8 research outputs found
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction
As algorithms are increasingly used to make important decisions that affect
human lives, ranging from social benefit assignment to predicting risk of
criminal recidivism, concerns have been raised about the fairness of
algorithmic decision making. Most prior works on algorithmic fairness
normatively prescribe how fair decisions ought to be made. In contrast, here,
we descriptively survey users for how they perceive and reason about fairness
in algorithmic decision making.
A key contribution of this work is the framework we propose to understand why
people perceive certain features as fair or unfair to be used in algorithms.
Our framework identifies eight properties of features, such as relevance,
volitionality and reliability, as latent considerations that inform people's
moral judgments about the fairness of feature use in decision-making
algorithms. We validate our framework through a series of scenario-based
surveys with 576 people. We find that, based on a person's assessment of the
eight latent properties of a feature in our exemplar scenario, we can
accurately (> 85%) predict if the person will judge the use of the feature as
fair.
Our findings have important implications. At a high-level, we show that
people's unfairness concerns are multi-dimensional and argue that future
studies need to address unfairness concerns beyond discrimination. At a
low-level, we find considerable disagreements in people's fairness judgments.
We identify root causes of the disagreements, and note possible pathways to
resolve them.Comment: To appear in the Proceedings of the Web Conference (WWW 2018). Code
available at https://fate-computing.mpi-sws.org/procedural_fairness
Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harm
Artificial intelligence (AI) systems can cause harm to people. This research
examines how individuals react to such harm through the lens of blame. Building
upon research suggesting that people blame AI systems, we investigated how
several factors influence people's reactive attitudes towards machines,
designers, and users. The results of three studies (N = 1,153) indicate
differences in how blame is attributed to these actors. Whether AI systems were
explainable did not impact blame directed at them, their developers, and their
users. Considerations about fairness and harmfulness increased blame towards
designers and users but had little to no effect on judgments of AI systems.
Instead, what determined people's reactive attitudes towards machines was
whether people thought blaming them would be a suitable response to algorithmic
harm. We discuss implications, such as how future decisions about including AI
systems in the social and moral spheres will shape laypeople's reactions to
AI-caused harm.Comment: ACM CHI 202
Dimensions of Diversity in Human Perceptions of Algorithmic Fairness
Algorithms are increasingly involved in making decisions that affect human
lives. Prior work has explored how people believe algorithmic decisions should
be made, but there is little understanding of which individual factors relate
to variance in these beliefs across people. As an increasing emphasis is put on
oversight boards and regulatory bodies, it is important to understand the
biases that may affect human judgements about the fairness of algorithms.
Building on factors found in moral foundations theory and egocentric fairness
literature, we explore how people's perceptions of fairness relate to their (i)
demographics (age, race, gender, political view), and (ii) personal experiences
with the algorithmic task being evaluated. Specifically, we study human beliefs
about the fairness of using different features in an algorithm designed to
assist judges in making decisions about granting bail. Our analysis suggests
that political views and certain demographic factors, such as age and gender,
exhibit a significant relation to people's beliefs about fairness.
Additionally, we find that people beliefs about the fairness of using
demographic features such as age, gender and race, for making bail decisions
about others, vary egocentrically: that is they vary depending on their own
age, gender and race respectively.Comment: Presented at the CSCW 2019 workshop on Team and Group Diversit
Taking Advice from (Dis)Similar Machines: The Impact of Human-Machine Similarity on Machine-Assisted Decision-Making
International audienceMachine learning algorithms are increasingly used to assist human decision-making. When the goal of machine assistance is to improve the accuracy of human decisions, it might seem appealing to design ML algorithms that complement human knowledge. While neither the algorithm nor the human are perfectly accurate, one could expect that their complementary expertise might lead to improved outcomes. In this study, we demonstrate that in practice decision aids that are not complementary, but make errors similar to human ones may have their own benefits. In a series of human-subject experiments with a total of 901 participants, we study how the similarity of human and machine errors influences human perceptions of and interactions with algorithmic decision aids. We find that (i) people perceive more similar decision aids as more useful, accurate, and predictable, and that (ii) people are more likely to take opposing advice from more similar decision aids, while (iii) decision aids that are less similar to humans have more opportunities to provide opposing advice, resulting in a higher influence on people's decisions overall
“Look! It’s a Computer Program! It’s an Algorithm! It’s AI!”: Does Terminology Affect Human Perceptions and Evaluations of Algorithmic Decision-Making Systems?
