FRecs - Fairness, Diversity and Transparency in Health Recommenders: Challenges & Ob jectivesTampere University Tampere
,
Finland
With the growing complexity of the available online information, users nd themselves overwhelmed by the mass of choices available. To facilitate users, recommender systems provide suggestions on interesting data items to them. Big data technology promises to improve people's lives in this direction, by enhancing the discovery of interesting information. However, this technology, if not used responsibly, may lead to discrimination and amplify biases in the original data; so, recommendations may play an important role in guiding users' decisions and forming opinions. In this paper, we focus on providing useful resources to patients that is essential in achieving the vision of participatory medicine. Speci cally, the objective of FRecs is to create new algorithms for generating responsible recommendations, i.e., recommendations that ensure fairness, diversity and transparency. Producing responsible recommendations is timely due to the huge growth of big data technologies and the current debate on fairness and transparency in algorithmic decision making, yet is not well enough supported by existing models and algorithms.
-
Nowadays, there is a big number of people who search online for health and
medical information. To facilitate the user selection process, given the growing
complexity of the available online information, recommender systems provide
suggestions on resources of potential interest to the users, like news articles
and other sources. The interest of a user for a suggested data item is inferred
typically, from the user's health background and interests in paper, electronic,
or mental records.
Recommender systems have attracted extensive research attention and have
been deployed in a wide range of applications. Recently there are also examples
coming from the health domain (e.g., [
25, 24
]). A recommender system consists
typically of a set of data items, data sources in the form of documents in our
case, a set of users and the ratings of users for certain documents. Typically, the
cardinality of the document set is very high and users rate only a few documents.
For the documents unrated by the users, recommender systems estimate a
relevance score, following for example the collaborative ltering approach [
19
], where
the relevance scores predicted for a user produced based on the ratings of other
similar users, or the content-based approach [
15
], where the system recommends
documents with features similar to documents a user likes. In FRecs, apart from
recommendations for individual users, we pay special attention to
recommendations for groups, for supporting cases in which a group of people participates in
an activity, e.g., a group therapy session, targeting at best satisfying the
preferences of all the group members [
1, 13
]. We next point out recommenders systems
state of the art in areas related to FRecs.
Background in fairness in recommender systems. By fairness, we
typically mean lack of bias [
17, 16, 18
]. It is not correct to assume that insights
achieved via computations on data are unbiased simply because data was
collected automatically or processing was performed algorithmically. Bias may come
from the algorithm, re ecting, for example, commercial or other preferences of
its designers, or even from the actual data, for example, if a survey contains
biased questions. Previous works in recommenders consider the notion of
fairness only indirectly, without guaranteeing it via explicit models and algorithms
[
9, 14, 24, 25
]. In the context of group recommendations, there are approaches
that introduce additional factors into the model, such as agreement [
1
] or social
relationships [
11
] among group members, but still without directly tackling the
concept of fairness. More recently, [
20
] uses the concept of fairness for
expressing the notion of quality of a set of items for a group. FRecs aims to model and
formally de ne fairness, as well as to introduce algorithms that directly optimize
it.
Background in diversity in recommender systems. Diversity ensures that
di erent kinds of data items are represented in the output of an algorithmic
process. For example, in a news recommender, instead of suggesting only news from
user's favourite political party, an approach would be to also display news from
other political parties to break out of user's internet bubble. This is a general
term used to capture the quality of a set of items, with regards to the variety of its
constituent elements. There is considerable work on search result diversi cation
(for surveys, see at [
32, 10
]). For example, [
21
] proposes diverse keyword database
search that utilizes user preferences, [
33
] introduces an order-independent
intralist similarity measure to assess the topical diversity of recommendation lists,
while [
27
] focuses on diversifying the recommended items with respect to user
interests. The work in FRecs di ers from prior work in the sense that we consider
a family of diversity constraints that can express coverage-based, in addition to
distance-based (relying on the pairwise similarity between documents), diversity.
We will take into account these constraints in order to de ne measures of
fairness. Fairness is related to diversi cation, for instance, when considering that
a fair set of documents is likely to include documents that represent di erent,
or even all, categories of documents, or when considering groups, that satisfy
di erent users.
