=Paper=
{{Paper
|id=Vol-3908/paper_10
|storemode=property
|title=Enhancing Fairness Through Time-aware Recourse: a Pathway to Realistic Algorithmic
Recommendations
|pdfUrl=https://ceur-ws.org/Vol-3908/paper_10.pdf
|volume=Vol-3908
|authors=Isacco Beretta,Martina Cinquini,Isabel Valera
|dblpUrl=https://dblp.org/rec/conf/ewaf/BerettaCV24
}}
==Enhancing Fairness Through Time-aware Recourse: a Pathway to Realistic Algorithmic
Recommendations==
https://ceur-ws.org/Vol-3908/paper_10.pdf
Enhancing Fairness through Time-Aware Recourse: A
Pathway to Realistic Algorithmic Recommendations⋆
Isacco Beretta1,† , Martina Cinquini1,*,† and Isabel Valera2
1
Department of Computer Science, University of Pisa, Pisa
2
Department of Computer Science, Saarland University, Saarbrücken, Germany
Abstract
Algorithmic Recourse (AR) addresses adverse outcomes in automated decision-making by offering
actionable recommendations. However, current state-of-the-art methods overlook the interdependence
of features and do not consider the temporal dimension. To fill this gap, time-car emerges as a pioneering
approach that integrates temporal information. Building upon this formulation, this work investigates
the context of fairness, specifically focusing on the implications for marginalized demographic groups.
Since long wait times can significantly impact communities’ financial, educational, and personal lives,
exploring how time-related factors affect the fair treatment of these groups is crucial to suggest potential
solutions to reduce the negative effects on minority populations. Our findings set the stage for more
equitable AR techniques sensitive to individual needs, ultimately fostering fairer suggestions.
Keywords
Algorithmic Recourse, Fairness, Consequential Recommendations
1. Introduction
Algorithmic Recourse (AR) seeks to provide actionable recommendations that should be per-
formed to reverse negative outcomes from automated decision-making systems. Recently, this
field has emerged as one of the most promising solutions to explainability in Machine Learning
due to its compliance with legal requirements [1], its psychological benefit for the individual [2],
and its potential to explore “what-if” scenarios [3]. Among current literature, recent work [4]
highlights that a significant drawback of AR methods is the implicit assumption of examining
features as independently manipulable inputs. Since the individual’s attributes change may
have downstream effects on other features, observing and identifying causal mechanisms is
crucial in analyzing real-world scenarios to avoid sub-optimal or infeasible actions. From this
perspective, [5, 6] propose a fundamental reformulation of the recourse problem, incorporating
knowledge of causal dependencies into recommending recourse actions. The ability to assess
the causal relationships explicitly guarantees plausible counterfactuals [7] and improves the
EWAF’24: European Workshop on Algorithmic Fairness, July 01–03, 2024, Mainz, Germany
*
Corresponding author.
†
These authors contributed equally.
$ isacco.beretta@phd.unipi.it (I. Beretta); martina.cinquini@phd.unipi.it (M. Cinquini); ivalera@cs.uni-saarland.de
(I. Valera)
https://marti5ini.github.io/ (M. Cinquini); https://ivaleram.github.io (I. Valera)
0000-0002-0877-7063 (I. Beretta); 0000-0002-0877-7063 (M. Cinquini); 0000-0002-6440-4376 (I. Valera)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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user’s perception of a decision’s quality since it reflects the tendency of human beings to think
in terms of cause-effect [8].
A significant limitation of current methods is their inability to incorporate the temporal
dimension. Neglecting the temporal interdependencies between features and actions can result
in erroneous identification of the more effective features cost and time-wise. As a result, there
is a need to devise Causal AR techniques that can incorporate temporal information to provide
explanations that precisely reflect the complex dynamics of the system and to guarantee that
the recommendations offered are reliable and plausible.
In [9], the authors discuss the necessity of interpreting the causal model as a representation
of a dynamical process that involves the evolution of its instances over time. Specifically, they
introduce time-car, one of the first proposals on integrating the temporal dimension into a
Causal AR problem by including the topological information of the causal graph in the cost
function evaluation.
This research investigates the implications of fairness within the time-car framework,
focusing on how longer periods needed for certain tasks affect marginalized demographic
groups and their connection to socioeconomic stability, educational opportunities, and overall
well-being. The increased time required for these tasks can intensify existing inequalities and
vulnerabilities, leading to a continuous cycle of disadvantage that is hard to break. This work
aims to formulate fairer AR methods sensitive to these populations’ unique needs and time
constraints.
2. Fairness through Time-Aware Recourse
Actionable Recourse. The problem of AR can be formulated as a constrained optimization in
the following terms: given a binary classification model ℎ : X → {0, 1}, and an instance 𝑋 for
which ℎ(𝑋) = 0, the goal is to determine the action A𝛿* satisfying
A𝛿* = arg min 𝑐(𝑋, A𝛿 ) 𝑠.𝑡. ℎ(A𝛿 (𝑋)) = 1
A𝛿
where A𝛿 (𝑋) represents a modified version of 𝑋. In other words, the objective is to identify
the minimal cost action that alters the model’s decision from unfavorable to favorable.
