Tutorial on Operationalizing Treatments against Bias: Challenges and Solutions @ ECIR 2021

Operationalizing Treatments against
Bias: Challenges and Solutions
https://biasinrecsys.github.io/ecir2021-tutorial/
BCS-IRSG ECIR 2021
43rd European Conference on Information Retrieval
March 28, 2021 09:00 13:00 CEST – ONLINE from Lucca

About us
2
Ludovico Boratto
Senior Research Scientist
EURECAT - Centre Tecnológic de Catalunya
Barcelona, Spain
ludovicoboratto.com
ludovico.boratto@acm.org
Mirko Marras
Postdoctoral Researcher
École Polytechnique Fédérale de Lausanne (EPFL)
Lausanne, Switzerland
mirkomarras.com
mirko.marras@acm.org
Operationalizing Treatments against Bias: Challenges and Solutions
Boratto and Marras

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
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Part I.I
Recommendation
Principles

What products could I buy?
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Recommendation principles

What courses could I attend?
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The problem
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Recommender
System
A solution
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Capitalizing on recommender systems
A recommender system suggests items that might be relevant for a user
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The recommendation ranking task
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● 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
Boratto and Marras

Modes of optimization
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● 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
Boratto and Marras

Core recommendation techniques
Adapted from [Ricci et al. 2015]
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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
Boratto and Marras

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
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Consumers Providers System
C P S
Boratto and Marras

A sample multi-sided scenario
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Consumers
Students
Providers
Teachers
System
Online Course Platform
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Multi-sided recommendation aspects
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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
Boratto and Marras

Part I.II
Hands on
Recommender Systems

Disclaimers
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● 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
Hands on Recommender Systems

Steps of this hands on
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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 based on point- and
pair-wise methods
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
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https://colab.research.google.com/github/biasinrecsys/ecir2021-tutorial/blob/master/notebooks/model_setup.ipynb
Hands on Recommender Systems

Part I.III
Algorithmic Bias
Foundations

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?
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Data and algorithmic bias fundamentals > Motivating examples

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
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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 subreddits popular among females
(males) show imbalance reinforcement over genders
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Motivating example in recruiting
[Singh et al. 2018]
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● 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
Boratto and Marras

Disclaimers
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● 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

Related scientiﬁc venues and initiatives
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Recent scientiﬁc tutorials
including:
Recent 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 2019-21
● FAIR-TREC @ TREC 2020-21
● FairUMAP @ UMAP 2018-20
● DAB @ CIKM 2017-19
● Bias @ ECIR 2020-21
● 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
● Hands-on on Data and Algorithmic Bias in Recommender Systems @ UMAP 2020
● Bias in Personalized Rankings: Concepts to Code @ ICDM 2020
Recent special issues in journals
including:
● Special Issue on Fair, Accountable, and Transparent Recommender Systems @ UMUAI
● Special Issue on Algorithmic Bias and Fairness in Search and Recommendation @ IPM

Perspectives impacted by bias
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Economics
Law
Rights
Security
Social
Dynamics
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
labour markets evolve and
can be amplified as the
technology progresses
Data and algorithmic bias fundamentals > Perspectives

How biases can inﬂuence economy
[Liu et al. 2019]
● Loan recommender systems are designed to assist
lenders in looking for promising borrowers
● Such systems model lenders’ historical behaviors and
generate personalized recommendations
● Certain geographical regions such as Asia and Africa
dominate recommendation
● Others like Oceania and Eastern Europe barely
receive recommendations
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Biases that impact on law
[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.:
● Mentions can be found in Art. 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
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Biases that impact on rights
[Yao et al. 2017]
As an example, United Nations Sustainable Development Goal 4 aims also to ensure
equitable quality education for all, but Yao et al. observed that:
● In 2010, women accounted for only 18% of the BSc degrees in Computer Science
● Historical rating data of Computer Science courses to be dominated by men
● The model may underestimate women’s preferences and be biased towards men
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● Sellers might attack the system by introducing a bias in the ratings
● The attack goal is to bribe users to increase ratings and push recommendations
● A novel Hybrid KNN CF is introduced to deal with this phenomenon
● By means of this novel algorithm:
○ The proﬁtability associated to increasing the ratings is strongly reduced w.r.t. SVD
○ Downgrading the ratings of competitors is not proﬁtable with this approach
○ System is more robust to attacks and more trustable by the users
Biases can affect security aspects
[Ramos et al. 2020]
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Biases associated to social dynamics
[Fabbri et al. 2020]
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● People recommendation in social networks, with users
divided into groups based on gender
● A range of state-of-the-art algorithms, such as
Adamic-Adar, SALSA, and ALS are inspected
● People recommenders produce disparate visibility on the
two subgroups of gender
● Homophily plays a key role in promoting or reducing
visibility for different subgroups
Lorenz Curves (inequality). Recommendations
introduce more inequality than degree distribution,
and this inequality is stronger in the minority class.

