Tutorial on Bias in Rec Sys @ UMAP2020

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

5. SESSION I Foundations

6. Recommendation principles

7. What products could I buy? 7Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras Recommendation principles

8. What courses could I attend? 8Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras Recommendation principles

9. The problem 9Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras Recommendation principles

10. Recommender System A solution 10Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras Recommendation principles

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

18. Data and algorithmic bias fundamentals

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

38. Bias through the pipeline

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

63. Discrimination

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 Data and algorithmic bias fundamentalsData and algorithmic bias fundamentalsData and algorithmic bias fundamentals > Discrimination

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

75. Mitigation design

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 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

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

91. Recommender Systems in Practice

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

94. Case Study I Item Popularity Bias

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

96. Case Study II Item Provider Fairness

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

98. Research challenges and emerging opportunities

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

104. Questions?

105. References #1 1. Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Augusto Pizzato: Multistakeholder 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). 6. Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley, Jingjing Zhang: Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects. Inf. Syst. Res. 24(4): 956-975 (2013). 7. Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, Joseph Trotta: Local Popularity and Time in top-N Recommendation. ECIR (1) 2019: 861-868 (2019). 8. Kenneth C. Arnold, Krysta Chauncey, Krzysztof Z. Gajos: Sentiment Bias in Predictive Text Recommendations Results in Biased Writing. Graphics Interface 2018: 42-49 (2018). 105Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

106. References #2 9. Alejandro Bellogín, Pablo Castells, Iván Cantador: Statistical biases in Information Retrieval metrics for recommender systems. Inf. Retr. J. 20(6): 606-634 (2017). 10. Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow: Fairness in Recommendation Ranking through Pairwise Comparisons. KDD 2019: 2212-2220 11. Asia J. Biega, Krishna P. Gummadi, Gerhard Weikum: Equity of Attention: Amortizing Individual Fairness in Rankings. SIGIR 2018: 405-414 12. Ludovico Boratto, Gianni Fenu, Mirko Marras: The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online Courses. ECIR (1) 2019: 457-472 (2019). 13. Ludovico Boratto, Gianni Fenu, Mirko Marras: Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation. CoRR abs/2006.04275 (2020a). 14. Ludovico Boratto, Gianni Fenu, Mirko Marras: Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems. CoRR abs/2006.04279 (2020b). 15. Engin Bozdag: Bias in algorithmic ﬁltering and personalization. Ethics Inf Technol 15, 209–227 (2013). 16. Robin Burke. Multisided fairness for recommendation. arXiv preprint arXiv:1707.00093 (2017). 17. Robin Burke, Nasim Sonboli, Aldo Ordonez-Gauger: Balanced Neighborhoods for Multi-sided Fairness in Recommendation. FAT 2018: 202-214 (2018) 18. Robin D. Burke, Himan Abdollahpouri, Bamshad Mobasher, Trinadh Gupta: Towards Multi-Stakeholder Utility Evaluation of Recommender Systems. UMAP Extended Proceedings (2016). 106Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

107. References #3 19. Rocío Cañamares, Pablo Castells: Should I Follow the Crowd?: A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems. SIGIR 2018: 415-424 20. Diego Carraro, Derek Bridge: Debiased ofﬂine evaluation of recommender systems: a weighted-sampling approach. SAC 2020: 1435-1442 (2020). 21. L. Elisa Celis, Damian Straszak, Nisheeth K. Vishnoi: Ranking with Fairness Constraints. ICALP 2018: 28:1-28:15 (2017). 22. Roberto Centeno, Ramón Hermoso, Maria Fasli: On the inaccuracy of numerical ratings: dealing with biased opinions in social networks. Inf. Syst. Frontiers 17(4): 809-825 (2015). 23. Allison J. B. Chaney, Brandon M. Stewart, Barbara E. Engelhardt: How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. RecSys 2018: 224-232 (2018). 24. Sushma Channamsetty, Michael D. Ekstrand: Recommender Response to Diversity and Popularity Bias in User Proﬁles. FLAIRS Conference 2017: 657-660 (2017). 25. Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed H. Chi: Top-K Off-Policy Correction for a REINFORCE Recommender System. WSDM 2019: 456-464 (2019a). 26. Ruey-Cheng Chen, Qingyao Ai, Gaya Jayasinghe, W. Bruce Croft: Correcting for Recency Bias in Job Recommendation. CIKM 2019: 2185-2188 (2019b). 27. Paolo Cremonesi, Franca Garzotto, Roberto Pagano, Massimo Quadrana: Recommending without short head. WWW (Companion Volume) 2014: 245-246 (2014). 107Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

