferences learnt from ratings/enrolments
● The imbalance in popularity among courses reinforces
coverage and concentration biases of ranked courses
● Popularity bias could impede new courses to emerge
● The market could be dominated by a few teachers
20Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentals > Motivating examples
21. Motivating example in social platforms
[Edizel et al. 2020]
● Reading users' stories is a common activity nowadays
● Users can vote stories up and down
● Gender attributes are not supported by Reddit
● Why recommender systems reinforce the imbalance
between genders while suggesting reddits?
● 95% (87%) of sub-reddits popular among females
(males) show imbalance reinforcement over genders
21Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentals > Motivating examples
22. Motivating example in recruiting
[Singh et al. 2018]
22
● Recruiters rely more and more on automated systems
● Based on the job, "best" candidates are suggested
● Small differences in relevance can lead to large
differences in exposure among candidate groups
● Is this winner-take-all allocation of exposure fair, even
if the winner just has a tiny advantage in relevance?
● It might be fairer to distribute exposure proportional
to relevance, even if this leads to a drop in utility
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentals > Motivating examples
23. Disclaimers
23Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
● We aim to focus on scientific literature that specifically consider recommender systems
● Pointers to representative scientific events on related concepts applied to ranking systems are given
● References discussed throughout the slides would not be exhaustive
● Refer to the extended bibliography attached to this tutorial for a more comprehensive list of papers
Data and algorithmic bias fundamentals
24. Related scientific venues and initiatives
24Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Special issues in journals
including:
Scientific tutorials
including:
Dedicated workshops or tracks
including:
Papers in top-tier conferences
including:
● RecSys
● SIGIR
● The Web Conf
● TREC
● UMAP
● WSDM
● CIKM
● ECIR
● KDD
● FAccTRec @ RecSys 2018-2020
● RMSE & Impact RS @ RecSys 2019
● FACTS-IR @ SIGIR 2019
● FATES @ The Web Conf 2019
● FAIR-TREC @ TREC 2020
● FairUMAP @ UMAP 2018-2020
● DAB @ CIKM 2017-2019
● Bias @ ECIR 2020
● FAT-ML @ ICML 2014-2019
● Fairness and Discrimination in Retrieval and Recommendation @ SIGIR 2019 & RecSys 2019
● Learning to Rank in theory and practice: From Gradient Boosting to Neural Networks and Unbiased Learning @ SIGIR 2019
● Multi-stakeholder Recommendations: Case Studies, Methods and Challenges @ RecSys 2019
● Experimentation with fairness-aware recommendation using librec-auto @ FAT 2020
● Special Issue on Fair, Accountable, and Transparent Recommender Systems @ UMUAI
● Special Issue on Algorithmic Bias and Fairness in Search and Recommendation @ IPM
Data and algorithmic bias fundamentals > Scientific context
25. Perspectives impacted by bias
25Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Economic
Legal
Social
Security
Technological
bias can introduce
disparate impacts
among providers,
influencing future success
and revenues
bias can affect core user's
rights that are regulated
by law, such as fairness
and discrimination
bias can reinforce
discrimination of certain
user's groups, including
ageism, sexism,
homophobia
bias can lead certain
groups of users or an
entire system to be more
vulnerable to attacks
(e.g., bribery)
bias can influence how
technologies progress and
can be amplified as the
algorithms evolve
Data and algorithmic bias fundamentals > Impacted perspectives
26. Law and rights impacted by bias
[Tolan et al. 2019]
● The right to non-discrimination, which can be undermined by inherent biases, is
embedded in the normative framework of the European Union, e.g.:
○ Explicit mentions of it can be found in Article 21 of the EU Charter of Fundamental Rights
○ Article 14 of the European Convention on Human Rights
○ Articles 18-25 of the Treaty on the Functioning of the European Union
● As an example, United Nations Sustainable Development Goal 4 aims also to "ensure
inclusive and equitable quality education and promote lifelong learning opportunities for all"
○ In Yao et al,. [2017], the authors observed that, in 2010, women accounted for only 18% of the
bachelor’s degrees awarded in computer science. The underrepresentation of women causes
historical rating data of computer-science courses to be dominated by men. Consequently, the
learned model may underestimate women’s preferences and be biased toward men.
26Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentals > Impacted perspectives
27. Social aspects associated to bias
[Fabbri et al. 2020]
27Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
● Context: people recommendation in social networks, with users divided into groups based on gender
● Algorithm: Adamic-Adar, SALSA, ALS
● Findings: people recommenders produce disparate visibility on the two subgroups. Homophily plays a
key role in promoting or reducing visibility for different subgroups.
Data and algorithmic bias fundamentals > Impacted perspectives
Lorenz Curves (inequality). Recommendations
introduce more inequality than the degree
distribution, and this inequality is stronger in
the minority class.
28. ● Context: Sellers might attack the system by introducing a bias in the ratings
○ Attack goal: bribe the users to increase ratings and push item recommendations
● Algorithm: Novel hybrid KNN CF (each user is represented as a compressed string)
● Findings: profitability associated to increasing the ratings is strongly reduced w.r.t. SV. Downgrading
the ratings of competitors is not profitable with this approach, while it is with SVD. System is more
robust to attacks and more trustable by the users.