Ethical aspects inﬂuenced by bias
[Bozdag 2013, Milano et al. 2020]
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Moral
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 Exposure
lack of exposure to contrasting
viewpoints, feedback effects
Data and algorithmic bias fundamentals > Ethical aspects

On biases against immoral content
[Pantakar et al. 2019]
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● News recommender system generates awareness on biased
news, to possibly avoid fake and politically polarized news
● They propose a news clustering and bias score attached to
each news. Recommendation of similar, unbiased content
● With a live-user evaluation, the rankings generated by the
algorithm match with the ones the users would generate

Bias affecting users' privacy
[Resheff et al. 2018]
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● User representations may be used to recover private user information
such as gender and age, undermining users' privacy
● A privacy-adversarial framework is proposed to eliminate leakage of
private information: an adversarial component is appended to the model for
each of the demographic variables we want to obfuscate, so that the user
representations are optimized to preclude predicting the variables
● 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)

Inﬂuence of bias on autonomy
[Arnold et al. 2018]
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● Text recommender systems that support creative tasks
(writing reviews). Do they exhibit unintentional biases in the
support that they offer?
● Contextual recommendations are proposed: (1) selection of
the three most likely next words, (2) generation of the most
likely phrase continuation for each word
● People who get recommended phrasal text entry shortcuts
that are skewed positive, write more positive reviews than
when presented with negative-skewed shortcuts

How biases can introduce opacity
[Eslami et al. 2020]
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● Opaque algorithms sometimes make biased or deceptive
decisions, many have called for increased transparency
● They conducted an analysis of 242 users’ online
discussions about the Yelp review filtering algorithm
● Users defend this algorithm and its opacity depending on
their engagement with and gain from the algorithm
● Then, adding transparency into the algorithm changed
users’ attitudes towards the algorithm
(a) A filtered review is presented as “recommended”
to the user who wrote it; (b) This review, however,
presented for other users as a filtered review.

Biases that emphasize unfairness
[Shakespeare et al. 2020]
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● An exploratory study, analyzing the extent to
which commonly deployed state-of-the-art
collaborative ﬁltering algorithms (CF) may act
to further increase/decrease artist gender bias
● They show that gender bias can propagate in
CF-based recommendations, according to the
bias present in the data

Biased effects on social exposure
[Papakyriakopoulos et al. 2020]
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● A segment of the political discussions on social networks
is shaped by users that are over-proportionally active
● By applying geometric topic modeling on German political
comments, they demonstrate that hyperactive users have
a signiﬁcant role in the political discourse
● By training recommender systems, they illustrate that
models provide very different suggestions to users, when
accounting for or ignoring hyperactive behavior

System objectives inﬂuenced by bias
[Kaminskas et al. 2017, Namatzadeh et al 2018, Singh et al. 2018]
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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 fundamentals > System objectives

Impact on utility (and trade-offs)
[Fu et al. 2020]
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● Studying 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
● They proposed an explainable CF + re-ranking to
balance predictions and group/individual fairness
● The disparity in utility is reduced while preserving
recommendation quality

Impact on coverage (and trade-offs)
[Dean et al. 2020]
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● The amount of recourse available to a user is the
percentage of unseen items that are reachable
● The availability of items in a recommender system is
the percentage of items reachable by a user
● Study on linear preference models (SLIM and MF)
● Unavailable items are less popular than available items
● Users with smaller history have more available recourse

Impact on diversity (and trade-offs)
[Lee and Hosanagar 2019]
● They investigated the impact of collaborative
recommender algorithms commonly used in
e-commerce on sales diversity
● The use of traditional collaborative ﬁlters is
associated with a decrease in sales diversity relative
to a world without product recommendations
● The decrease in aggregate sales diversity may not
always be accompanied by a corresponding decrease
in individual-level consumption diversity
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Impact on novelty (and trade-offs)
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● They show that the probability of being recommended and
the item true positive rate are biased against the item
popularity
● They propose an in-processing approach aimed at
minimizing the biased correlation between user-item
relevance and item popularity
● With small losses in accuracy, their popularity-mitigation
approach leads to important gains in beyond-accuracy
recommendation quality, especially novelty

Impact on providers' exposure
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● Certain minority groups of providers
are being disproportionately affected
by unintentional discrimination
● They show how adding observation
upsampling can dramatically change
the exposure given to providers
● The ranked lists then provide fairer
exposure, wider minority-group item
coverage, and limited loss in utility

