# Fairness and Transparency in Recommendation: The Users’ Perspective

Nasim Sonboli<EMAIL_ADDRESS>,  Jessie J. Smith
<EMAIL_ADDRESS>University of Colorado BoulderBoulderColoradoUSA ,
Florencia Cabral Berenfus<EMAIL_ADDRESS>University
of Colorado BoulderBoulderColoradoUSA ,  Robin Burke<EMAIL_ADDRESS>University of Colorado BoulderBoulderColoradoUSA and  Casey Fiesler
<EMAIL_ADDRESS>University of Colorado BoulderBoulderColoradoUSA

(2021)

###### Abstract.

Though recommender systems are defined by personalization, recent work has
shown the importance of additional, beyond-accuracy objectives, such as
fairness. Because users often expect their recommendations to be purely
personalized, these new algorithmic objectives must be communicated
transparently in a fairness-aware recommender system. While explanation has a
long history in recommender systems research, there has been little work that
attempts to explain systems that use a fairness objective. Even though the
previous work in other branches of AI has explored the use of explanations as
a tool to increase fairness, this work has not been focused on recommendation.
Here, we consider user perspectives of fairness-aware recommender systems and
techniques for enhancing their transparency. We describe the results of an
exploratory interview study that investigates user perceptions of fairness,
recommender systems, and fairness-aware objectives. We propose three features
– informed by the needs of our participants – that could improve user
understanding of and trust in fairness-aware recommender systems.

fairness, transparency, qualitative study, recommender systems, explanation

††journalyear: 2021††copyright: acmlicensed††conference: Proceedings of the
29th ACM Conference on User Modeling, Adaptation and Personalization; June
21–25, 2021; Utrecht, Netherlands††booktitle: Proceedings of the 29th ACM
Conference on User Modeling, Adaptation and Personalization (UMAP ’21), June
21–25, 2021, Utrecht, Netherlands††price: 15.00††doi:
10.1145/3450613.3456835††isbn: 978-1-4503-8366-0/21/06††ccs: Human-centered
computing User studies††ccs: Information systems Personalization††ccs:
Information systems Recommender systems††ccs: Human-centered computing User
centered design

##  1\. Introduction & Background

In recent years, the penetration of algorithmic systems into many realms of
society has raised concerns that such systems may distribute benefits and
harms unfairly across individuals and groups. This is particularly evident
where high stakes decisions are made, ones that have significant impact on
individuals’ lives and livelihoods. Personalized recommender systems are a
class of algorithms that learn from past user preferences in order to predict
future interests of users and provide them with suggestions tailored to their
tastes. In a personalized system, the objective of the recommendation
algorithm is to accurately represent users’ interests. However, in recent
years, non-accuracy objectives such as fairness have become more common. This
is because recommender systems are increasingly being employed in higher-
stakes areas such as employment and financial services. Moreover, the user –
that is, the consumer of the recommendation – is often not the only
stakeholder in a recommender system. As a result, researchers have begun to
explore how to ensure that a recommender system distributes its benefits
fairly within and across different stakeholder groups, such as users and item
providers (Yao and Huang, 2017; Burke et al., 2018; Ekstrand et al., 2018; Liu
et al., 2019; Kamishima et al., 2016; Beutel et al., 2019; Sonboli et al.,
2020). In this paper, we describe findings from an exploratory interview study
in which we elicited folk theories about recommender systems, as well as
opinions about how fairness might be incorporated into such systems, and what
forms of transparency around these algorithms are most effective for the
users.

###  1.1. Folk Theories to Guide Explanation Design for Fair Recommendations

One method to collect and incorporate users’ needs into a technical system is
to examine their _folk theories_ of that system (Kulesza et al., 2012; Ghori
et al., 2019). Folk theories in Human-Computer Interaction (HCI) are defined
as ”intuitive, informal theories that individuals develop to explain the
outcomes, effects, or consequences of technological systems” (DeVito et al.,
2017). Folk theories can help us understand how users perceive the systems
that they are interacting with (DeVito et al., 2018a; Rader and Gray, 2015;
Eslami et al., 2016), including uncovering inaccurate knowledge that users
might have about a system in order to intervene on misguided or even harmful
behavior on the platform (Eslami et al., 2016; Rader and Gray, 2015; DeVito et
al., 2018b). These theories, accurate or not, shape the nature of user
interaction and experience (Eslami et al., 2019). Therefore, it is important
to broaden the education of users about the inner-workings of these systems as
they lead to a better understanding of AI and web platforms (Long and Magerko,
2020; Eslami et al., 2016).

