People’s trust in algorithmic decision-making systems in health : a comparison between ADA health and IBM Watson

Algorithmic decision-making systems (ADMs) support an ever-growing number of decision- making processes. We conducted an online survey study in [omitted] (N=1082) to understand how lay people perceive and trust health ADMs. Inspired by the traditional Ability, Benevolence, and Integrity trust model (Mayer et al., 1995), this study investigated how trust is constructed in health ADMs. In addition, we investigated how trust construction differs between ADA Health (a chatbot that suggests a diagnosis) and IBM Watson (a system that suggest treatments for cancer). Our results show that perceptions of accuracy, fairness and control significantly differ between both contexts. Accuracy and fairness play the biggest role in predicting trust for both ADMs. Control plays a smaller, yet significant, role. Interestingly, control and accuracy play a bigger role in explaining trust for ADA Health than for IBM Watson. Moreover, goal appropriateness and AI concern proof to be good predictors for accuracy, fairness, and control. These results exemplify the importance to take broader contextual, algorithmic, and case specific characteristics into account when investigating trust construction in ADMs

Data quality strategies in gas metal arc welding production for machine learning applications

Amidst the advent of Industry 4.0, the manufacturing industry is exploring AI methodologies and other data-driven approaches for the understanding and optimization of gas metal arc welding (GMAW) processes. Various data sources such as process data logs and image data are available to the users of modern welding systems. However, to make good use of the data for machine learning, data sets of different quality and information density have to be fused. In this paper, we propose strategies for improving the dataset quality of time series process data and image data from the GMAW process. We explore resampling strategies to ensure the harmonization of time series data. Additionally, ideas for improving image quality from welding process cameras are discussed

Bioinformatics pipeline for processing single-cell data

Single-cell proteomics can offer valuable insights into dynamic cellular interactions, but identifying proteins at this level is challenging due to their low abundance. In this chapter, we present a state-of-the-art bioinformatics pipeline for single-cell proteomics that combines the search engine Sage (via SearchGUI), identification rescoring with MS2Rescore, quantification through FlashLFQ, and differential expression analysis using MSqRob2. MS2Rescore leverages LC-MS/MS behavior predictors, such as MS2PIP and DeepLC, to recalibrate scores with Percolator or mokapot. Combining these tools into a unified pipeline, this approach improves the detection of low-abundance peptides, resulting in increased identifications while maintaining stringent FDR thresholds

Intact neural responding to hearing one's own name in children with autism

Diminished responding to one's own name is one of the strongest and earliest predictors of autism. However, research on the neural correlates of this response in autism is scarce. Here we investigate neural responses to hearing the own name in school-aged children with and without autism. Thirty-four children with autism and 33 without autism (ages 7-13) were presented with three categories of names (own name, close other's name and unknown other name) as task-irrelevant deviant stimuli in an auditory oddball paradigm, while EEG was recorded. In line with previous findings, parietal P3 amplitudes for the own name were enhanced compared with a close other's name. Older children showed a stronger self-specific effect than younger children. However, this self-preferential effect was not different between groups, despite the fact that parents of children with autism reported significantly less own-name responsiveness in daily life. Neither the N1 component or SON negativity showed self-specific effects. In school-aged children, only the parietal P3 component, and not the N1 or SON negativity, appears to be enhanced for the own name as compared to a close other's name. Age seems to have an effect on the own name modulation of the P3 amplitude, which may explain the relatively small overall effect size. Against expectations, groups did not differ on this self-specific effect. Further research into neural and behavioral responses to hearing one's own name in autism, across different age groups, is warranted