108. References #4 28. Jiao Dai, Mingming Li, Songlin Hu, Jizhong Han: A Hybrid Model Based on the Rating Bias and Textual Bias for Recommender Systems. ICONIP (2) 2018: 203-214 (2018). 29. Sarah Dean, Sarah Rich, Benjamin Recht: Recommendations and user agency: the reachability of collaboratively-ﬁltered information. FAT* 2020: 436-445 (2020). 30. Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette: Evaluating Stochastic Rankings with Expected Exposure. CoRR abs/2004.13157 (2020). 31. Divyaa L. R., Nargis Pervin: Towards generating scalable personalized recommendations: Integrating social trust, social bias, and geo-spatial clustering. Decis. Support Syst. 122 (2019). 32. Qiang Dong, Quan Yuan, Yang-Bo Shi: Alleviating the recommendation bias via rank aggregation. Physica A: Statistical Mechanics and its Applications, 534, 122073. (2019). 33. Bora Edizel, Francesco Bonchi, Sara Hajian, André Panisson, Tamir Tassa: FaiRecSys: mitigating algorithmic bias in recommender systems. Int. J. Data Sci. Anal. 9(2): 197-213 (2020) 34. David Elsweiler, Christoph Trattner, Morgan Harvey: Exploiting Food Choice Biases for Healthier Recipe Recommendation. SIGIR 2017: 575-584 (2017). 35. Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, Maria Soledad Pera: All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. FAT 2018: 172-186 (2018a). 108Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

109. References #5 36. Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, Daniel Kluver: Exploring author gender in book rating and recommendation. RecSys 2018: 242-250 (2018b) 37. Michael D. Ekstrand, Vaibhav Mahant: Sturgeon and the Cool Kids: Problems with Random Decoys for Top-N Recommender Evaluation. FLAIRS Conference 2017: 639-644 (2017). 38. Francesco Fabbri, Francesco Bonchi, Ludovico Boratto, Carlos Castillo: The Effect of Homophily on Disparate Visibility of Minorities in People Recommender Systems. ICWSM 2020: 165-175 (2020). 39. Golnoosh Farnadi, Pigi Kouki, Spencer K. Thompson, Sriram Srinivasan, Lise Getoor: A Fairness-aware Hybrid Recommender System. CoRR abs/1809.09030 (2018). 40. Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, Gerard de Melo: Fairness-Aware Explainable Recommendation over Knowledge Graphs. CoRR abs/2006.02046 (2020). 41. Huifeng Guo, Jinkai Yu, Qing Liu, Ruiming Tang, Yuzhou Zhang: PAL: a position-bias aware learning framework for CTR prediction in live recommender systems. RecSys 2019: 452-456 (2019). 42. Fangjian Guo, David B. Dunson: Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach. RecSys 2015: 317-320 (2015). 43. Katja Hofmann, Anne Schuth, Alejandro Bellogín, Maarten de Rijke: Effects of Position Bias on Click-Based Recommender Evaluation. ECIR 2014: 624-630 (2014). 44. Longke Hu, Aixin Sun, Yong Liu: Your neighbors affect your ratings: on geographical neighborhood inﬂuence to rating prediction. SIGIR 2014: 345-354 (2014). 109Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