We argue that in order to design for transparency in a fairness-aware
recommender system, we must first elicit feedback from the user. Folk theories
are one means to recognize where users might have inaccurate knowledge about
the system and its objectives. We propose to use explanations as a way to
effectively educate users about these gaps in their knowledge, which might
include fairness objectives in a recommendation system. Using explanations to
educate in this way can help users create more accurate mental models of these
systems, which can impact their actions on a platform, and help to inform
future designs (Herlocker et al., 2000a).

###  1.2. Fairness Objectives in Recommendation

Fairness in recommendation is generally assessed through two factors:
representation and accuracy. Most of the proposed methods try to ensure parity
in either of these factors among all user groups (e.g. women vs. men) or item
groups (e.g. popular vs. unpopular). Parity is sought to avoid under-
representation or over-representation of items/user subgroups, and to avoid
under-estimation or over-estimation of accuracy for any item or user group
(Yao and Huang, 2017; Steck, 2018; Burke et al., 2018; Ekstrand et al., 2018;
Kamishima et al., 2016; Beutel et al., 2019; Sonboli et al., 2020; Sonboli and
Burke, 2019; Xiao et al., 2017; Ekstrand et al., 2019), as these issues could
lead to discriminatory or unjust consequences for certain users.

Since recommender systems are multistakeholder systems, each stakeholder group
may have different fairness needs. The key stakeholders in a recommendation
system are the consumers (those who receive the recommendations, who we will
also refer to as _users_), the providers (those who provide items that will be
recommended), and the system/platform (that hosts the recommendations) (Burke,
Robin, 2017). Fairness can be defined and sought for any of the stakeholders
involved. In this paper we focus on one aspect of fairness-aware
recommendation: _provider fairness_. In our interviews, we frame the objective
of provider fairness as a method that increases the representation of items
that are consistently not being recommended.

###  1.3. Transparency & Explanations

One of our key areas of interest in this study is the question of how to
design for transparency in the context of fairness-aware algorithms.
Transparency has become an essential feature of algorithmic design, especially
in application areas that are socially sensitive due to (1) legal requirements
for transparency such as the GDPR (Regulation, 2016; Goodman and Flaxman,
2017); (2) the need to build users’ trust in a system (Cramer et al., 2008; Pu
and Chen, 2007; Kizilcec, 2016a); (3) the need for error detection to help
mitigate bias and discrimination in a system (Tintarev and Masthoff, 2007;
Phillips et al., 2020; Goetz et al., 2003; Theodorou et al., 2017); (4)
helping further public adoption of new technologies (Kizilcec, 2016b; Abdul et
al., 2018; Bellotti and Edwards, 2001; Dzindolet et al., 2003; Gregor and
Benbasat, 1999); and/or (5) creating an environment of accountability for the
platforms that host the algorithms (Diakopoulos, 2015; Abdul et al., 2018).

We believe that transparency only increases in importance for fairness-aware
systems (Lepri et al., 2018). Because of the complex and contested nature of
fairness as a concept (Narayanan, 2018), it is not enough to claim that a
system is fair: users need to understand the specifics of the fairness
objectives that have been encoded into it. It is well recognized that users of
recommender systems benefit from explanations of the recommendations that they
receive (Tintarev and Masthoff, 2007; Cosley et al., 2003; Herlocker et al.,
2000b). Because fairness-aware recommendation algorithms influence the
recommendations users receive in a way that responds to fairness goals, we
argue that it is particularly important that recommender systems be
transparent about their fairness objectives.

Transparency for fairness-aware recommendations is different from general
transparency in recommendation algorithms (e.g., as discussed in (Tintarev and
Masthoff, 2007; Sinha and Swearingen, 2002)) in that the goal is not only to
explain how the system works, but also to provide clarity for what its
fairness goals are, the motivations for these goals, and how they might impact
different stakeholders. However, explanations of fairness-aware algorithms
must be done with care; the _ways_ in which fair algorithms are explained to
users have been proven to heavily influence their trust, adoption, and
understanding of these systems (Wang et al., 2020; Colquitt and Chertkoff,
2002). Since explanations in a fairness-aware system impact users’ trust of
and actions within the system, it is especially important to ensure that these
explanations are created effectively and carefully, with the users’ needs in
mind.

