694,970 research outputs found
Raising Algorithm Bias Awareness Among Computer Science Students Through Library and Computer Science Instruction
We are a computer science professor and two librarians who work closely with computer science students. In this paper, we outline the development of an introductory algorithm bias instruction session. As part of our lesson development, we analyzed the results of a survey we conducted of computer science students at three universities on their perceptions about search-engine and big-data algorithms. We examined whether an information literacy component focused on algorithmic bias was beneficial to offer to students in the computational sciences and designed an instructional prototype. We studied qualitative data, including feedback from students and colleagues on our initial instruction module to create the next two modules. We found that students’ reception to the subject of algorithm bias can range from defensive and unaccepting to open and accepting of the existence of such bias. Since the topic ultimately deals with issues of racial, gender-based, and other discrimination, a multidisciplinary approach is needed when teaching about algorithm bias. Our assertion is that librarians have a role in partnering with computer science instructors to ensure that students who major in computer science, who will be the primary creators of algorithms as they enter the workforce, can develop an early awareness and understanding of bias in information systems. Further, when the students receive such training, the automated systems they generate will produce more fair outcomes. Our pedagogy incorporates insights from computer science, library science, medical ethics, and critical theory. The aim of our algorithm bias instruction is to help computer science students recognize and mitigate the systematic marginalization of groups within the current technological environment
Men Also Do Laundry: Multi-Attribute Bias Amplification
As computer vision systems become more widely deployed, there is increasing
concern from both the research community and the public that these systems are
not only reproducing but amplifying harmful social biases. The phenomenon of
bias amplification, which is the focus of this work, refers to models
amplifying inherent training set biases at test time. Existing metrics measure
bias amplification with respect to single annotated attributes (e.g.,
). However, several visual datasets consist of images with
multiple attribute annotations. We show models can learn to exploit
correlations with respect to multiple attributes (e.g., {,
}), which are not accounted for by current metrics. In
addition, we show current metrics can give the erroneous impression that
minimal or no bias amplification has occurred as they involve aggregating over
positive and negative values. Further, these metrics lack a clear desired
value, making them difficult to interpret. To address these shortcomings, we
propose a new metric: Multi-Attribute Bias Amplification. We validate our
proposed metric through an analysis of gender bias amplification on the COCO
and imSitu datasets. Finally, we benchmark bias mitigation methods using our
proposed metric, suggesting possible avenues for future bias mitigationComment: Accepted at ICML 202
Adaptive model for recommendation of news
Most news recommender systems try to identify users' interests and news'
attributes and use them to obtain recommendations. Here we propose an adaptive
model which combines similarities in users' rating patterns with epidemic-like
spreading of news on an evolving network. We study the model by computer
agent-based simulations, measure its performance and discuss its robustness
against bias and malicious behavior. Subject to the approval fraction of news
recommended, the proposed model outperforms the widely adopted recommendation
of news according to their absolute or relative popularity. This model provides
a general social mechanism for recommender systems and may find its
applications also in other types of recommendation.Comment: 6 pages, 6 figure
Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection
Face recognition (FR) systems continue to spread in our daily lives with an
increasing demand for higher explainability and interpretability of FR systems
that are mainly based on deep learning. While bias across demographic groups in
FR systems has already been studied, the bias of explainability tools has not
yet been investigated. As such tools aim at steering further development and
enabling a better understanding of computer vision problems, the possible
existence of bias in their outcome can lead to a chain of biased decisions. In
this paper, we explore the existence of bias in the outcome of explainability
tools by investigating the use case of face presentation attack detection. By
utilizing two different explainability tools on models with different levels of
bias, we investigate the bias in the outcome of such tools. Our study shows
that these tools show clear signs of gender bias in the quality of their
explanations
Yukawa potentials in systems with partial periodic boundary conditions II : Lekner sums for quasi-two dimensional systems
Yukawa potentials may be long ranged when the Debye screening length is
large. In computer simulations, such long ranged potentials have to be taken