Security aspects undermined by bias
[Ramos et al. 2020]
28Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentals > Impacted perspectives
29. Ethical aspects influenced by bias
[Bozdag 2013, Milano et al. 2020]
29Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Content
recommendation of
inappropriate content
Opacity
black-box algorithms, uninformative
explanations, feedback effects
Privacy
unauthorised data collection, data
leaks, unauthorised inferences
Fairness
observation bias,
population imbalance
Autonomy and Identity
behavioural traps and encroachment
on sense of personal autonomy
Social
lack of exposure to contrasting
viewpoints, feedback effects
Data and algorithmic bias fundamentals > Influenced ethical aspects
30. Bias leading to inappropriate content
[Pantakar et al. 2019]
30Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentals > Influenced ethical aspects
● Context: news recommender system who generates
awareness on biased news, to possibly avoid fake and
politically polarized news
● Algorithm: news clustering and bias score attached to
each news. Recommendation of similar, unbiased content
● Findings: live-user evaluation. The rankings generated by
the algorithm matches with the ones the users would
generate
31. Privacy of user representations
[Resheff et al. 2018]
31Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
● Context: user representations may be used to recover private user information such as gender and age
● Algorithms: privacy-adversarial framework to eliminate leakage of private information. An adversarial
component is appended to the model for each of the demographic variables to obfuscate, so that the
learned user representations are optimized to preclude predicting the variables
● Findings: privacy preserving recommendations, minimal overall adverse effect on recommender
performance, fairness of results (all knowledge of the attributes is scrubbed from the representations
used by the model)
Data and algorithmic bias fundamentals > Influenced ethical aspects
32. Influence of bias on autonomy
[Arnold et al. 2018]
32Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentals > Influenced ethical aspects
● Context: text recommender systems that support creative tasks
(domain: writing restaurant reviews). Do they exhibit
unintentional biases in the support that they offer? Do these
biases affect what people produce using these systems?
● Algorithms: contextual recommendations: (1) it selects the three
most likely next-word predictions, (2) it generates the most likely
phrase continuation for each word using beam search
● Findings: People who get recommended phrasal text entry
shortcuts that are skewed positive, write more positive reviews
than when presented with negative-skewed shortcuts
33. Objectives influenced by bias
[Kaminskas et al. 2017, Namatzadeh et al 2018, Singh et al. 2018]
33Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Utility
Recommendation
Objectives
Novelty
Diversity
Coverage
Serendipity
the degree to which recommended
items are potentially useful and of
interest for the user
the degree of attention
received by (groups of)
items or providers
the degree to which the list has
valuable items not looked for and
generate surprise for the user
the degree to which the generated
recommendations cover the catalog
of available items
the degree to which the list of
retrieved items covers a broad area
of the information space
the degree to which items are unknown
by the user and/or are different from
what the user has seen before
Visibility &
Exposure
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Influenced objectives
34. Impact of bias on utility
[Fu et al. 2020]
34Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Influenced objectives
● Context: study recommendation performance
according to the level of activity of users. Inactive
users are more susceptible to unsatisfactory
recommendations (insufficient training data) +
recommendations are biased by the training records of
more active users.
● Algorithm: explainable CF + re-ranking to balance
predictions and group/individual fairness
● Findings: reduce disparity in utility while preserving
recommendation quality
35. Impact of bias on diversity
[Channamsetty and Ekstrand 2017, Lee and Hosanagar 2019]
● CF does not propagate users’ preferences for popularity and diversity into the
recommendations → lack of personalization
● Recommender systems lead to a decrease in sales diversity w.r.t. environments w/o
recommendations
● Recommenders can help individuals explore new products, but similar users end up
exploring the same kinds of products resulting in concentration bias at the
aggregate level
35Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Influenced objectives
36. Trade-offs often come up
[Leonhardt et al. 2018, Boratto et al. 2020a]
● The introduction of diversity thanks to a
post-processing leads to an increasing disparity in
recommendation quality
36Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Influenced objectives
● Debiasing NMF and BPR in terms of
popularity leads to a trade-off between
accuracy and beyond-accuracy metrics
37. Recourse and item availability
[Dean et al. 2020]
37Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Influenced objectives
● Context: The amount of recourse available to a user is the percentage of unseen items that are
reachable (user-centric perspective). The availability of items in a recommender system is the
percentage of items that are reachable by some user (item-centric perspective).
● Algorithms: Linear preference models (SLIM and MF)
● Findings: unavailable items are systematically less popular than available items. Users with smaller
history lengths have more available recourse
39. Recommendation pipeline
39
Platform
Data
ModelRecommendations
Data Acquisition
and Storage
Data
Preparation
Model
Prediction
Recommendation
Delivering
Model
Evaluation
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Pre-
Processed
Data
Model Setup
and Training
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
40. Types of bias associated to users
[Olteanu et al. 2017]
● Population biases
differences in demographics or other user characteristics, between a population of users represented in a
dataset/platform and a target population
● Behavioral biases
differences in user behavior across platforms or contexts, or across users represented in different
datasets
● Content biases
behavioral biases that are expressed as lexical, syntactic, semantic, and structural differences in the
contents generated by users
● Linking biases
behavioral biases that are expressed as differences in the attributes of networks obtained from user
connections, interactions or activity
● Temporal biases
differences in populations or behaviors over time
40Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
41. Bias on items due to their popularity
[Jannach et al. 2014]
● Context: movies, books, hotels, and mobile games
● Algorithms: CB-Filtering, SlopeOne, User-KNN, Item-KNN, FM (MCMC), RfRec, Funk-SVD,
Koren-MF, FM (ALS), BPR
● Findings: techniques performing well on accuracy focus their recommendations on a tiny fraction of