2.1. Same Cost, Different Times
In [9], the authors introduce a new definition of the cost of an action that incorporates the
temporal dimension:
𝑐(𝑋, 𝑌, A𝛿 ) = 𝑐𝑠 (𝑋, A𝛿 ) + 𝜆𝑐𝑡 (𝑋, A𝛿 , 𝑌 ) ,
where 𝑋 denotes the individual’s initial state, 𝑌 is the target state (e.g., the one that guarantees
loan acceptance), and A𝛿 is the action taken to obtain the transition between them. 𝜆 is a tunable
parameter that values how important is time compared to the other features. In particular, it
balances the two components of the cost function, namely 𝑐𝑠 , which denotes the cost function
in the feature space, and 𝑐𝑡 , which reflects the time part. A time-unaware recommendation
algorithm is basically one that fixes 𝜆 = 0.
(a) Not accounting for time could introduce hidden (b) Let 𝑐𝑜𝑠𝑡(𝛿) = 𝑐𝑠 (𝛿) + 𝜆1(𝛿𝑖𝑛𝑐𝑜𝑚𝑒 ̸= 0),
biases in recommendation algorithms. it could be that 𝜆 is depending on age.
Figure 1: Possible scenarios where the time cost matters from a fairness perspective.
This section explores a scenario where sensitive attributes are included among the features,
denoted as 𝐴 ⊂ 𝑋. We examine the case of two individuals, 𝑖1 and 𝑖2 , with different sensitive
attributes’ values, such that 𝐴(𝑖1 ) ̸= 𝐴(𝑖2 ). We hypothesize that the cost recommendations
from the time-unaware automatic decision system for these individuals, 𝑐𝑠 (𝑋(𝑖1 ), A𝛿1 ) and
𝑐𝑠 (𝑋(𝑖2 ), A𝛿2 ), are approximately equal. This implies that despite the difference in sensitive
attributes, the system suggests similar cost interventions for both individuals. However, the
temporal cost 𝑐𝑡 could vary significantly between them, meaning one individual might need
more time to achieve the desired state than the other. This scenario is illustrated in Figure 1a.
2.2. Not Everyone Values Time Equally
In another scenario, time may be regarded as a resource whose value varies based on individual
characteristics. Figure 1b demonstrates this idea through a specific case related to applying
for a loan. The value of 𝜆 might be higher for the older population as they are likely closer
to retirement and have a limited window to recuperate from financial setbacks. Conversely,
younger individuals might have a lower 𝜆 value given their longer time horizon to adjust their
savings behavior. Hence, financial models must be calibrated to accommodate varying 𝜆 values
across different demographic segments. This understanding enables the creation of customized
recommendations sensitive to each individual’s dynamics and the time-related evaluation of
changes within their specific societal and economic contexts.
2.3. Actionability as a Time-Constraint
In the context of AR, plausibility refers to the perceived consistency and reasonableness of
the recommendations provided by recourse approaches. From a psychological perspective,
providing plausible explanations enables users to form mental models that align with their prior
knowledge [10]. When the temporal dimension is incorporated into causal reasoning, an AR
approach could ensure that the actions suggested are psychologically congruent with human
Figure 2: (Left) As 𝑚𝑎𝑥𝑡𝑖𝑚𝑒 decreases, the graph undergoes a pruning process that reduces the number
of actionable variables. For example, only C and D will be actionable if the user specifies a maximum
time of 2 years for the request. (Right) A real-life application is one where an individual has finite time
available.
intuitions and mental frameworks. This compatibility fosters a sense of trust and confidence in
the algorithmic system, facilitating user acceptance and engagement.
Furthermore, actionability is considered one of the crucial aspects in a counterfactual gen-
eration process, as highlighted in [11]. We propose expanding the concept beyond the notion of
being able to act upon to include the ability to do so within a reasonable timeframe (Figure 2). In
fact, if the action required to implement a recommendation is excessively time-consuming or
impractical, the recommendation becomes unhelpful for the user. In the constrained optimiza-
tion framework of AR, the actionability threshold is directly controlled by the maximum time
constraint, denoted as 𝑚𝑎𝑥𝑡𝑖𝑚𝑒 . This parameter can be determined a priori or adapted based
on the user’s specific requirements each time a request is made. In the latter case, the parameter
enables personalized control over actionability for the applicant. From this perspective, we
propose a new interpretation of fair recommendations expressed as follows:
A time-aware algorithmic recourse model is fair if its recommendations remain fair
under any fixed time constraint.
3. Conclusions
Our work discusses the importance of incorporating temporal dimensions in Causal AR to ad-
dress the unfair time requests on marginalized groups, revealing hidden biases in time-unaware
systems. By showing scenarios where identical cost actions lead to disparate time requirements
for different individuals and by revising actionability to include time constraints, we identify
the need for time-aware models that ensure fairness and align with human psychological
expectations, encouraging trust in automated decision-making and promoting fairer outcomes.
Acknowledgments
Work partially supported by the European Community H2020-EU.2.1.1 programme under the
G.A. 952215 (Tailor project) and under Res. Infr. G.A. 871042 (SoBigData++ project).
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