Accuracy evaluation of radiographers screen reading mammograms

This thesis evaluated the accuracy of radiographers screen-reading mammograms. This was undertaken as a potential solution to current radiologist workforce shortages that may contribute to delays in women receiving their screening mammogram results. This large, well-designed Australian study undertook extensive analysis and imparts evidence that even prior to any formal reading training, radiographers have good accuracy levels when screen-reading mammograms. It is expected that with formal screen-reading training these accuracy levels will further improve, such that radiographers have the potential to be one of the two screen-readers within the BreastScreen Australia program, contributing to timeliness and improved accuracy outcomes

Testing the Continuum of Delusional Beliefs: An Experimental Study Using Virtual Reality

A key problem in studying a hypothesized spectrum of severity of delusional ideation is determining that ideas are unfounded. The first objective was to use virtual reality to validate groups of individuals with low, moderate, and high levels of unfounded persecutory ideation. The second objective was to investigate, drawing upon a cognitive model of persecutory delusions, whether clinical and nonclinical paranoia are associated with similar causal factors. Three groups (low paranoia, high nonclinical paranoia, persecutory delusions) of 30 participants were recruited. Levels of paranoia were tested using virtual reality. The groups were compared on assessments of anxiety, worry, interpersonal sensitivity, depression, anomalous perceptual experiences, reasoning, and history of traumatic events. Virtual reality was found to cause no side effects. Persecutory ideation in virtual reality significantly differed across the groups. For the clear majority of the theoretical factors there were dose–response relationships with levels of paranoia. This is consistent with the idea of a spectrum of paranoia in the general population. Persecutory ideation is clearly present outside of clinical groups and there is consistency across the paranoia spectrum in associations with important theoretical variables

Millennia of legal content criteria of lies and truths: wisdom or common-sense folly?

Long before experimental psychology, religious writers, orators, and playwrights described examples of lie detection based on the verbal content of statements. Legal scholars collected evidence from individual cases and systematized them as “rules of evidence”. Some of these resemble content cues used in contemporary research, while others point to working hypotheses worth exploring. To examine their potential validity, we re-analyzed data from a quasi-experimental study of 95 perjury cases. The outcomes support the fruitfulness of this approach. Travelling back in time searching for testable ideas about content cues to truth and deception may be worthwhile

On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection

Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full automation. To realize the potential of machine learning for improving human decisions, it is important to understand how assistance from machine learning models affects human performance and human agency. In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency. We propose a spectrum between full human agency and full automation, and develop varying levels of machine assistance along the spectrum that gradually increase the influence of machine predictions. We find that without showing predicted labels, explanations alone slightly improve human performance in the end task. In comparison, human performance is greatly improved by showing predicted labels (>20% relative improvement) and can be further improved by explicitly suggesting strong machine performance. Interestingly, when predicted labels are shown, explanations of machine predictions induce a similar level of accuracy as an explicit statement of strong machine performance. Our results demonstrate a tradeoff between human performance and human agency and show that explanations of machine predictions can moderate this tradeoff.Comment: 17 pages, 19 figures, in Proceedings of ACM FAT* 2019, dataset & demo available at https://deception.machineintheloop.co

Fake review detection using time series

Today’s e-commerce is highly depended on online customers’ reviews posted in opinion sharing websites that are growing incredibly. These reviews are important not only effect on potential customers’ purchase decision but also for manufacturers and business holders to reshape and customize their products and manage competition with rivals throughout the market place. Moreover opinion mining techniques that analyze customer reviews obtained from opinion sharing websites for different purposes could not reveal accurate results for combination of spam reviews and truthful reviews in datasets. Thus employing review spam detection techniques in review websites are highly essential in order to provide reliable resources for customers, manufacturers and researchers. This study aims to detect spam reviews using time series. To achieve this, the novel proposed method detects suspicious time intervals with high number of reviews. Then a combination of three features, i.e. rating of reviews, similarity percentage of review contexts and number of other reviews written by the reviewer of current review, will be used to score each review. Finally a threshold defined for total scores assigned to reviews will be the border line between spam and genuine reviews. Evaluation of obtained results reveals that the proposed method is highly effective in distinguishing spam and non-spam reviews. Furthermore combination of all features used in this research exposed the best results. This fact represents the effectiveness of each feature