For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAI

Counterfactual explanations (CFEs) are a popular approach in explainable artificial intelligence (xAI), highlighting changes to input data necessary for altering a model's output. A CFE can either describe a scenario that is better than the factual state (upward CFE), or a scenario that is worse than the factual state (downward CFE). However, potential benefits and drawbacks of the directionality of CFEs for user behavior in xAI remain unclear. The current user study (N=161) compares the impact of CFE directionality on behavior and experience of participants tasked to extract new knowledge from an automated system based on model predictions and CFEs. Results suggest that upward CFEs provide a significant performance advantage over other forms of counterfactual feedback. Moreover, the study highlights potential benefits of mixed CFEs improving user performance compared to downward CFEs or no explanations. In line with the performance results, users' explicit knowledge of the system is statistically higher after receiving upward CFEs compared to downward comparisons. These findings imply that the alignment between explanation and task at hand, the so-called regulatory fit, may play a crucial role in determining the effectiveness of model explanations, informing future research directions in xAI. To ensure reproducible research, the entire code, underlying models and user data of this study is openly available: https://github.com/ukuhl/DirectionalAlienZooComment: 22 pages, 3 figures This work has been accepted for presentation at the 1st World Conference on eXplainable Artificial Intelligence (xAI 2023), July 26-28, 2023 - Lisbon, Portuga

Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning

IntroductionTo foster usefulness and accountability of machine learning (ML), it is essential to explain a model's decisions in addition to evaluating its performance. Accordingly, the field of explainable artificial intelligence (XAI) has resurfaced as a topic of active research, offering approaches to address the “how” and “why” of automated decision-making. Within this domain, counterfactual explanations (CFEs) have gained considerable traction as a psychologically grounded approach to generate post-hoc explanations. To do so, CFEs highlight what changes to a model's input would have changed its prediction in a particular way. However, despite the introduction of numerous CFE approaches, their usability has yet to be thoroughly validated at the human level.MethodsTo advance the field of XAI, we introduce the Alien Zoo, an engaging, web-based and game-inspired experimental framework. The Alien Zoo provides the means to evaluate usability of CFEs for gaining new knowledge from an automated system, targeting novice users in a domain-general context. As a proof of concept, we demonstrate the practical efficacy and feasibility of this approach in a user study.ResultsOur results suggest the efficacy of the Alien Zoo framework for empirically investigating aspects of counterfactual explanations in a game-type scenario and a low-knowledge domain. The proof of concept study reveals that users benefit from receiving CFEs compared to no explanation, both in terms of objective performance in the proposed iterative learning task, and subjective usability.DiscussionWith this work, we aim to equip research groups and practitioners with the means to easily run controlled and well-powered user studies to complement their otherwise often more technology-oriented work. Thus, in the interest of reproducible research, we provide the entire code, together with the underlying models and user data: https://github.com/ukuhl/IntroAlienZoo

HBV and HCV genome in peripheral blood mononuclear cells in patients undergoing chronic hemodialysis

HBV and HCV genome in peripheral blood mononuclear cells in patients undergoing chronic hemodialysis. Patients undergoing chronic hemodialysis are at risk for infection with hepatitis B virus (HBV) and hepatitis C virus (HCV). As peripheral blood mononuclear cells (PMNC) are known to be susceptible to infection of both HBV and HCV, assessment of viral genomes in those cells could uncover occult infections not detected by serologic methods or virus determination in serum. We investigated all 67 patients undergoing chronic hemodialysis at a single dialysis unit by PCR for the presence of HBV or HCV genomes in serum as well as in PMNC. None of the 67 patients was HBsAg positive or showed HBV-DNA in serum, but in 5 patients HBV-DNA in PMNC was detected as the only marker of HBV-infection; those patients were also anti-HBc negative. In 9 patients HCV-RNA was positive in serum; in 5 of those patients it was also found in PMNC. Three of these infected patients were negative for anti-HCV. One other patient had no anti-HCV or HCV-RNA in serum, but was positive for HCV-RNA in PMNC. Thus, in 6 patients (8.9%) undergoing chronic hemodialysis we found evidence of infection with HBV or HCV by detecting viral genomes in PMNC without the presence of viremia, antigenemia or specific viral antibodies in serum. The detection of viral genomes in PMNC could be useful in the positive identification of additional potentially infectious patients

A global Staphylococcus aureus proteome resource applied to the in vivo characterization of host-pathogen interactions.