110. References #6 45. Farnaz Jahanbakhsh, Justin Cranshaw, Scott Counts, Walter S. Lasecki, Kori Inkpen: An Experimental Study of Bias in Platform Worker Ratings: The Role of Performance Quality and Gender. CHI 2020: 1-13 (2020). 46. Dietmar Jannach, Lukas Lerche, Iman Kamehkhosh, Michael Jugovac: What recommenders recommend: an analysis of recommendation biases and possible countermeasures. User Model. User Adapt. Interact. 25(5): 427-491 (2015). 47. Marius Kaminskas, Derek Bridge: Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems. ACM Trans. Interact. Intell. Syst. 7(1): 2:1-2:42 (2017). 48. Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma: Recommendation Independence. FAT 2018: 187-201 (2018). 49. Dominik Kowald, Markus Schedl, Elisabeth Lex: The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study. ECIR (2) 2020: 35-42 (2020). 50. Sanjay Krishnan, Jay Patel, Michael J. Franklin, Ken Goldberg: A methodology for learning, analyzing, and mitigating social inﬂuence bias in recommender systems. RecSys 2014: 137-144 (2014). 51. Caitlin Kuhlman, MaryAnn Van Valkenburg, Elke A. Rundensteiner: FARE: Diagnostics for Fair Ranking using Pairwise Error Metrics. WWW 2019: 2936-2942 (2019). 52. Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum: iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making. ICDE 2019: 1334-1345 110Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

111. References #7 53. Dokyun Lee, Kartik Hosanagar: How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment. Inf. Syst. Res. 30(1): 239-259 (2019). 54. Jurek Leonhardt, Avishek Anand, Megha Khosla: User Fairness in Recommender Systems. WWW (Companion Volume) 2018: 101-102 (2018). 55. Mingming Li, Jiao Dai, Fuqing Zhu, Liangjun Zang, Songlin Hu, Jizhong Han: A Fuzzy Set Based Approach for Rating Bias. AAAI 2019: 9969-9970 (2019). 56. Daryl Lim, Julian J. McAuley, Gert R. G. Lanckriet: Top-N Recommendation with Missing Implicit Feedback. RecSys 2015: 309-312 (2015). 57. Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, Shaoping Ma: Fairness-Aware Group Recommendation with Pareto-Efﬁciency. RecSys 2017: 107-115 (2017). 58. Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke: Crank up the Volume: Preference Bias Ampliﬁcation in Collaborative Recommendation. RMSE@RecSys 2019 (2019). 59. Dugang Liu, Chen Lin, Zhilin Zhang, Yanghua Xiao, Hanghang Tong: Spiral of Silence in Recommender Systems. WSDM 2019: 222-230 (2019a). 60. Weiwen Liu, Jun Guo, Nasim Sonboli, Robin Burke, Shengyu Zhang: Personalized fairness-aware re-ranking for microlending. RecSys 2019: 467-471 (2019b). 61. Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, Ed H. Chi: Off-policy Learning in Two-stage Recommender Systems. WWW 2020: 463-473 (2020). 62. Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney: Recommender Systems, Missing Data and Statistical Model Estimation. IJCAI 2011: 2686-2691 (2011). 111Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