the item spectrum or recommend mostly top sellers
41Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
42. To what degree popularity is good?
[Cañamares and Castells 2018]
● Context: movies
● Algorithms: User KNN, Item KNN, AvgRating, MF, Random, Pop
● Findings: effectiveness or ineffectiveness of popularity depends on the interplay of three main
variables: item relevance, item discovery by users, and the decision by users to interact with
discovered items. Authors identify the key probabilistic dependencies among these factors
42Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
43. Bias affecting item categories
[Guo and Dunson 2015, Lin et al. 2019]
● Items of different genres have different rating
values and different samples
● Bayesian multiplicative probit model to uncover
category-wise bias in ratings
43Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
● User preferences propagate differently in CF
recommendations, according to movie genre
and user gender
● Majority user group contributes with more
neighbors and influence predictions more
● SVD++ and BiasedMF dampen the preference
bias for movie genres for both men and women
● WRMF is well-calibrated for Sci-Fi/Crime for
both men and women but the behavior is
inconsistent for Action/Romance
44. Bias on ratings based on proximity
[Hu et al. 2014]
44Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
● Context: business recommendation (Yelp venues, e.g., restaurant, a shopping mall)
● Algorithms: MF + geographic influence
● Findings: there is a weak positive correlation between a business’s ratings and its neighbors’ ratings.
Geographical distance between a user and a business adversely affects the prediction accuracy.
Geographic influence helps improving prediction accuracy w.r.t. classic MF approaches
45. Biases conveyed by user's reviews
[Piramuthu et al. 2012, Xu et al. 2018, Dai et al. 2018, Vall et al. 2019]
● Sequential bias: the sequence in which reviews are written play an
appreciable role in how the reviews that follow later in the
sequence are written.
○ A theoretical model was devised, but real impact is left as future work
● Opinion bias: given a user–item pair, the opinion bias is defined as
the bias between rating and review. The rating matrix is filled with
a linear combination of the rating and the review sentiment
● Textual bias inspects how recommenders systems are influenced
by the fact that words may express different meanings in review
context
● Song order in sequence-aware recommendations: RNN-based
recommenders can learn patterns from song order, but they do not
help improving recommendation effectiveness
45Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
46. Time-related bias on local popularity
[Anelli et al. 2019]
46Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
● Context: popularity is a local concept and a function of time. Popular and recently rated items are
deemed as relevant for the user. Movies and toys recommendation.
● Algorithms: User KNN with the concept of precursor neighbors and a time decay
● Findings: Time-aware neighbors and local popularity lead to a comparable effectiveness (in terms of
NDCG w/ rime-independent rating order condition) + an improved efficiency
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
47. Types of bias in platforms
[Olteanu et al. 2017]
● Functional biases
biases that are a result of platform-specific mechanisms or affordances, that is, the possible actions within
each system or environment
● Normative biases
biases that are a result of written norms or expectations about unwritten norms describing acceptable
patterns of behavior on a given platform
● External biases
biases resulting from factors outside the platform, including considerations of socioeconomic status,
education, social pressure, privacy concerns, interests, language, personality, and culture
● Non-individual accounts
interactions on social platforms that are not produced by individuals, but by accounts representing
various types of organizations, or by automated agents
47Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
48. Bias in implicit/explicit feedback loop
[Hofmann et al. 2014]
● Context: how a recommender system’s evaluation
based on implicit feedback relates to rating-based
evaluation, and how evaluation outcomes may be
affected by bias in user behavior
● Findings:
○ implicit and explicit evaluation agree well when
assumptions agree well (e.g., precision@10 and CTR
with no-bias)
○ match between assumption on user behavior and
explicit evaluation matters – if assumptions are
violated, the wrong recommender can be preferred
48Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
49. ● Context: characterize the impact of human-system feedback loop in the context of recommender
systems, demonstrating the unintended consequences of algorithmic confounding
● Findings:
○ the recommendation feedback loop
causes homogenization of user behavior
○ users experience losses in utility due
to homogenization effects
○ the feedback loop amplifies the impact
of recommender systems on the
distribution of item consumption
Homogeneity in recommendation loop
[Chaney et al. 2018]
49Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
50. Anchoring preferences to suggestions
[Adomavicius et al 2013]
● Context: explore how consumer preferences are impacted by predictions of recommender systems
● Findings:
○ the rating presented by a recommender serves as an anchor for the consumer’s preference
○ viewers’ preference ratings can be significantly influenced by the recommendation received
○ the effect is sensitive to the perceived reliability of a recommender system
○ the effect of anchoring is continuous and linear, operating over a range of system perturbations
50Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
51. Missing-not-at-random feedback
[Pradel et al. 2012]
● Context: study two major biases of the selection of
items, i.e., some items obtain more ratings than
others (popularity) and positive ratings are
observed more frequently than negative ratings
(positivity)
● Findings:
○ considering missing data as a form of negative
feedback during training may improve performances
○ ...but it can be misleading when testing, favoring
popularity more than user preferences
51Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
52. Misleading cues can bias user's views
[Elsweiler et al. 2017]
● Context: they explore the feasibility of substituting
meals that would typically be recommended to
users with similar, healthier dishes, investigating
how people perceive and select recipes
● Findings:
○ participants are unable to reliably identify which
recipe contains most fat due to their answers being
biased by lack of information
○ perception of fat content can be influenced by the
information available and, in some cases, misleading
cues (image or title) can bias a false impression
52Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