Background in transparency in recommender systems. Users many times
want to know and control both what is being recorded about them, and how this
piece of information is being used, for example, to recommend content or for
target advertising. While privacy is clearly related to this, here we focus on the
concept of transparency that plays an important role as well. A transparent data
analysis framework requires suggestions that can be easily understood by the
users. That is, transparency contrasts with the concept of \black box" systems,
where even the data scientist or algorithm designer cannot explain the output
of an algorithm. In FRecs, we envision providing explainable documents along
with explanations. There are di erent ways of classifying explanation styles. A
user-based explanation is based on similar users [
29
], while an item-based
explanation presents the items that had the highest impact on the recommender's
decision [
28
]. In all styles, the input data employed for producing
recommendations, may be di erent from the input data used for generating the explanation
[
8, 23
], leading to explanation generation modules that are separate from the
recommender system. However, building recommendations based on the items'
explainability, thus integrating recommendation and explanation, may improve
transparency by suggesting interpretable items to the user. Recently, [
31
]
proposes a model-based collaborative ltering approach to generate explainable
recommendations based on item features and sentiment analysis of user reviews, in
addition to the ratings data. In our approach, we aim at an integrated approach
that considers explanations in the recommendation process rather than
separating the explanation from the recommendation process. Recommendations along
with their associated explanations will form graph-based summaries that include
documents that ensure fairness and diversity.
Background in online recommendations. In practice, even though a set
of suggestions has to be selected, not all data items in the set is available for
evaluation at once. Rather, items may appear one at a time, with a decision
to be made on the speci c item instantaneously. Such situations motivate us
to consider an online scenario, which is sometimes referred to as streaming. In
such scenario, we need to process items incrementally, maintaining a valuable
recommendations set at any point in time. Previous works in this line, e.g., [
5
],
consider a xed window of recent items, posing a problem for items that are not
generated at a xed rate. More recently, [
4
] proposes algorithms to diversify a
stream of results using a jumping window approach. FRecs aims to start at the
beginning of the stream, providing a fair, diverse and transparent set of
documents considering the whole document set, rather than a xed number of recent
documents. This will allow our algorithm to actively withdraw documents from
the recommendations set, instead of simply dropping documents as they leave
the window. To our knowledge, combing fairness, diversity and transparency,
especially in an online setting has not been considered.
We will use the prominent collaborative ltering recommender model. In
collaborative ltering, users preferences are represented by a ratings matrix. It
is based on the idea that people who agreed in their evaluation of certain data
items in the past are likely to agree again in the future. A key advantage of
collaborative ltering is that it is capable of accurately recommending, even
complex, data items without requiring an understanding of the item itself. Our
goal is to extend the exible collaborative ltering model by integrating fairness,
diversity and transparency into the recommender system.
2
The FRecs Challenges & Objectives
Given that both fairness and diversity are set-based concepts (e.g., it makes no
sense to talk about an individual item as being diverse), in FRecs, we focus on
set-based selections, unlike most algorithmic decision-making approaches that
are based on individual items, where a utility score is associated with each item
typically computed with respect to the values of the item. Even more so than in
traditional recommendations for individual users, identifying documents of high
relevance to a group is challenging, especially for cases where group members
disagree on their favorite items.
We focus on developing novel data analysis methods that ensure fairness,
diversity and transparency in set selection for recommendations. In addition to
produce traditional recommendations, we consider the online case, in which not
all documents are available at once, and we have to classify each individual
document as presented, into the selected ones or not for recommendations. Our
aim is on both recommendations for individual users and on recommendations for
groups. Producing responsible recommendations is timely due to the huge growth
of big data technologies and the current debate on fairness and transparency in
algorithmic decision making, yet is not well enough supported by existing models
and algorithms.
The objective of FRecs is to create new algorithms for responsible
recommendations for individual users and groups of users, i.e., recommendations that
ensure fairness, diversity and transparency. The algorithms will cover both cases
in which we make the assumption that all documents are available before any
selections have to be made, as well as we decide whether to accept, reject or
defer a document in an online manner as the documents appear. We translate
the aforementioned challenges into research objectives, described below:
Fairness and diversity. Previous work focused separately on either fairness
or diversity in query processing and recommender systems [
14, 5, 25, 10
]; it is a
useful basis but must be signi cantly extended to bring in both fairness and
diversity. We consider fairness as the proportional representation of the values
of attributes of particular concern, and diversity as the existence of such values.
In this line, we will study how fairness and diversity can be combined with
respect to the users preferences as expressed by their ratings. We will pay special
attention on allowing combinations of attributes of particular concern, so as to
capture attribute dependencies.
Transparency and explainability. In all explanation styles, data
employed for producing recommendations, can be di erent from the data used in
generating the explanations [
8, 28, 13
], leading to explanation generation modules
that are separate from the recommender system. Performing the
recommendation task based on the items' explainability, thus integrating recommendation
and explanation, can improve transparency by suggesting interpretable
documents to the user, while preserving the powerful prediction of the collaborative
ltering approach. Moving forward, we target at building upon our previous
work on summary-driven data exploration [
26, 22
], in order to provide
explainable recommendations through summaries. Recommendation summaries will be
de ned as the process of distilling knowledge from the whole result set in
order to produce an abridged version. We do not focus on providing only the most
important documents, i.e., the ones with the maximum utility score, but on
summaries consisting of explainable documents that exhibit fairness and diversity.
