107 research outputs found
Analysis of top to bottom- shuffles
A deck of cards is shuffled by repeatedly moving the top card to one of
the bottom positions uniformly at random. We give upper and lower bounds
on the total variation mixing time for this shuffle as ranges from a
constant to . We also consider a symmetric variant of this shuffle in which
at each step either the top card is randomly inserted into the bottom
positions or a random card from the bottom positions is moved to the top.
For this reversible shuffle we derive bounds on the mixing time. Finally,
we transfer mixing time estimates for the above shuffles to the lazy top to
bottom- walks that move with probability 1/2 at each step.Comment: Published at http://dx.doi.org/10.1214/10505160500000062 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
The nascent field of fair machine learning aims to ensure that decisions
guided by algorithms are equitable. Over the last several years, three formal
definitions of fairness have gained prominence: (1) anti-classification,
meaning that protected attributes---like race, gender, and their proxies---are
not explicitly used to make decisions; (2) classification parity, meaning that
common measures of predictive performance (e.g., false positive and false
negative rates) are equal across groups defined by the protected attributes;
and (3) calibration, meaning that conditional on risk estimates, outcomes are
independent of protected attributes. Here we show that all three of these
fairness definitions suffer from significant statistical limitations. Requiring
anti-classification or classification parity can, perversely, harm the very
groups they were designed to protect; and calibration, though generally
desirable, provides little guarantee that decisions are equitable. In contrast
to these formal fairness criteria, we argue that it is often preferable to
treat similarly risky people similarly, based on the most statistically
accurate estimates of risk that one can produce. Such a strategy, while not
universally applicable, often aligns well with policy objectives; notably, this
strategy will typically violate both anti-classification and classification
parity. In practice, it requires significant effort to construct suitable risk
estimates. One must carefully define and measure the targets of prediction to
avoid retrenching biases in the data. But, importantly, one cannot generally
address these difficulties by requiring that algorithms satisfy popular
mathematical formalizations of fairness. By highlighting these challenges in
the foundation of fair machine learning, we hope to help researchers and
practitioners productively advance the area
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The limits of human predictions of recidivism.
Dressel and Farid recently found that laypeople were as accurate as statistical algorithms in predicting whether a defendant would reoffend, casting doubt on the value of risk assessment tools in the criminal justice system. We report the results of a replication and extension of Dressel and Farid's experiment. Under conditions similar to the original study, we found nearly identical results, with humans and algorithms performing comparably. However, algorithms beat humans in the three other datasets we examined. The performance gap between humans and algorithms was particularly pronounced when, in a departure from the original study, participants were not provided with immediate feedback on the accuracy of their responses. Algorithms also outperformed humans when the information provided for predictions included an enriched (versus restricted) set of risk factors. These results suggest that algorithms can outperform human predictions of recidivism in ecologically valid settings
Herbivore: A Scalable and Efficient Protocol for Anonymous Communication
Anonymity is increasingly important for networked applications
amidst concerns over censorship and privacy. In this paper, we describe Herbivore, a peer-to-peer, scalable, tamper-resilient communication system that provides provable anonymity and privacy. Building on dining cryptographer networks, Herbivore scales by partitioning the network into anonymizing cliques. Adversaries able to monitor all network traffic cannot deduce the identity of a sender or receiver beyond an anonymizing clique. In addition to strong anonymity, Herbivore simultaneously provides high efficiency and scalability, distinguishing it from other anonymous communication protocols. Performance measurements from a prototype implementation show that the system can achieve high bandwidths and low latencies when deployed over the Internet
Popular Support for Balancing Equity and Efficiency in Resource Allocation: A Case Study in Online Advertising to Increase Welfare Program Awareness
Algorithmically optimizing the provision of limited resources is commonplace
across domains from healthcare to lending. Optimization can lead to efficient
resource allocation, but, if deployed without additional scrutiny, can also
exacerbate inequality. Little is known about popular preferences regarding
acceptable efficiency-equity trade-offs, making it difficult to design
algorithms that are responsive to community needs and desires. Here we examine
this trade-off and concomitant preferences in the context of GetCalFresh, an
online service that streamlines the application process for California's
Supplementary Nutrition Assistance Program (SNAP, formerly known as food
stamps). GetCalFresh runs online advertisements to raise awareness of their
multilingual SNAP application service. We first demonstrate that when ads are
optimized to garner the most enrollments per dollar, a disproportionately small
number of Spanish speakers enroll due to relatively higher costs of non-English
language advertising. Embedding these results in a survey (N = 1,532) of a
diverse set of Americans, we find broad popular support for valuing equity in
addition to efficiency: respondents generally preferred reducing total
enrollments to facilitate increased enrollment of Spanish speakers. These
results buttress recent calls to reevaluate the efficiency-centric paradigm
popular in algorithmic resource allocation.Comment: This paper will be presented at the 2023 International Conference on
Web and Social Media (ICWSM'23
Reevaluating the Role of Race and Ethnicity in Diabetes Screening
There is active debate over whether to consider patient race and ethnicity
when estimating disease risk. By accounting for race and ethnicity, it is
possible to improve the accuracy of risk predictions, but there is concern that
their use may encourage a racialized view of medicine. In diabetes risk models,
despite substantial gains in statistical accuracy from using race and
ethnicity, the gains in clinical utility are surprisingly modest. These modest
clinical gains stem from two empirical patterns: first, the vast majority of
individuals receive the same screening recommendation regardless of whether
race or ethnicity are included in risk models; and second, for those who do
receive different screening recommendations, the difference in utility between
screening and not screening is relatively small. Our results are based on broad
statistical principles, and so are likely to generalize to many other
risk-based clinical decisions.Comment: 11 pages, 4 figure
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