53. Sample of external bias
[Jahanbakhsh et al. 2020]
● Context: study how the ratings people receive on online labor
platforms are influenced by their performance, gender, their
rater’s gender, and displayed ratings from other raters
● Findings:
○ when the performance of paired workers was similar, low-performing
females were rated lower than their male counterparts
○ where there was a clear performance difference between paired workers,
low-performing females were preferred over a similarly-performing males
○ displaying an average rating from other raters made ratings more extreme,
resulting in high performing workers receiving significantly higher ratings
and low performers lower ratings compared to absence of ratings
53Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
54. Bias on preference-consistent info
[Schwind et al. 2012]
● Context: when a diversity of viewpoints on controversial issues is available, learners
prefer information that is consistent with their prior preferences; so, they investigated
the role of two potential moderators (prior knowledge; cooperation vs. competition) on:
○ confirmation bias: the tendency to select more preference-consistent information
○ evaluation bias: the tendency to evaluate preference-consistent information as better
● Findings:
○ preference-inconsistent recommendations can be used to overcome this bias
○ preference-inconsistent recommendations reduced confirmation bias irrespective of prior
knowledge; evaluation bias was only reduced for participants with no prior knowledge
○ preference-inconsistent recommendations led to reduced confirmation bias under cooperation
and under competition; evaluation bias was only reduced under cooperation.
54Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
55. Decision biases in user's choices
[Teppan and Zanker 2015]
● Context: experimental analysis of the impact of different decision biases like decoy or position
effects, as well as risk aversion in positive decision frames on users' choice behavior
● Findings: strong dominance of risk aversion strategies and the need for awareness of these effects
55Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
56. Sources of bias in data collection
[Olteanu et al. 2017]
● Data acquisition
○ discouraging data collection by third parties
○ programmatic limitation of access to data (e.g., time, amount, size)
○ not all relevant data captured by the platform
○ opaque and unclear sampling strategies
● Data querying
○ limited expressiveness of APIs regarding information needs
○ different ways of operationalization of information by APIs
○ influence of keywords on datasets in keyword-based queries
● Data filtering
○ removal of outliers that are relevant for the analysis
○ bounding of analysis due to text filtering operations
56Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
57. Sources of bias in data preparation
[Olteanu et al. 2017]
● Data cleaning
○ data representation choices and default values
○ normalization procedures (e.g., based on geographical information)
● Data enrichment
○ subjective and noisy labels due to manual annotations
○ errors due to automatic annotation based on statistical or machine learning
● Data aggregation
○ lose of information due to high-level aggregation
○ spurious patterns of association when data is groups based on certain attributes
57Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
58. Sources of bias in model exploitation
[Olteanu et al. 2017]
● Qualitative analysis
○ data representation choices and default values
○ normalization procedures (e.g., based on geographical information)
● Descriptive analysis
○ research often relying on counting entities
○ influence of bias and confounders on correlation analysis
● Inference analysis
○ variations of performance across and within datasets
○ definition of target variables, class labels, or data representations
○ effect of the objective function to the inference task
● Observational analysis
○ peer effects due to platform affordances and conventions
○ selection bias and how treatment effects on results generalizability
58Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
59. Sources of bias in model evaluation
[Olteanu et al. 2017, Bellogin et al. 2017]
● Evaluation data selection
○ imbalances of data samples due to their popularity
○ sensitivity to the ratio of the test ratings versus the added non-relevant items
● Metrics selection
○ influence of choice of metrics on research study takeaways
○ accounting domain impact throughout performance assessment
● Result assessment and interpretation
○ traces and patterns changing with context
○ going beyond studies evaluated on a single dataset or method
59Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
60. Facing popularity bias in evaluation
[Bellogin et al. 2017]
60Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
● a percentile-based approach consists in:
○ dividing items in m popularity percentiles
○ breaking down the computation of
accuracy by such percentiles
○ averaging the m obtained values
● a uniform test approach consists in:
○ formation of data splits where all items
have the same amount of test ratings
○ picking a set T of candidate items and a
number g of test ratings per item
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
61. Random decoys in evaluation
[Ekstrand et al. 2017] [Other readings: Lim et al. 2015, Yang et al. 2018, Carraro et al. 2020]
● Context: examine the random decoys protocol, where the
candidate set consists of the test set items plus a
randomly-selected set of N decoy items
● Findings:
○ the distribution of items goodness required to avoid
misclassified decoys with reasonable probability is
unreasonable
○ there is a serious discrepancy between theoretical
and observed behavior of the random decoy
strategy with respect to popularity bias
61Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
62. Error bias in evaluation
[Tian et al. 2020]
● Context: offline evaluation cannot accurately
assess novel, relevant recommendations,
because the most novel items are missing from
the data and cannot be judged as relevant
● Findings:
○ missing data in the observation process causes
the evaluation to mis-estimate metric values
○ substantial breakthroughs in recommendation
quality will be difficult to be assessed offline
62Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Bias through the pipeline
64. Biases that lead to discrimination
[Mehrabi et al. 2019]
64Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Direct
Discrimination
direct discrimination happens when
protected attributes of groups or
individuals explicitly result in
non-favorable outcomes toward them
Indirect
Discrimination
individuals appear to be treated based on neutral
and non-protected attributes; however, protected
groups or individuals get to be treated unjustly as a
result of implicit effects from protected attributes