into account with convenient algorithms to avoid systematic bias in the
sampling of the phase space. Recently, we have provided Ewald sums for
quasi-two dimensional systems with Yukawa interaction potentials [M. Mazars,
{\it J. Chem. Phys.}, {\bf 126}, 056101 (2007) and M. Mazars, {\it Mol. Phys.},
Paper I]. Sometimes, Lekner sums are used as an alternative to Ewald sums for
Coulomb systems. In the present work, we derive the Lekner sums for quasi-two
dimensional systems with Yukawa interaction potentials and we give some
numerical tests for pratical implementations. The main result of this paper is
to outline that Lekner sums cannot be considered as an alternative to Ewald
sums for Yukawa potentials. As a conclusion to this work : Lekner sums should
not be used for quasi-two dimensional systems with Yukawa interaction
potentials.Comment: 25 pages, 5 figures and 1 tabl
Computer-assisted versus oral-and-written dietary history taking for diabetes mellitus
Background: Diabetes is a chronic illness characterised by insulin resistance or deficiency, resulting in elevated glycosylated haemoglobin A1c (HbA1c) levels. Diet and adherence to dietary advice is associated with lower HbA1c levels and control of disease. Dietary history may be an effective clinical tool for diabetes management and has traditionally been taken by oral-and-written methods, although it can also be collected using computer-assisted history taking systems (CAHTS). Although CAHTS were first described in the 1960s, there remains uncertainty about the impact of these methods on dietary history collection, clinical care and patient outcomes such as quality of life.
Objectives: To assess the effects of computer-assisted versus oral-and-written dietary history taking on patient outcomes for diabetes mellitus.
Search methods: We searched The Cochrane Library (issue 6, 2011), MEDLINE (January 1985 to June 2011), EMBASE (January 1980 to June 2011) and CINAHL (January 1981 to June 2011). Reference lists of obtained articles were also pursued further and no limits were imposed on languages and publication status.
Selection criteria: Randomised controlled trials of computer-assisted versus oral-and-written history taking in patients with diabetes mellitus.
Data collection and analysis: Two authors independently scanned the title and abstract of retrieved articles. Potentially relevant articles were investigated as full text. Studies that met the inclusion criteria were abstracted for relevant population and intervention characteristics with any disagreements resolved by discussion, or by a third party. Risk of bias was similarly assessed independently.
Main results: Of the 2991 studies retrieved, only one study with 38 study participants compared the two methods of history taking over a total of eight weeks. The authors found that as patients became increasingly familiar with using CAHTS, the correlation between patients' food records and computer assessments improved. Reported fat intake decreased in the control group and increased when queried by the computer. The effect of the intervention on the management of diabetes mellitus and blood glucose levels was not reported. Risk of bias was considered moderate for this study.
Authors' conclusions: Based on one small study judged to be of moderate risk of bias, we tentatively conclude that CAHTS may be well received by study participants and potentially offer time saving in practice. However, more robust studies with larger sample sizes are needed to confirm these. We cannot draw on any conclusions in relation to any other clinical outcomes at this stage
Quantum Monte Carlo simulation
Contemporary scientific studies often rely on the understanding of complex
quantum systems via computer simulation. This paper initiates the statistical
study of quantum simulation and proposes a Monte Carlo method for estimating
analytically intractable quantities. We derive the bias and variance for the
proposed Monte Carlo quantum simulation estimator and establish the asymptotic
theory for the estimator. The theory is used to design a computational scheme
for minimizing the mean square error of the estimator.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS406 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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Why Are People's Decisions Sometimes Worse with Computer Support?
In many applications of computerised decision support, a recognised source of undesired outcomes is operators' apparent over-reliance on automation. For instance, an operator may fail to react to a potentially dangerous situation because a computer fails to generate an alarm. However, the very use of terms like "over-reliance" betrays possible misunderstandings of these phenomena and their causes, which may lead to ineffective corrective action (e.g. training or procedures that do not counteract all the causes of the apparently "over-reliant" behaviour). We review relevant literature in the area of "automation bias" and describe the diverse mechanisms that may be involved in human errors when using computer support. We discuss these mechanisms, with reference to errors of omission when using "alerting systems", with the help of examples of novel counterintuitive findings we obtained from a case study in a health care application, as well as other examples from the literature
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