While explanation has a long history in recommender systems research (Tintarev
and Masthoff, 2007; Cosley et al., 2003; Herlocker et al., 2000b), there has
been little work that addresses explaining systems that involve a fairness
objective. Similarly, while previous work in other branches of AI has explored
the use of explanations as a tool to increase fairness (for example, by
reducing unwanted bias or discrimination) (Arrieta et al., 2020; Wang et al.,
2019; Doshi-Velez and Kim, 2017), this work has not focused on recommendation
specifically.

This paper contributes findings about participants’ folk theories of
recommendations and fairness, as well as their opinions about transparency and
explanation. Based on these findings, we take the position that explanations
ought to be used as a means to educate users about algorithmic objectives,
especially in fairness-aware systems, and suggest three features that should
be included in effective explanations of fairness-aware recommender systems.

##  2\. Methods

In this work, we use interviews as a way to explore the stories and
experiences of a sample of users of recommender systems. Though some previous
work has stated that user input for algorithmic design may not be helpful for
engineers, due to users’ potential lack of knowledge of the system (Saxena et
al., 2019), we argue that it is precisely this lack of knowledge that can be
beneficial for the design and deployment of explanations in systems.
Understanding what the users do _not_ know about a system can help the
engineer pinpoint where users might need more education about the platforms
they are interacting with, and how an explanation can meet those needs.

As an initial exploratory study, we recruited a convenience sample of 30
undergraduate and graduate students from a large state university in the
United States, all of whom had prior exposure to recommender systems (e.g., on
e-commerce or streaming platforms such as Amazon or Netflix). Our participants
included 18 women and 12 men, with an age range of 18 to 32. 15 participants
were studying Computer Science, Information Science, or related fields, and 15
were pursuing other majors. Participants were recruited via social media
postings, a university announcement board, and advertisements in classes, and
were compensated $10 for their time; this study was approved by our
institution’s IRB. Though our sample of young internet users provides a good
foundation for understanding opinions and concerns that ordinary
recommendation consumers might have about fairness, we recognize that it is
limited in scope and we do not suggest that our findings are generalizable to
a broader population.

We conducted face-to-face, semi-structured interviews with participants that
began with a discussion of their past experiences with recommender systems and
their folk theories of recommendation algorithms and fairness objectives. We
asked them to explain their current knowledge about how these systems work,
and what fairness meant to them in this context. For the latter half of the
interviews, we used the Kiva crowd-sourced microlending platform (explained in
more detail below) as a case study to explore the ways that explanations can
help or harm the participant’s understanding of a fairness-aware system.

Following interview transcription, researchers conducted thematic analysis
(Braun and Clarke, 2006). Three independent analysts conducted open-coding on
a subset of the interviews using MAXQDA software, and then met to confer on,
synthesize, and finalize a set of themes that best captured the insights
gathered from our participants. We conducted a detailed analysis of each
theme, resulting in the overall story that our data tells, as described in
detail in the Results and Discussion sections.

###  2.1. Kiva as a Case Study

Kiva is an organization seeking to enhance financial inclusion in the world
through microlending. Their platform provides a space for those who are
seeking loans (borrowers) to get funded by those who are seeking to supply
capital (lenders). Kiva’s mission emphasizes equitable access to capital for
all borrowers, who are individuals without access to traditional forms of
capital, to improve their living situations. If Kiva implemented a recommender
system, the providers of the recommended content would be the borrowers, and
the consumers of the recommendations would be the lenders (Choo et al., 2014).
Naturally, a recommender system in this context raises an important fairness
concern. If borrowers are recommended inequitably, the system could be
contributing to an inequitable pattern of resource distribution on its
platform. We used Kiva as a case study both to have a concrete example to
ensure consistency across participants, and because Kiva is a real-world
example that contains fairness concerns. In our interviews, we asked the
participants to consider what helpful explanations might look like in Kiva’s
platform if fairness-aware recommendation were implemented.

##  3\. Results

In this section, we describe the main findings from our exploratory interviews
with users of recommender systems. We begin by explaining some of the gaps in
participants’ knowledge about recommender systems and fairness objectives, as
gathered from participant folk theories. Then, we explore the ways in which
participants indicated that explanation design of a fairness-aware system
could help or harm their understanding and trust of the system. Participant
quotes are indicated by anonymized participant numbers; participants 1-15 were
studying CS or adjacent fields, and 16-30 were in non-technical fields.

###  3.1. Folk Theories of Recommender Systems

Some participants expressed that the “black box” nature of recommendations
made it difficult for them to learn how a recommendation system might create
personalized recommendations for them. For example, as P5 said, ”recommender
systems in most cases are pretty unknown to the user.”