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Discrimination
65. Granularity of discrimination
[Mehrabi et al. 2019]
65Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Individual
Discrimination
when a system gives unfairly different
predictions to individuals who are
considered similar for that task
Group
Discrimination
when a system systematically treats
individuals who belong to different
groups unfairly
Sub-group
Discrimination
when a system systematically
discriminate individuals over a
large collection of subgroups
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Discrimination
66. Contextualizing to recommendation
● Individual and sub-group discrimination are very challenging to assess: user similarity
at individual level should be on intrinsic properties that characterize the users and not
based on behavioral aspects
● No study so far in the RecSys domain
● Advances in ranking systems, where recommendation approaches can draw from:
[Zehlike et al. 2017, Celis et al. 2017, Biega et al. 2018, Lahoti et al. 2019, Yada et al. 2019, Singh and Joachims
2019, Kuhlman et al. 2019, Zehlike and Castillo 2020, Ramos and Boratto 2020a, Diaz et al. 2020]
● For much more, see the "Fairness & Discrimination in Retrieval & Recommendation"
tutorial at https://fair-ia.ekstrandom.net/sigir2019-slides.pdf
66Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Discrimination
67. Types of disparity in groups
[Mehrabi et al. 2019]
67Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Disparate
Treatment
when members of different
groups are treated differently
Disparate
Impact
when members of different groups
obtain different outcomes
Disparate
Mistreatment
when members of different groups
have different error rates
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Discrimination
68. Contextualizing disparities
● Recommender systems usually do not receive as input any sensitive attribute of the
user, so disparate treatment is usually not considered
● Disparate impact usually does not affect recommender systems: two users of
different genders with the same ratings for the same items, usually receive the
same recommendations
● Disparate mistreatment means that groups of users with different sensitive
attributes receive different recommendation quality. Known as consumer fairness
in the RecSys domain (presented later)
68Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Discrimination
69. Definitions of fairness
[Mehrabi et al. 2019]
69Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Equalized Odds
an algorithm is fair if the groups have
equal rates for true positives and false
positives
Fairness through
Awareness
an algorithm is fair if it gives similar
predictions to similar individuals
Equal Opportunity
an algorithm is fair if the groups have
equal true positive rates
Fairness through
Unawareness
an algorithm is fair as long as any
protected attributes is not explicitly
used in the decision-making process
Demographic Parity
an algorithm is fair if the likelihood of a
positive outcome should be the same
regardless of the group
Equality of Treatment
an algorithm is fair if the ratio of false
negatives and false positives is the
same for all the groups
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Discrimination
70. Fairness metrics for ranked outputs
[Yang and Stoyanovich 2017, Farnadi et al. 2018, Sonboli et al. 2019]
● Normalized discounted difference (rND) computes the difference in the proportion of
members of the protected group at top-i and in the over-all population
● Normalized discounted KL-divergence (rKL) measures the expectation of the logarithmic
difference between two discrete probability distributions
● Local fairness supports the fact that fairness may be local and the identification of protected
groups is only possible through consideration of local conditions
● Non-parity unfairness measures the absolute difference between the overall average ratings
of users belonging to the unprotected and the protected group
● Value unfairness measures the inconsistency in signed estimation error across the protected
and unprotected groups (becomes larger when predictions for one group are overestimated)
● Absolute unfairness measures the inconsistency in absolute estimation error across user
groups (becomes large if one group consistently receives more accurate recommendations)
70Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
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71. Multi-sided fairness
[Burke et al. 2017]
71Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Consumer-Provider Fairness
It might be needed that the platform
guarantees fairness for both
consumers and providers, e.g.,:
● people matching
● property/business
recommendation
● user skills and job matching
● and so on...
Consumer Fairness
We talk about unfairness for consumers
when their experience in the platform
differs:
● in terms of service effectiveness
(results’ relevance, user
satisfaction)
● resulting outcomes (exposure to
lower-paying job offers)
● participation costs (differential
privacy risks)
Provider Fairness
Providers experience unfairness when a
platform/service creates:
● different opportunities for their
items to be consumed
● different visibility or exposure in
the ranking
● different participation costs
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Discrimination
72. Impact of bias on consumer fairness
[Ekstrand et al. 2018a]
● Context: they investigate whether demographic
groups obtain different utility from recommender
systems in LastFM and Movielens 1M datasets
● Algorithms: Popular, Mean Item-Item, User-User,
FunkSVD
● Findings: ML1M & LFM1K have statistically-significant
differences between gender groups, and LFM360K has
significant differences between age brackets
72Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Discrimination
73. Impact of bias on consumer fairness
[Kowald et al. 2020]
● Context: investigate three user groups from Last.fm based on how much their listening preferences
deviate from the most popular music, among all Last.fm users: (i) low-mainstream users, (ii)
medium-mainstream users, and (iii) high-mainstream users
● Findings: low-mainstreaminess group significantly receives the worst recommendations
73Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Discrimination
74. Impact of bias on provider fairness
[Ekstrand et al. 2018b]
● Context: examine the response of collaborative filtering algorithms to the distribution of their input
data, with respect to content creator’s gender
● Findings: matrix factorization produced reliably male-biased recommendations, while nearest-neighbor
and hierarchical Poisson factorization techniques were closer to the user profile tendency while being
less diffuse than their inputs
74Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Discrimination
76. Bias-aware process pipeline
76
IDENTIFY PRODUCT GOALS
● What are you trying to achieve?