Part I.IV
Bias through the
experimental pipeline

Data Acquisition
and Storage
Recommendation pipeline
47
Platform
Data
Model
Recommendations
Data
Preparation
Model
Prediction
Recommendation
Delivering
Model
Evaluation
Boratto and Marras
Pre-
Processed
Data
Model Setup
and Training
Data and algorithmic bias fundamentals > Bias through the pipeline

Recommendation pipeline: users
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Types of bias associated to users
[Olteanu et al. 2017]
● Population biases
differences in demographics 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 expressed as differences in the attributes of networks obtained from user connections, interactions or activity
● Temporal biases
differences in populations or behaviors over time
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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, RfRec, Funk-SVD, Koren-MF, ALS, BPR
● Findings: techniques performing well on accuracy focus their recommendations on a tiny fraction of the
item spectrum or recommend mostly top sellers
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On behavioral bias towards popularity
[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
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Behavioral patterns on 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
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● User preferences propagate differently in recommendations,
according to movie genre and user gender
● 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

Biases conveyed by users' content
[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 next reviews are written
● 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 are influenced by the
fact that words may express different meanings in review context
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Bias related to behavior over time
[Anelli et al. 2019]
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● Context: Popularity is a local concept and a function of time. Popular and recently rated items are
deemed as relevant for the user. Movie and toy 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/ time-independent rating order condition) + an improved efﬁciency

Recommendation pipeline: platform
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Platform
Boratto and Marras

Types of bias in platforms
● Functional biases
biases resulting from 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
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Functional bias in feedback loop
[Hofmann et al. 2014]
● Context: how a recommender systems 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)
○ match between assumption on user behavior and
explicit evaluation matters – if assumptions are
violated, the wrong recommender can be preferred
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● 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
Impact on homogeneity in the loop
[Chaney et al. 2018]
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Functional aspects influence choices
[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
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Missing data biases internal functions
[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
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Deceptive expectations bias user 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
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Example 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
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Bias introduced by external viewpoints
[Schwind et al. 2012]
● Context: when a diversity of viewpoints is available, learners prefer information that is consistent with
their prior preferences; so, they investigated the role of two potential moderators 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.
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On 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
● Findings: risk aversion can be observed in all settings, while position and decoy effects only play a role
when risk aversion is not too predominant
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Data Acquisition
and Storage
Recommendation pipeline: data
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Data
Data
Preparation
Boratto and Marras
Pre-
Processed
Data

Sources of bias in data collection
● 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 or 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
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Sources of bias in data preparation
● 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
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Recommendation pipeline: model
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Model
Recommendations
Model Prediction
Recommendation
Delivering
Model
Evaluation
Boratto and Marras
Model Setup
and Training

Sources of bias in model exploitation
● Qualitative analysis
○ data representation choices and default values
○ normalization procedures (e.g., based on geographical information)
● Descriptive analysis
○ research often relying on counting entities or inﬂuence of bias and confounders on correlation analysis
● Inference analysis
○ 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
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Sources of bias in model evaluation
[Olteanu et al. 2017, Bellogín 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
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Bias on popularity in evaluation
[Bellogín et al. 2017]
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● First percentile-based approach:
○ dividing items in m popularity percentiles
○ breaking down the computation of
accuracy by such percentiles
○ averaging the m obtained values
● Second uniform test approach:
○ 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

Bias and 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
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Bias on error estimation in evaluation
[Tian et al. 2020]
● Context: offline evaluation cannot accurately
assess novel, relevant recommendations; 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
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Bias-aware process pipeline
74
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 stakeholders’ 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
Boratto and Marras

Part II.I
Bias Mitigation Design

Techniques for bias treatment
78
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
Boratto and Marras
Bias mitigation design

Example of pre-processing treatment
[Jannach et al. 2015]
● Idea: extending BPR with a modiﬁed distribution
function φ that samples tuples (u,i, j) where i is
less popular and j is more popular
● Algorithms: BPR
● Findings: it is possible to mitigate popularity bias,
with minor losses in effectiveness
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Example of in-processing treatment
● 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.
● 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
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Example of post-processing treatment
● Idea: they modiﬁed the xQuAD to produce a new re-ranked list
S (|S | < |R|) that manages popularity bias while still being
accurate
● Algorithms: RankALS (i.e., pair-wise learning)
● Findings: on two datasets, the re-ranking methods boost
long-tail items while keeping the accuracy loss small, compared
to the model-based technique
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Other treatments for popularity bias
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. 2020a]
○ adversarial framework: minimax game between the BPR model and a discriminator [Zhu 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]
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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]
○ integrate a framework for counterfactual recommendation that enables uniform data modeling [Liu et al. 2020]
○ a counterfactual attribute-based propensity framework that can be estimated ofﬂine [Qin et al. 2020]
○ 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 model into account while training the candidate model [Ma et al. 2020]
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● 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 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]
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● 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]
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● 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]
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● 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 assimilation-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]
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Part II.II
Hands on
Item Popularity Bias

