119 research outputs found
Runaway Feedback Loops in Predictive Policing
Predictive policing systems are increasingly used to determine how to
allocate police across a city in order to best prevent crime. Discovered crime
data (e.g., arrest counts) are used to help update the model, and the process
is repeated. Such systems have been empirically shown to be susceptible to
runaway feedback loops, where police are repeatedly sent back to the same
neighborhoods regardless of the true crime rate.
In response, we develop a mathematical model of predictive policing that
proves why this feedback loop occurs, show empirically that this model exhibits
such problems, and demonstrate how to change the inputs to a predictive
policing system (in a black-box manner) so the runaway feedback loop does not
occur, allowing the true crime rate to be learned. Our results are
quantitative: we can establish a link (in our model) between the degree to
which runaway feedback causes problems and the disparity in crime rates between
areas. Moreover, we can also demonstrate the way in which \emph{reported}
incidents of crime (those reported by residents) and \emph{discovered}
incidents of crime (i.e. those directly observed by police officers dispatched
as a result of the predictive policing algorithm) interact: in brief, while
reported incidents can attenuate the degree of runaway feedback, they cannot
entirely remove it without the interventions we suggest.Comment: Extended version accepted to the 1st Conference on Fairness,
Accountability and Transparency, 2018. Adds further treatment of reported as
well as discovered incident
Approximation algorithm for the kinetic robust K-center problem
AbstractTwo complications frequently arise in real-world applications, motion and the contamination of data by outliers. We consider a fundamental clustering problem, the k-center problem, within the context of these two issues. We are given a finite point set S of size n and an integer k. In the standard k-center problem, the objective is to compute a set of k center points to minimize the maximum distance from any point of S to its closest center, or equivalently, the smallest radius such that S can be covered by k disks of this radius. In the discrete k-center problem the disk centers are drawn from the points of S, and in the absolute k-center problem the disk centers are unrestricted.We generalize this problem in two ways. First, we assume that points are in continuous motion, and the objective is to maintain a solution over time. Second, we assume that some given robustness parameter 0<t⩽1 is given, and the objective is to compute the smallest radius such that there exist k disks of this radius that cover at least ⌈tn⌉ points of S. We present a kinetic data structure (in the KDS framework) that maintains a (3+ε)-approximation for the robust discrete k-center problem and a (4+ε)-approximation for the robust absolute k-center problem, both under the assumption that k is a constant. We also improve on a previous 8-approximation for the non-robust discrete kinetic k-center problem, for arbitrary k, and show that our data structure achieves a (4+ε)-approximation. All these results hold in any metric space of constant doubling dimension, which includes Euclidean space of constant dimension
Fairness in representation: quantifying stereotyping as a representational harm
While harms of allocation have been increasingly studied as part of the
subfield of algorithmic fairness, harms of representation have received
considerably less attention. In this paper, we formalize two notions of
stereotyping and show how they manifest in later allocative harms within the
machine learning pipeline. We also propose mitigation strategies and
demonstrate their effectiveness on synthetic datasets.Comment: 9 pages, 6 figures, Siam International Conference on Data Minin
Gaps in Information Access in Social Networks
The study of influence maximization in social networks has largely ignored
disparate effects these algorithms might have on the individuals contained in
the social network. Individuals may place a high value on receiving
information, e.g. job openings or advertisements for loans. While
well-connected individuals at the center of the network are likely to receive
the information that is being distributed through the network, poorly connected
individuals are systematically less likely to receive the information,
producing a gap in access to the information between individuals. In this work,
we study how best to spread information in a social network while minimizing
this access gap. We propose to use the maximin social welfare function as an
objective function, where we maximize the minimum probability of receiving the
information under an intervention. We prove that in this setting this welfare
function constrains the access gap whereas maximizing the expected number of
nodes reached does not. We also investigate the difficulties of using the
maximin, and present hardness results and analysis for standard greedy
strategies. Finally, we investigate practical ways of optimizing for the
maximin, and give empirical evidence that a simple greedy-based strategy works
well in practice.Comment: Accepted at The Web Conference 201
Reducing Access Disparities in Networks using Edge Augmentation
In social networks, a node's position is a form of \it{social capital}.
Better-positioned members not only benefit from (faster) access to diverse
information, but innately have more potential influence on information spread.
Structural biases often arise from network formation, and can lead to
significant disparities in information access based on position. Further,
processes such as link recommendation can exacerbate this inequality by relying
on network structure to augment connectivity.
We argue that one can understand and quantify this social capital through the
lens of information flow in the network. We consider the setting where all
nodes may be sources of distinct information, and a node's (dis)advantage deems
its ability to access all information available on the network. We introduce
three new measures of advantage (broadcast, influence, and control), which are
quantified in terms of position in the network using \it{access signatures} --
vectors that represent a node's ability to share information. We then consider
the problem of improving equity by making interventions to increase the access
of the least-advantaged nodes. We argue that edge augmentation is most
appropriate for mitigating bias in the network structure, and frame a budgeted
intervention problem for maximizing minimum pairwise access.
Finally, we propose heuristic strategies for selecting edge augmentations and
empirically evaluate their performance on a corpus of real-world social
networks. We demonstrate that a small number of interventions significantly
increase the broadcast measure of access for the least-advantaged nodes (over 5
times more than random), and also improve the minimum influence. Additional
analysis shows that these interventions can also dramatically shrink the gap in
advantage between nodes (over \%82) and reduce disparities between their access
signatures
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