● For what population of people?
● What metrics are you tacking?
MITIGATE ISSUES
● Does data include enough minority samples?
● Do our proxies measure what we think they do?
● Does the bias notion capture customer needs?
IDENTIFY STAKEHOLDERS
● Who has a stake in this product?
● Who might be harmed?
● How?
DEVELOP AND ANALYZE THE SYSTEM
● How well the system matches product goals?
● To what degree bias is still present?
● How decisions impact on each stakeholder?
DEFINE A BIAS NOTION
● What type of bias? At what point?
● What distributions?
Bias-aware
process pipeline
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77. Techniques for bias treatment
77
Pre-processing
before model training
In-processing
during model training
Post-processing
after model training
Pre-processing techniques try to
transform the data so that the
bias is mitigated. If the algorithm
is allowed to modify the training
data, pre-processing can be used
In-processing techniques try to
modify learning algorithms to
mitigate bias during training process.
If it is allowed to change the learning
procedure, in-processing can be used
Post-processing is performed by
re-ranking items of the lists obtained
after model training. If the algorithm
can treat the learned model as a black
box, post-processing can be used
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
78. Sample of pre-processing treatment
[Rastegarpanah et al. 2019]
● Idea: rather than transforming the system’s input
data, they investigate whether simply augmenting
the input with additional data can improve the
social desirability of the recommendations
● Algorithms: MF family of algorithms
● Findings: the small amounts of antidote data
(typically on the order of 1% new users) can
generate a dramatic improvement (on the order of
50%) in the polarization or the fairness of the
system’s recommendations
78Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
79. Sample of in-processing treatment
[Beutel et al. 2019]
● Idea: they propose a metric for measuring fairness
based on pairwise comparisons and devise a
correlation-based regularization approach to improve
model performance for the given fairness metric
● Algorithms: learning-to-rank (e.g., point- and pair-wise)
● Findings: while the regularization decreases the
pairwise accuracy of the non-subgroup items, it closes
the gap in the inter-group pairwise fairness, resulting in
only a 2.6% advantage for non-subgroup items in
inter-group pairwise fairness, down from 35.6%
79Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
80. Sample of in-processing treatment
[Abdollahpouri et al. 2017]
● Idea: identify a regularization component of the
objective to be minimized when the distribution
of recommendations achieves a 50/50 balance
between medium-tail and short-head items. We
intend to generalize this objective in future work.
● Algorithms: RankALS (i.e., pair-wise learning)
● Findings: it is possible to model the trade-off
between long-tail catalog coverage and ranking
accuracy as a multi-objective optimization
problem based on a dissimilarity matrix
80Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
81. Sample of post-processing treatment
[Liu et al. 2019b]
● Idea: combining of a personalization-induced term
and a fairness-induced term, promoting the loans that
belong to currently uncovered borrower groups
● Algorithms: RankSGD, UserKNN, WRMF, Maxent
● Findings: they find a balance between the two terms,
where nDCG still remains at a high level after the
re-ranking, while fairness of the recommendation is
significantly improved, as loans belonging to
less-popular groups are promoted.
81Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
82. Other treatments against popularity
among others...
● Treatments that manipulate interactions before training a model:
○ sample tuples (u,i, j) where i is less popular than j for pair-wise learning [Jannach et al. 2014]
○ remove popular items, simulating situations in which these items are missing [Cremonesi et al. 2014]
○ detect and fix noisy ratings by characterizing items and users by their profiles [Toledo et al. 2015]
● Treatments that regularize the loss function score during training:
○ a regularization that balance recommendation accuracy and intra-list diversity [Abdollahpouri et al. 2017]
○ a regularization that minimizes the correlation between accuracy and item popularity [Boratto et al. 2020]
● Treatments that re-rank items after model training:
○ two-way aggregation of direct and reversed rank results (to improve coverage and accuracy) [Dong et al. 2020]
○ a re-ranking that suggests first items from unseen providers (to improve coverage) [Burke et al. 2016]
○ a re-ranking score that balances predicted rating with the inverse of popularity [Abdollahpouri et al. 2018]
○ a re-ranking that includes long-tail items the user might like [Abdollahpouri et al. 2019]
82Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
83. Other treatments against C-fairness
among others...
● Strategies that manipulate data of (groups of) individuals before training:
○ add new users who rate existing items to minimize polarization or improve fairness [Rastegarpanah et al. 2019]
● Strategies that introduce regularization or constraints during training:
○ create a balanced neighborhood from protected and unprotected classes [Burke et al. 2018]
○ objective functions pushing independence between predicted ratings and sensitive attributes [Kamishima et al. 2018]
○ a fairness-aware model that isolates and extracts sensitive information from latent factor matrices [Zhu et al. 2018]
○ a probabilistic programming approach for building fair hybrid recommender systems [Farnadi et al. 2018]
● Strategies that require a re-ranking of the items after training:
○ a heuristic re-ranking to mitigate unfairness in explainable recommendation over knowledge graphs [Fu et al. 2020]
83Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
84. Treatments for C-fairness of groups
among others...