We aim to handle e ciently exploratory operations, like zoom-in and zoom-out,
on both data models, providing granular information access to the user.
Individual user and group recommendations. In addition to
individual user recommendations, FRecs focuses also on recommendations for groups.
Based on the useful insights produced by previous work on group
recommendations [
24, 25
], we will provide new de nitions for fairness and diversity applicable
to groups. Regarding fairness, in addition to proportional representation, we
consider also envy-freeness, in which intuitively, a user considers a set of documents
fair for him/her, if there are documents for which the user does not feel jealous,
i.e., the presented documents have utility scores within the range of scores of
the best documents for him/her. Coverage-based diversity for groups rely on the
existence of a number of documents for all group members. Furthermore, our
early work on the e ective presentation of group recommendations [
13, 23
], will
be extended by integrating documents' explainability into the group
recommendation process. We envision a de nition for FRecs group recommendations in
which all fairness, diversity and transparency aspects play a crucial role.
Static and online processing. For locating the recommendations to be
presented to the user/group, we consider two cases. First, in the static case, we
solve the problem making the assumption that we have access to all documents.
Fairness, diversity and transparency constraints will direct the process; the goal
is to return the set of documents with the highest utility computed with respect
to these constraints. In addition, we consider the online case, in which not all
documents in the set are available at once. This way, we exploit our previous
work in online settings [
5
] to classify each individual document, as presented, into
the selected or not selected box based on the fairness, diversity and transparency
constraints. The focus here is on extending the K-choice Secretary Problem [
2
],
in order to design and develop online methods for picking a set of documents,
presented in random order - independently to their utility, subject to fairness,
diversity and transparency constraints.
3
Research Methods
In FRecs, we extend state of the art work on fairness, diversity and transparency
in set selection, explanations for recommendations, group recommendations and
online set selection.
Fairness and diversity in recommender systems. The basic problem
setting is that we have a set of documents, each with associated attributes.
From this set, we wish to select K documents to maximize a utility score. The
utility score of the K documents can be computed based on the documents
individual utility scores (as obtained, for example, by a recommendation
algorithm), or based on more complex functions that take into account co-existence
of documents. Let us now turn to fairness and diversity constraints. Among the
attributes associated with documents, we assume that one attribute is of
particular concern. Our notions of fairness and diversity are de ned with respect to
the value of this attribute. In practice, there may be multiple such attributes,
rather than just one. If combinations of multiple attributes are of concern, or
if dependencies between the attributes need to be captured explicitly, we could
represent such combinations as a single Cartesian product attribute of concern.
Attributes may also have associated privacy concerns, and so may need to be
converted to noisy histograms, e.g., to enforce di erential privacy. We assume
that documents are partitioned on the value of the attribute of concern. Let
there be d distinct values of the attribute of concern. Our requirement is to
choose ki elements for each distinct value i in [1; : : : ; d] with each ki 2 [0; K] and
Sum(k i) = K. This begs the question of what the ki values should be. We next
brie y consider several notions of fairness and diversity that will be exploited
towards producing recommendations in FRecs.
For achieving fairness, we will start by considering the proportional
representation of the values of the attribute of concern. Namely, proportional
representation requires that the desired size K of the selected documents set be prorated
among the d categories. Another potentially appropriate fairness metric is the
normalized di erence: the mean di erence normalized by the rate of positive
outcomes, which in our case corresponds to being selected among the top-k [
34,
3
]. Proportional representation can be extended, so as to be used for producing
fair recommendations for groups. In this case, we say that a user is satis ed
by a document, if the document is ranked in the top documents for the user.
Intuitively, this means that the user considers the top-K recommendations fair
for him/her, if there are at least a particular number of documents that the user
likes. As an alternative, we consider fair group recommendations by counting
the envy of the users in a group. This way, we say that a user is satis ed by
a speci c document, if the utility score of the document for the user is among
the top scores of the users in the group for this document. Intuitively, in this
de nition, the user considers the package fair for him/her, if there are at least a
particular number of documents for which the user does not feel envious. With
respect to proportional representation and envy-freeness concepts, the goal is to
de ne measures for counting the fairness of a set of documents.
FRecs considers both distance-based and coverage-based (di erently to
previous approaches) de nitions for diversity. Distance-based diversity rely on a
pairwise distance measure between two documents appearing in the resulting
set. Given such a measure, the diversity of a set of documents is expressed by
using an aggregation function of the pairwise distances between the documents
in the set [
21
]. Maximizing the minimum (resp., average) diversity is known as
MaxMin (resp., MaxSum) diversi cation [
6
]. Assuming that we have a set of
categories, and that each document belongs to one or more categories,
coveragebased diversity aims to represent every category in the selected set [
32
]. Whether
this is possible depends on how K, the number of documents selected in total,
compares to d, the number of categories of documents. This way, the diversity
of a set of documents is expressed as the extent to which the documents in the
set cover the categories.