In the media, in policy-making, but also in research articles, algorithmic decision-making (ADM) systems are referred to as algorithms, artificial intelligence, and computer programs, amongst other terms. We hypothesize that such terminological differences can affect people’s perceptions of properties of ADM systems, people’s evaluations of systems in application contexts, and the replicability of research as findings may be influenced by terminological differences. In two studies (N = 397, N = 622), we show that terminology does indeed affect laypeople’s perceptions of system properties (e.g., perceived complexity) and evaluations of systems (e.g., trust). Our findings highlight the need to be mindful when choosing terms to describe ADM systems, because terminology can have unintended consequences, and may impact the robustness and replicability of HCI research. Additionally, our findings indicate that terminology can be used strategically (e.g., in communication about ADM systems) to influence people’s perceptions and evaluations of these systems
The Conflict Between Explainable and Accountable Decision-Making Algorithms
Decision-making algorithms are being used in important decisions, such as who
should be enrolled in health care programs and be hired. Even though these
systems are currently deployed in high-stakes scenarios, many of them cannot
explain their decisions. This limitation has prompted the Explainable
Artificial Intelligence (XAI) initiative, which aims to make algorithms
explainable to comply with legal requirements, promote trust, and maintain
accountability. This paper questions whether and to what extent explainability
can help solve the responsibility issues posed by autonomous AI systems. We
suggest that XAI systems that provide post-hoc explanations could be seen as
blameworthy agents, obscuring the responsibility of developers in the
decision-making process. Furthermore, we argue that XAI could result in
incorrect attributions of responsibility to vulnerable stakeholders, such as
those who are subjected to algorithmic decisions (i.e., patients), due to a
misguided perception that they have control over explainable algorithms. This
conflict between explainability and accountability can be exacerbated if
designers choose to use algorithms and patients as moral and legal scapegoats.
We conclude with a set of recommendations for how to approach this tension in
the socio-technical process of algorithmic decision-making and a defense of
hard regulation to prevent designers from escaping responsibility.Comment: To appear in the FAccT 2022 proceeding
"Look! It's a Computer Program! It's an Algorithm! It's AI!": Does Terminology Affect Human Perceptions and Evaluations of Algorithmic Decision-Making Systems?
In the media, in policy-making, but also in research articles, algorithmic
decision-making (ADM) systems are referred to as algorithms, artificial
intelligence, and computer programs, amongst other terms. We hypothesize that
such terminological differences can affect people's perceptions of properties
of ADM systems, people's evaluations of systems in application contexts, and
the replicability of research as findings may be influenced by terminological
differences. In two studies (N = 397, N = 622), we show that terminology does
indeed affect laypeople's perceptions of system properties (e.g., perceived
complexity) and evaluations of systems (e.g., trust). Our findings highlight
the need to be mindful when choosing terms to describe ADM systems, because
terminology can have unintended consequences, and may impact the robustness and
replicability of HCI research. Additionally, our findings indicate that
terminology can be used strategically (e.g., in communication about ADM
systems) to influence people's perceptions and evaluations of these systems.Comment: Preregistrations for the studies included in this paper are available
under https://aspredicted.org/LDC\_GSM and https://aspredicted.org/NTE\_WN
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying classification with respect to specific fairness metrics, modeled the corresponding fair classification problem as constrained optimization problems, and developed tailored algorithms to solve them. Despite this, there still remain important metrics for which there are no fair classifiers with theoretical guarantees; primarily because the resulting optimization problem is non-convex. The main contribution of this paper is a meta-algorithm for classification that can take as input a general class of fairness constraints with respect to multiple non disjoint and multi-valued sensitive attributes, and which comes with provable guarantees. In particular, our algorithm can handle non-convex "linear fractional" constraints (which includes fairness constraints such as predictive parity) for which no prior algorithm was known. Key to our results is an algorithm for a family of classification problems with convex constraints along with a reduction from classification problems with linear fractional constraints to this family. Empirically, we observe that our algorithm is fast, can achieve near-perfect fairness with respect to various fairness metrics, and the loss in accuracy due to the imposed fairness constraints is often small