112. References #8 63. Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 (2019). 64. Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, Fernando Diaz: Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems. CIKM 2018: 2243-2251 (2018). 65. Silvia Milano, Mariarosaria Taddeo, Luciano Floridi. Recommender systems and their ethical challenges. AI & Soc (2020). 66. Natwar Modani, Deepali Jain, Ujjawal Soni, Gaurav Kumar Gupta, Palak Agarwal: Fairness Aware Recommendations on Behance. PAKDD (2) 2017: 144-155 (2017). 67. Azadeh Nematzadeh, Giovanni Luca Ciampaglia, Filippo Menczer, Alessandro Flammini: How algorithmic popularity bias hinders or promotes quality. CoRR abs/1707.00574 (2017). 68. Alexandra Olteanu, Carlos Castillo, Fernando Diaz, Emre Kiciman: Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. Frontiers Big Data 2: 13 (2019) 69. Zohreh Ovaisi, Ragib Ahsan, Yifan Zhang, Kathryn Vasilaky, Elena Zheleva: Correcting for Selection Bias in Learning-to-rank Systems. WWW 2020: 1863-1873 (2020). 70. Anish Anil Patankar, Joy Bose, Harshit Khanna: A Bias Aware News Recommendation System. ICSC 2019: 232-238 (2019). 71. Bruno Pradel, Nicolas Usunier, Patrick Gallinari: Ranking with non-random missing ratings: inﬂuence of popularity and positivity on evaluation metrics. RecSys 2012: 147-154 (2012). 112Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

113. References #9 72. Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty: FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms. WWW 2020: 1194-1204 (2020). 73. Gourab K. Patro, Abhijnan Chakraborty, Niloy Ganguly, Krishna Gummadi. Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 181-188). 74. Selwyn Piramuthu, Gaurav Kapoor, Wei Zhou, Sjouke Mauw: Input online review data and related bias in recommender systems. Decis. Support Syst. 53(3): 418-424 (2012). 75. Guilherme Ramos and Ludovico Boratto, Reputation (In)dependence in Ranking Systems: Demographics Influence Over Output Disparities, in Proceedings of the 43rd International ACM SIGIR Conference on Researchand Development in Information Retrieval, SIGIR 2020 (2020a) 76. Guilherme Ramos, Ludovico Boratto, Carlos Caleiro. On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms. Information Processing & Management, 57(2), 102058 (2020). 77. Bashir Rastegarpanah, Krishna P. Gummadi, Mark Crovella: Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems. WSDM 2019: 231-239 78. Yehezkel S. Resheff, Yanai Elazar, Moni Shahar, Oren Sar Shalom: Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations. ICPRAM 2019: 476-482 79. Francesco Ricci, Lior Rokach, Bracha Shapira: Recommender Systems: Introduction and Challenges. Recommender Systems Handbook 2015: 1-34 (2015). 113Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

114. References #10 80. Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, Kazuhide Nakata: Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. WSDM 2020: 501-509 (2020). 81. Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims: Recommendations as Treatments: Debiasing Learning and Evaluation. ICML 2016: 1670-1679 (2016). 82. Christina Schwind, Jürgen Buder: Reducing conﬁrmation bias and evaluation bias: When are preference-inconsistent recommendations effective - and when not? Comput. Hum. Behav. 28(6): 2280-2290 (2012). 83. Dimitris Serbos, Shuyao Qi, Nikos Mamoulis, Evaggelia Pitoura, Panayiotis Tsaparas: Fairness in Package-to-Group Recommendations. WWW 2017: 371-379 (2017). 84. Ashudeep Singh, Thorsten Joachims: Policy Learning for Fairness in Ranking. NeurIPS 2019: 5427-5437 (2019). 85. Ashudeep Singh, Thorsten Joachims: Fairness of Exposure in Rankings. KDD 2018: 2219-2228 (2018). 86. Nasim Sonboli, Robin Burke: Localized Fairness in Recommender Systems. UMAP (Adjunct Publication) 2019: 295-300 (2019). 87. Harald Steck: Item popularity and recommendation accuracy. RecSys 2011: 125-132 (2011). 88. Maria Stratigi, Haridimos Kondylakis, Kostas Stefanidis: Fairness in Group Recommendations in the Health Domain. ICDE 2017: 1481-1488 (2017). 89. Erich Teppan, Marcus Zanker, M: Decision biases in recommender systems. Journal of Internet Commerce, 14(2), 255-275 (2015). 90. Mucun Tian, Michael D. Ekstrand: Estimating Error and Bias in Ofﬂine Evaluation Results. CHIIR 2020: 392-396 (2020). 114Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