However, in general, participants had somewhat of an understanding for how
recommender systems worked – particularly in terms of how much data was being
collected on them. Some participants’ folk theories aligned well with
accuracy-based recommendation algorithms, such as collaborative filtering
algorithms like User-KNN or Item-KNN.

> “So if like a lot of people buy, you know, a hammock, then if a large subset
> of those people who bought a hammock, also bought a sleeping bag, it might
> recommend the sleeping bag to you.” – P21

###  3.2. Folk Theories of Fairness Objectives

When asked about how recommendations could be unfair to users, very few
participants indicated that they had ever thought of provider fairness for
recommendations. This raised a concern that there might be a lack of
communication between the system and the user when it comes to issues of
provider fairness in recommendation.

After a short discussion about fairness objectives and the impact that a
recommender system might have on the providers, many participants indicated
that they thought provider fairness was important to them in a recommender
system, as expressed by P18.

> “I don’t think I’ve thought about it as much from a seller’s perspective,
> but I can see like, you know, these platforms are made for more than just
> [users]. They’re made for [providers] as well. So like people who sell
> things on Amazon or make music and put it on Spotify, it’s like, yeah,
> you’re probably inherently at a disadvantage to people who are already big
> or are already in favor of the algorithm.” – P18

However, the majority of participants were still not entirely sure how
fairness goals might impact their recommendations. Thus, we began to explore
different ways that a recommendation platform could explain this impact to
users in an educational and empowering way.

###  3.3. Explaining Fairness Goals to Users

Throughout the interviews, most participants indicated that they would want to
see the fairness goals of an organization described to the users in some way,
either through a short explanation or an entire page, as described by P8.

> “[Organizations] should have [fairness goals] somewhere I could find it like
> the little ‘about us’, like ‘learn more about our corporation’ tab where
> they would explain ‘these are our moral values, these are what we
> prioritize’.” – P8

One participant thought that fairness goals were best incorporated into the UX
of the platform, as long as the language did not unintentionally manipulate
users.

> “Or like work [fairness goals] into their platform somehow. Like how Spotify
> does the ‘New Music Fridays’… I think that there are opportunities to weave
> that into places on a platform but not necessarily blanket it across so the
> user doesn’t feel like they have choice.” – P29

This raises an important concern that is not new in the field of explanation:
the concern that explanations, if not designed carefully, could be used as a
tool to manipulate users.

###  3.4. Fairness as a User Choice

One outcome of fairness-aware recommendation is to ”nudge” users into choosing
items that they might otherwise not. The intention of this kind of system is
to encourage the user to choose the items that meet the fairness objective of
the system itself, and it is precisely this intention that might underlie some
participants’ concerns with being ”manipulated”. In fairness-aware recommender
systems, _coercing_ users into choosing a ”fair” recommendation – which might
not be ’fair’ for everyone – could be misleading. This concern was expressed
amongst our participants, particularly because fairness definitions are often
disputed, and what might be considered _fair_ for some, could seem _unfair_
for others (Smith et al., 2020). In order to remedy these concerns, several of
our participants indicated that they would prefer to be informed about the
existence of fairness-aware recommendations so that the user could make the
choice of fairness or personalization for themselves, as expressed by P22.

> “[Fairness-aware recommendation] is manipulative in some sort of way. I
> think the best thing that they can do would be to give a short explanation
> that they changed [the personalized recommendation algorithm] and then kind
> of show the whole list and not point out any others in the list… allowing an
> unbiased choice from the viewer.” – P22

If manipulation was a concern amongst participants, then how should
explanations be designed to mitigate that concern? Further, how might
explanations instead be designed to foster trust and communication between a
system and its users? In the next section, we propose that these concerns can
be alleviated through explanations if they are used effectively as a tool for
education.

###  3.5. Explanations as Education

One prominent theme that emerged throughout the interviews was that
transparency as a means for education was essential. Whether an algorithm uses
fairness objectives or not, participants expressed that they needed to be
educated about why they were being recommended the items that they were. This
point was succinctly summed up by P21 who stated: “the more transparency, the
better.”

Many participants indicated that better design practices could help promote
fair treatment for providers and relay this fair treatment back to the
recommendation consumers. In order to ground participants in a specific
platform and discuss concrete explanation designs, we used Kiva as a case
study. Specifically, we asked the participants how they thought transparency
could be designed when fairness goals were incorporated in a system like Kiva.
We gathered feedback about the benefits and flaws of explanation designs that
were minimal and global, versus detailed and specific.