89
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
Boratto and Marras
Investigation on item popularity bias
https://colab.research.google.com/github/biasinrecsys/ecir2021-tutorial/blob/master/notebooks/item_popularity_bias.ipynb

Part II.III
Unfairness Mitigation
Design

Biases that lead to discrimination
[Mehrabi et al. 2019]
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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
Unfairness mitigation design

Granularity of discrimination
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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
discriminates individuals over a
large collection of subgroups

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 recommender system 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
● Group discrimination is much more common (e.g., how do the generated recommendation reﬂect the
needs of consumers/providers of different genders?)
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Types of disparity in groups
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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

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)
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Deﬁnitions of fairness
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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 attribute 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

Fairness metrics for ranked outputs (I)
[Yang and Stoyanovich 2017, Farnadi et al. 2018, Sonboli et al. 2019, Deldjoo et al. 2020]
● 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
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Fairness metrics for ranked outputs (II)
[Yang and Stoyanovich 2017, Farnadi et al. 2018, Sonboli et al. 2019, Deldjoo et al. 2020]
● Non-parity unfairness measures the absolute difference between the overall average ratings of users
belonging to the unprotected and protected groups
● 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)
● Csiszar generalized measure of divergence compares the probability distribution of the model
performance and a fair probability distribution
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Multi-sided fairness
[Burke et al. 2017]
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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 platform differs:
● in terms of service effectiveness
(results’ relevance, user satisfaction)
● resulting outcomes (exposure to
lower-paying job offers)
● participation costs (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

Impact of bias on consumer fairness (I)
[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
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● Context: investigate three user groups from Last.fm based on how much their listening preferences
deviate from the most popular music: (i) low-, (ii) medium-, and (iii) high-mainstream users
● Findings: low-mainstreaminess group signiﬁcantly receives the worst recommendations
Impact of bias on consumer fairness (II)
[Kowald et al. 2020]
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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 creators’ 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
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Example of pre-processing treatment
[Rastegarpanah et al. 2019]
● Idea: augmenting the input with additional data can
improve the social desirability of 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
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Example of in-processing treatment
[Beutel et al. 2019]
● Idea: propose a metric for measuring fairness based on
pairwise comparisons; a correlation-based approach to
improve 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%
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Example of post-processing treatment
[Liu et al. 2019]
● 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
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Other treatments against C-fairness
among others...
● Strategies that introduce regularization or constraints during training:
○ create a balanced neighborhood from protected and unprotected classes [Burke et al. 2018]
○ objective 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]
○ a re-ranking to provide fair recommendations to groups [Kaya et al. 2020]
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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
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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. 2019]
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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]
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Part II.IV
Hands on
Item Provider Fairness

111
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
Boratto and Marras
Investigation on item provider fairness
https://colab.research.google.com/github/biasinrecsys/ecir2021-tutorial/blob/master/notebooks/item_provider_fairness.ipynb

Contextual challenges
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● Stakeholders have different (and conﬂicting) needs. How can recommender systems account for them?
● Multi-disciplinary approaches to go beyond 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
● 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
Final Remarks and Open Discussion

Operational challenges
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● Measuring and operationalizing 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?

Evaluation challenges
● 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?
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Resources from this tutorial
1. Tutorial website https://biasinrecsys.github.io/ecir2021-tutorial/
2. Github repository https://github.com/biasinrecsys/ecir2021-tutorial
3. Jupyter notebooks
3.1. https://colab.research.google.com/github/biasinrecsys/ecir2021-tutorial/blob/master/notebooks/model_setup.ipynb
3.2. https://colab.research.google.com/github/biasinrecsys/ecir2021-tutorial/blob/master/notebooks/item_popularity_bias.ipynb
3.3. https://colab.research.google.com/github/biasinrecsys/ecir2021-tutorial/blob/master/notebooks/item_provider_fairness.ipynb
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References #1
1. Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Augusto Pizzato: Multi
Stakeholder recommendation: Survey and research directions. User Model. User Adapt. Interact. 30(1): 127-158 (2020).
2. Himan Abdollahpouri, Robin Burke, Bamshad Mobasher: Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking. FLAIRS Conference
2019: 413-418 (2019).
3. Himan Abdollahpouri, Robin Burke, Bamshad Mobasher. Popularity-Aware Item Weighting for Long-Tail Recommendation. arXiv preprint arXiv:1802.05382 (2018).
4. Himan Abdollahpouri, Robin Burke, Bamshad Mobasher: Controlling Popularity Bias in Learning-to-Rank Recommendation. RecSys 2017: 42-46 (2017).
5. Panagiotis Adamopoulos, Alexander Tuzhilin: On over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in
collaborative ﬁltering systems. RecSys 2014: 153-160 (2014).
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References #2
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References #3
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