Whereas in most studies conducted previously the effect of automation bias has been investigated in terms of an instantaneous decision, this study is aimed at quantifying its duration.
The study evaluated two speech recognition systems, Wav2vec2 and Whisper, for potential biases for Dutch speakers.Results obtained by evaluating on the JASMIN corpus revealed biases against non-native speakers, children, and the elderly,with (slightly) better performance for women. The study emphasizes the need for ASR systems to handle variations in speakingin order to reach equal performance among all users.
Reporting of research findings is often selective. This threatens the validity of the published body of knowledge if the decision to report depends on the nature of the results. The evidence derived from studies on causes and mechanisms underlying selective reporting may help to avoid or reduce reporting bias. Such research should be guided by a theoretical framework of possible causal pathways that lead to reporting bias. We build upon a classification of determinants of selective reporting that we recently developed in a systematic review of the topic. The resulting theoretical framework features four clusters of causes. There are two clusters of necessary causes: (A) motivations (e.g. a preference for particular findings) and (B) means (e.g. a flexible study design). These two combined represent a sufficient cause for reporting bias to occur. The framework also features two clusters of component causes: (C) conflicts and balancing of interests referring to the individual or the team, and (D) pressures from science and society. The component causes may modify the effect of the necessary causes or may lead to reporting bias mediated through the necessary causes. Our theoretical framework is meant to inspire further research and to create awareness among researchers and end-users of research about reporting bias and its causes.
![Causes of reporting bias: a theoretical framework [version 2; peer review: 2 approved]](https://publinova-harvester-content-prod.s3.amazonaws.com/thumbnails/files/previews/pdf/20240419021423483168.68f_a863_4e94_84cd_cf2a63540cea_18310_jenny_van_der_stee-thumbnail-400x300.png)
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Een persisterende infectie met hoog-risico humaan papillomavirus (hrHPV) is de belangrijkste factor voor de ontwikkeling van afwijkingen in de baarmoederhals en het ontstaan van baarmoederhalskanker. Ongeveer 80% van de vrouwen loopt in haar leven een HPV infectie op, toch is het risico op kanker relatief laag. hrHPV infectie is noodzakelijk maar niet de enige factor die bijdraagt aan de ontwikkeling van baarmoederhalskanker. Er zijn aanwijzingen dat vaginoom afwijkingen, zoals een disbalans van micro-organismen in de vagina, cofactoren kunnen zijn voor een persisterende hrHPV infectie. Een eerste analyse van een kleine 1000 uitstrijken (waarvan de helft hrHPV-positief) die in het HPV expertisecentrum van het Jeroen Bosch ziekenhuis getest werden op aanwezigheid van een beperkt aantal verschillende bacteriesoorten liet zien dat dat in het hrHPV-positieve cohort statistisch significant meer vaginoom afwijkingen voorkwamen dan in het hrHPV-negatieve cohort. Dit motiveert ons een haalbaarheidsonderzoek te starten met als doel te bepalen of het vaginoom (het geheel van bacteriën, schimmels en virussen in de vagina) kan dienen als triagemarker voor een persisterende hrHPV infectie, die kan leiden tot baarmoederhalskanker. In dit onderzoek willen we het gehele vaginoom in kaart te brengen van een subgroep van vrouwen met en zonder hrHPV infectie. Sequencing technologieën zijn bij uitstek geschikt voor het in kaart brengen van een diversiteit aan micro-organismen op basis van hun genoom, maar kunnen arbeidsintensief zijn en genereren complexe data waardoor er een IT structuur voor (beveiligde) opslag en analyse nodig is. Samen met het HPV expertisecentrum en MKB partners willen we onderzoeken welke sequencing methode de meest betrouwbare resultaten geeft en het best bruikbaar is in een diagnostische laboratoriumsetting. Daarnaast zal onderzocht worden hoe we om moeten gaan met de gegenereerde data en opslag daarvan.