Summarizing the scenarios considered above, our focus is on designing and
developing algorithms that allow us to treat combinations between fairness and
diversity. Namely, we formulate the problem as follows: Given a collection of
documents, a fairness measure and a set of categories (resp., a distance measure),
locate the set of documents that maximizes fairness, and includes y documents
from each category (resp., and all pairs of documents in the set have distance
greater than a threshold). Since the problem of identifying the diverse set of
documents with the maximum fairness is NP-hard, for enhancing the e ciency
of our approach, we opt to start with greedy algorithms, like for example, add
the document to the output set, in each round of the algorithm, that maximizes
fairness and covers a category that does not exist in the already selected
documents. More sophisticated algorithms will be designed in order to ensure the
satisfaction of all constraints dictated by fairness and diversity.
Transparency via explanations in recommender systems. It has been
shown that explanations in recommender systems can help users make more
accurate decisions; hence, improving user satisfaction and acceptance of
recommendations [
8
]. The typical ow of most recommenders is to generate
explanations for the recommendations produced [
30
]. In FRecs, we propose integrating
explanations with recommendations. For doing so, we need to be able to
quantify the explainability of a documents, so as to combine explainability with the
utility score of the document. Considering, for instance, the user-based
collaborative ltering case, explainability can be formulated with respect to the ratings
of the users that are similar to a user in question. If many similar users have
rated an document, this can provide a basis upon which to explain the
document, which in turn means that the document can be considered as explainable.
Our goal is to propose novel algorithms for producing recommendations that, in
addition to the utility score of an document, consider the explainability score of
the document. We formulate this as an optimization problem that outputs the
set of documents with a maximum score that is de ned by combining documents
utility and explainability.
Given the growing complexity of the available online information, databases
are becoming increasingly di cult to understand and use. To facilitate users,
FRecs builds upon our previous work [
26
] and provides, in an e ective way,
overviews of recommendations, forming graph-based summaries that include the
most valuable documents for a user or group, subject to fairness, diversity and
explainability. To create an explainable summary, we include in the graph, nodes
that represent both documents and users, along with their connections, so as
to highlight interesting associations and enable a decent understanding of the
provided information. Moving forward, although exploration operators over
summaries have already been identi ed as useful (e.g., [
12
]), the available approaches,
even in di erent domains, are limited, working with prede ned taxonomies on
documents. Here, we introduce exploration operators on recommendation
summaries, that can be used iteratively, to allow focusing on a speci c subgraph of
the initial summary, providing granular information access to the user.
Zoomin and zoom-out operators are de ned in order to be able to promote either
fairness, diversity or both at the same time.
Online recommendations. In practice, even though a set of documents
has to be selected, not all documents in the set may be available for evaluation
at once. Rather, they may appear one at a time, with a decision to be made on
the speci c document instantaneously. This means that we have to select or not
each individual document, as presented, subject to the utility, fairness, diversity
and explainability criteria in our problem statement.
The problem of designing an online algorithm to optimize the probability
of selecting the document with the maximum utility in a randomly-ordered
sequence has been studied extensively [
7
], and is known as the Secretary Problem.
In this problem, the goal is to hire one secretary from a pool of N candidates,
where candidates arrive in random order. When a candidate is interviewed, the
decision must be made to hire or reject the candidate, and this decision is
irreversible. A generalization of this problem, called the K-choice Secretary Problem
[
7
], is stated as follows: design an online algorithm for picking K out of N
documents presented in random order, to maximize their expected sum. In FRecs,
we target at designing online algorithms for picking K out of N documents, each
with an associated utility score, presented in random order. Speci cally, the goal
is to select documents to recommend that maximize their expected aggregated
utility, subject to fairness, diversity and explainability. Intuitively, we start with
the basic idea of solving the K-choice Secretary Problem separately for each
concept, aiming to satisfy all of them. In addition, we will study other interesting
variants, like how to work when documents are partially ordered.
Conclusions
FRecs aims to develop novel algorithms for selecting sets of documents, in both
a static and online setting, optimized for providing fair, representative and
explainable recommendations in the health domain, as well as for
recommendations comprehension. The key insight is that fairness, diversity and transparency
should not be analyzed in isolation, but together. The approach advances the
state of science in realizing a holistic treatment of fairness, diversity and
transparency through di erent stages of the data management and analysis life-cycle,
namely, data processing, selection, ranking, and result interpretation. The work
also concerns enabling incremental maintenance of the responsible properties of
a set of recommendations.
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