115. References #11 91. Songül Tolan: Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges. CoRR abs/1901.04730 (2019). 92. Raciel Yera Toledo, Yailé Caballero Mota, Luis Martínez-López: Correcting noisy ratings in collaborative recommender systems. Knowl. Based Syst. 76: 96-108 (2015). 93. Andreu Vall, Massimo Quadrana, Markus Schedl, Gerhard Widmer: Order, context and popularity bias in next-song recommendations. Int. J. Multim. Inf. Retr. 8(2): 101-113 (2019). 94. Mengting Wan, Jianmo Ni, Rishabh Misra, Julian J. McAuley: Addressing Marketing Bias in Product Recommendations. WSDM 2020: 618-626 (2020). 95. Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi: Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random. ICML 2019: 6638-6647 (2019). 96. Jacek Wasilewski, Neil Hurley: Are You Reaching Your Audience?: Exploring Item Exposure over Consumer Segments in Recommender Systems. UMAP 2018: 213-217 (2018). 97. Bin Xia, Junjie Yin, Jian Xu, Yun Li: WE-Rec: A fairness-aware reciprocal recommendation based on Walrasian equilibrium. Knowl. Based Syst. 182 (2019). 98. Yuanbo Xu, Yongjian Yang, Jiayu Han, En Wang, Fuzhen Zhuang, Hui Xiong: Exploiting the Sentimental Bias between Ratings and Reviews for Enhancing Recommendation. ICDM 2018: 1356-1361 (2018). 99. Himank Yadav, Zhengxiao Du, Thorsten Joachims: Fair Learning-to-Rank from Implicit Feedback. CoRR abs/1911.08054 (2019). 115Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

116. References #12 100. Longqi Yang, Yin Cui, Yuan Xuan, Chenyang Wang, Serge J. Belongie, Deborah Estrin: Unbiased offline recommender evaluation for missing-not-at-random implicit feedback. RecSys 2018: 279-287 (2018). 101. Ke Yang, Julia Stoyanovich: Measuring Fairness in Ranked Outputs. SSDBM 2017: 22:1-22:6 (2017). 102. Sirui Yao, Bert Huang: Beyond Parity: Fairness Objectives for Collaborative Filtering. NIPS 2017: 2921-2930 (2017). 103. Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, Ed H. Chi: Sampling-bias-corrected neural modeling for large corpus item recommendations. RecSys 2019: 269-277 (2019). 104. Meike Zehlike, Carlos Castillo: Reducing Disparate Exposure in Ranking: A Learning To Rank Approach. WWW 2020: 2849-2855 (2020). 105. Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, Ricardo Baeza-Yates: FA*IR: A Fair Top-k Ranking Algorithm. CIKM 2017: 1569-1578 (2017). 106. Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay: Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Comput. Surv. 52(1): 5:1-5:38 (2019) 107. Xiaoying Zhang, Hong Xie, Junzhou Zhao, John C. S. Lui: Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations. IJCAI 2018: 5409-5413 (2018). 108. Xing Zhao, Ziwei Zhu, Majid Alfifi, James Caverlee: Addressing the Target Customer Distortion Problem in Recommender Systems. WWW 2020: 2969-2975 (2020). 109. Yong Zheng, Tanaya Dave, Neha Mishra, Harshit Kumar: Fairness In Reciprocal Recommendations: A Speed-Dating Study. UMAP (Adjunct Publication) 2018: 29-34 (2018). 116Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

117. References #13 110. Ziwei Zhu, Xia Hu, James Caverlee: Fairness-Aware Tensor-Based Recommendation. CIKM 2018: 1153-1162 (2018). 117Hands on Data and Algorithmic Bias in Recommender SystemsBoratto and Marras

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