● Stratigi et al. 2017:
○ improving the opportunities that patients have to inform themselves online about diseases and possible treatments
○ identify the correct set of similar users for a user in question, considering health information and ratings
○ a fairness-aware recommendation model of items that are relevant and satisfy the majority of the group members
● Lin et al. 2017:
○ maximize the satisfaction of each group member while minimizing the unfairness between them
○ introduce two concepts of social welfare and fairness, modeling overall utilities and balance between group members
○ an optimization framework for fairness-aware group recommendation from the perspective of Pareto Efficiency
● Serbos et al. 2017:
○ recommending packages of items to groups of users, e.g., recommending vacation packages to groups of tourists
○ fair in the sense that every group member is satisfied by a sufficient number of items in the package
○ propose greedy algorithms that find approximate solutions to meet a better trade-off within reasonable time
84Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
85. Other treatments against P-fairness
among others...
● Strategies involving some degree of pre-processing:
○ a mitigation approach that relies on tailored upsampling in pre-processing of interactions involving minority
groups of providers [Boratto et al. 2020b]
● Strategies that change or regularize the algorithm learning process:
○ the concept of balanced neighborhood from protected and unprotected class can be applied also to improve
fairness across providers [Burke et al. 2018]
○ a correlation-based regularization that minimizes the correlation between the residual between the clicked
and unclicked item and the group membership of the clicked item [Beutel et al. 2019]
● Strategies that re-rank items with a post-processing procedure:
○ a re-ranking to improve exposure distribution across creators, controlling divergence between the desired
distribution and the actual obtained distribution of exposure [Modani et al. 2017]
○ iteratively generate the ranking list by trading off between accuracy and the coverage of the providers based
on the adaptation of the xQuad algorithm [Liu et al. 2019b]
85Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
86. Treatments with a multi-sided focus
among others...
● In a speed-dating domain:
○ in a speed-dating context, a multi-dimensional utility framework which analyzes the relationship between
utilities and recommendation performance, achieving a trade-off [Zheng et al. 2018]
○ an approach to rerank the recommendation list by optimizing (1) the disparity of service; (2) the similarity of
mutual preference; (3) the equilibrium of demand and supply [Xia et al. 2019]
● Or in a generic multi-sided marketplace:
○ an integer linear programming-based optimization to deploy changes incrementally in steps, ensuring smooth
transition of item exposure and a minimum loss in utility [Patro et al. 2019]
○ an algorithm guarantees at least maximin share of exposure for most of the producers and envy-free up to one
good fairness for every customer [Patro et al. 2020]
86Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
87. Treatments against other biases (1)
● Biases related to how items are sampled, positioned, and/or selected, e.g.:
○ connect recommendation to causal inference from experimental and observational data [Schnabel et al. 2016]
○ integrate imputed errors and propensities, for alleviating the effect of propensity variance [Wang et al. 2019]
○ manage the spiral of silence effect, i.e., users are likely to rate if they perceive a support by the dominant opinion [Liu D.
et al. 2019a]
○ estimate item frequency from a data stream, subject to vocabulary and distribution shifts [Yi et al. 2019]
○ model position-bias offline and conduct online inference without position information [Guo et al. 2019]
○ off-policy correction to learn from feedback given by an ensemble of prior model policies [Chen et al. 2019a]
○ a clipped estimator to improve the bias-variance trade-off than w.r.t. unbiased estimator [Saito et al. 2020]
○ a counterfactual approach which accounts for selection and position bias jointly [Ovaisi et al. 2020]
○ a two-stage off-policy that takes the ranker model into account while training the candidate model [Ma et al. 2020]
87Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
88. Treatments against other biases (2)
● Biases associated to how items reach their audience:
○ a novel probabilistic method for weighted sampling of k neighbors that considers the similarity levels
between the target user (or item) and the candidate neighbors [Adamopoulos et al. 2014]
○ a target customer re-ranking algorithm to adjust the population distribution and composition in the
top-k target customers of an item while maintaining recommendation quality [Zhao et al. 2020]
● Biases associated to reviews and textual opinions, e.g.:
○ a sentiment classification scoring method, which employs dual attention vectors to predict the users’
sentiment scores of their reviews , to catch opinion bias and enhance user-item matrix [Xu et al. 2018]
○ a hybrid model that integrates modified-sied information related to textual bias and rating bias in
matrix factorization, getting a specific word representation for each item review [Dai et al. 2018]
● Biases associated to how items are marketed, e.g.:
○ a fairness-aware framework to address market imbalance bias by calibrating the parity of prediction
errors across different market segments [Wan et al. 2020]
88Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
89. Mitigations against other biases (3)
● Biases associated to social trust and influence:
○ a mitigation using polynomial regression and a Bayesian information criterion to predict ratings less influenced
by the tendency to conform to the perceived “norm” in a community [Krishnan et al. 2014]
○ clustering user-item space to discover rating bubbles derived from the theory of social bias, i.e., existing ratings
indirectly influences the users' opinion to follow the herd instinct [Divyaa et al. 2019]
○ a matrix completion algorithm that performs hybrid memory-based collaborative filtering, improving how the
bribery effect is managed and how the system is robust against bribery [Ramos et al. 2020b]
● Biases related to the interactions of users over time, e.g.:
○ an historical influence-aware latent factor model to capture and mitigate historical distortions in each single
rating under the assimilate-contrast theory: users conform to historical ratings if historical ratings are not far
from the product quality (assimilation), while users deviate from historical ratings if historical ratings are
significantly different from the product quality (contrast) [Zhang et al. 2018]
○ an unbiased loss using inverse propensity weighting, that includes the recency propensity of item x at time t, to
be used in point-wise learning to rank [Chen et al. 2019b]
89Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Mitigation design
90. BREAK
We resume at 12:00 UTC+00:00
Hands on Data and Algorithmic
Bias in Recommender Systems
ACM UMAP2020: 28th
Conference on User Modeling, Adaptation and Personalization
July 12, 2020 – ONLINE from GENOA
92. Steps of this hands on
92
1
Data Load
We load data from publicly
available datasets, specifically
focusing on Movielens 1M
(movies)
Data Pre-Processing
We process data to be fed
into the model and we prepare
training samples, focusing on
pairwise data
2
Model Definition and Train
We define the architecture of
the model, setup the training
parameters and run the model
training process
3
Relevance Computation
Given a pre-trained model, we
compute the user-item
relevance scores across all
the user-item pairs
4
Model Evaluation
We compute accuracy and
beyond-accuracy metrics,
such as coverage, novelty, and
diversity
5
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
http://bit.ly/BiasInRecSysTutorial-NB1-UMAP2020
Recommender systems in practice
93. Disclaimers
93Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
● In this tutorial, we do not aim to show how to fine-tune algorithms
● Due to the time constraints, we decided to reduce the optimization part
● The pre-trained models do not represent fine-tuned baselines
● The goal is to get familiar with an environment where it is easier to control the whole recsys process
Recommender systems in practice
95. Steps of this hands on
95
1
Model Exploration
We consider on data and
models introduced in the first
hands on to inspect of
popularity impacts on visibility
and exposure of items
Mitigation Setup
We arrange a representative
set of mitigation strategies
against popularity bias in pre-,
in- and post-processing
2
Mitigation Running
We run the mitigation
procedure, inspecting how the
optimization processes
influences popularity values
3
Model Re-Evaluation
We re-run the evaluation of
the first hands on to highlight
how disparities among
popular and unpopular items
are reduced
4
Impact Assessment
We interpret the results
obtained during evaluation in
order to envision how
stakeholders are impacted
5
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
http://bit.ly/BiasInRecSysTutorial-NB2-UMAP2020
N
Investigation on item popularity bias
97. Steps of this hands on
97
1
Model Exploration
We consider on data and
models introduced in the first
hands on to inspect of
popularity impacts on visibility
and exposure of providers
Mitigation Setup
We arrange a representative
set of mitigation strategies
against provider unfairness in
pre-, in- and post-processing
2
Mitigation Running
We run the mitigation
procedure, inspecting how the
optimization processes
influences provider fairness
3
Model Re-Evaluation
We re-run the evaluation of
the first hands on to highlight
how disparities among
providers are reduced
4
Impact Assessment
We interpret the results
obtained during evaluation in
order to envision how
stakeholders are impacted
5
Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
http://bit.ly/BiasInRecSysTutorial-NB3-UMAP2020
Investigation on item provider fairness
99. Contextual challenges
99Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Research challenges and emerging opportunities
● Different stakeholders have different (and possibly conflicting) needs. How can recommender systems
account for them?
● Multi-disciplinary approaches to go beyond recommendation algorithms (e.g., to link justice and
fairness)
● Synthesizing a definition of bias or fairness is challenging
● Creating a common vocabulary to recognize different types of bias and unfairness and advance as a
community
● Data to characterize bias phenomena with enough depth is lacking (especially for sensitive attributes)
● There are forms of bias on the Web that have not been studied in the recommendation literature
100. Operational challenges
100Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Research challenges and emerging opportunities
● Measuring and operationalizing a definition of bias or fairness. How can we optimize a recommender
system for it?
● Can we mitigate multiple forms of bias at the same time?
● Slight changes throughout the pipeline can make a huge difference on impact
● Research and development should be more focused on the real world application
● When mitigating bias we usually trade for other qualities. How can we mitigate bias without
compromising recommendation quality?
101. Bridging offline and online evaluation
● What if we do not have the sensitive attributes in the collected data?
● How should we select an approach with respect to another (e.g., equity vs equality)?
● How to identify harms in the considered context?
● Will the chosen offline metrics and experiments lead to the desired results online?
● How to inspect whether data generation and collection methods are appropriate?
● How could we take into account both bias goals and efficiency in the real world?
101Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
Research challenges and emerging opportunities
102. 102Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
To what extent are search and
recommendation algorithms
getting closer to each other?
103. Resources from this tutorial
● Tutorial website: http://bit.ly/BiasInRecSysTutorial-UMAP2020
● Github repository: http://bit.ly/BiasInRecSysTutorial-Github-UMAP2020
● Jupyter notebooks:
○ Neural recommender system train and test: http://bit.ly/BiasInRecSysTutorial-NB1-UMAP2020
○ Investigation on item popularity bias: http://bit.ly/BiasInRecSysTutorial-NB2-UMAP2020
○ Investigation on item provider fairness: http://bit.ly/BiasInRecSysTutorial-NB3-UMAP2020
103Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras
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