Several participants were in favor of including one global explanation for all
fairness-aware recommendations. Some indicated that a single explanation could
still leave room for the system to include more in-depth explanations about a
specific group of providers that the system is optimizing for fairness. For
example, many participants expressed that they would like to know more about
why Kiva borrowers from a specific geographic area are underfunded on the
platform, and how choosing to lend to people from that area could have a
positive social impact.

> “I like [longer explanations], where you can scroll underneath and get like
> a broader perspective of what’s going on in the whole region, I would like
> that.” – P5

Another alternative was to provide a separate explanation for every individual
recommendation, on the recommended item itself. Many participants expressed
that if explanations were specific to each recommendation, the manner in which
they were formatted played a big role in their utility to the consumer. P30
described that for explanations to be effective and informative, the
“algorithms should be explained to you in a simple sentence structure… short,
concise and in digestible pieces”. They added that explanations should be
accessible and inclusive for all audiences, regardless of their level of
understanding of algorithms.

In summary, we found that when it comes to transparency for fairness-aware
recommendations, design decisions matter. Transparency should give users
greater agency to understand how provider fairness might make their
recommendations different than personalization. Further, when recommendation
fairness is explained to the consumer, it should be explained in a way that is
accessible, understandable, specific, and concise.

##  4\. Discussion & Conclusions

In this study, we explored what users of recommender systems understand about
personalized and fairness-aware recommendations. Through semi-structured
interviews, we used folk theories as an effective method for pinpointing gaps
and inaccuracies in user knowledge of a system. This formed our basis for
understanding that transparency in recommender systems ought to serve as a
means of education for users, especially in a fairness-aware system where the
objectives are often unknown or misunderstood by the user. Taking into account
the feedback provided by the interview participants, we provide the following
suggestions for features that should be included in effective explanation
design for fairness-aware systems.

  1. (1)

_Explanations should define the system’s fairness objective for users._ For
example, an explanation could educate the user about the impact that a
fairness-aware recommendation might have on the item providers.

  2. (2)

_Explanations should not nudge/manipulate users into making a decision, even
if the goal is fairness._ For example, an explanation could educate users
about the existence of a fairness-aware recommendation while still allowing
the user to decide if they prefer personalization instead.

  3. (3)

_Explanations should disclose the motivation for using fairness as a system
objective._ For example, an explanation could educate users about the fairness
concerns of the system, or the values of the organization and why they chose
fairness as an objective.

These three features were compiled based on the specific gaps that our
participants had in their knowledge about fairness-aware recommender systems.
Specifically, the folk theories that participants shared on these systems led
us to believe that there might exist a major gap in communication towards
users about recommender systems’ objectives. These features also summarize
participants’ most frequent desires for what they wished explanations could
teach them about the system in order for them to understand and trust it more.
It is also interesting to note that whenever participants were educated about
provider fairness concerns in recommendations, they generally showed interest
in the option of using a system that had fairness as an objective.

However, we note some limitations in this study’s exploration of participants’
opinions around fairness, transparency and explanation. Firstly, presenting
examples of provider fairness in a face-to-face interview setting could have
resulted in acquiescence response bias, or a tendency for participants to
frame their views in ways that they believed was expected from or valued by
the interviewer (Liu et al., 2017). Moreover, it is possible that social
desirability response bias could have influenced participants’ expressed
interest in provider fairness, when discussing Kiva as a nonprofit
organization seeking to increase financial access globally (Randall and
Fernandes, 1991; van de Mortel, 2008). It may also be the case that
recommender systems that do not carry such high stakes for providers (e.g., a
food delivery recommender system) might attract different levels of interest
from users when it comes to provider fairness. Thus, we suggest that future
work could further investigate similar questions by way of anonymous self-
reports, or by assessing participant behavior in an experimental design.

With this work, we encourage organizations that are trying to incorporate
fairness into the design of their recommender systems to seek input from their
users when designing for transparency. If transparency design fails to
consider the knowledge (or lack thereof) of the users, the platform runs the
risk of losing user trust. Moreover, if fairness goals are explained to users
in vague or ineffective ways, this can cause confusion or lead to
misunderstanding of the goals of the system. In turn, when fairness goals are
explained to the users effectively, they can gain agency, and be left feeling
more empowered and knowledgeable about the platform and their impact while
using it. Ultimately, we argue that in order to gain users’ trust in and
adoption of a fairness-aware recommender system, collaboration is _necessary_.
Through effective communication, feedback, and transparency, recommender
systems can contribute to a more empowering, fair, and trustworthy digital
future.

##  5\. Acknowledgements

This work was supported by the National Science Foundation under Grant No.
1911025.

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