Data-independent acquisition mass spectrometry promises higher performance in terms of quantification and reproducibility compared to data-dependent acquisition mass spectrometry methods. To enable high-accuracy quantification of Staphylococcus aureus proteins, we have developed a global ion library for data-independent acquisition approaches employing high-resolution time of flight or Orbitrap instruments for this human pathogen. We applied this ion library resource to investigate the time-resolved adaptation of S. aureus to the intracellular niche in human bronchial epithelial cells and in a murine pneumonia model. In epithelial cells, abundance changes for more than 400 S. aureus proteins were quantified, revealing, e.g., the precise temporal regulation of the SigB-dependent stress response and differential regulation of translation, fermentation, and amino acid biosynthesis. Using an in vivo murine pneumonia model, our data-independent acquisition quantification analysis revealed for the first time the in vivo proteome adaptation of S. aureus. From approximately 2.15 × 1

Structural characteristics and contractual terms of specialist palliative homecare in Germany

Background Multi-professional specialist palliative homecare (SPHC) teams care for palliative patients with complex symptoms. In Germany, the SPHC directive regulates care provision, but model contracts for each federal state are heterogeneous regarding staff requirements, cooperation with other healthcare providers, and financial reimbursement. The structural characteristics of SPHC teams also vary. Aim We provide a structured overview of the existing model contracts, as well as a nationwide assessment of SPHC teams and their structural characteristics. Furthermore, we explore whether these characteristics serve to find specifc patterns of SPHC team models, based on empirical data. Methods This study is part of the multi-methods research project “SAVOIR”, funded by the German Innovations Fund. Most model contracts are publicly available. Structural characteristics (e.g. number, professions, and affiliations of team members, and external cooperation) were assessed via an online database (“Wegweiser Hospiz- und Palliativversorgung”) based on voluntary information obtained from SPHC teams. All the data were updated by phone during the assessment process. Data were descriptively analysed regarding staff, cooperation requirements, and reimbursement schemes, while latent class analysis (LCA) was used to identify structural team models. Results Model contracts have heterogeneous contract partners and terms related to staff requirements (number and qualifications) and cooperation with other services. Fourteen reimbursement schemes were available, all combining different payment models. Of the 283 SPHC teams, 196 provided structural characteristics. Teams reported between one and 298 members (mean: 30.3, median: 18), mainly nurses and physicians, while 37.8% had a psychosocial professional as a team member. Most teams were composed of nurses and physicians employed in different settings; for example, staff was employed by the team, in private practices/nursing services, or in hospitals. Latent class analysis identified four structural team models, based on the team size, team members’ affiliation, and care organisation. Conclusion Both the contractual terms and teams’ structural characteristics vary substantially, and this must be considered when analysing patient data from SPHC. The identified patterns of team models can form a starting point from which to analyse different forms of care provision and their impact on care quality

Community-developed checklists for publishing images and image analysis

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However for scientists wishing to publish the obtained images and image analyses results, there are to date no unified guidelines. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here we present community-developed checklists for preparing light microscopy images and image analysis for publications. These checklists offer authors, readers, and publishers key recommendations for image formatting and annotation, color selection, data availability, and for reporting image analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby heighten the quality of microscopy data is in publications.Comment: 28 pages, 8 Figures, 3 Supplmentary Figures, Manuscript, Essential recommendations for publication of microscopy image dat

Germline Transgenic Pigs by Sleeping Beauty Transposition in Porcine Zygotes and Targeted Integration in the Pig Genome

Genetic engineering can expand the utility of pigs for modeling human diseases, and for developing advanced therapeutic approaches. However, the inefficient production of transgenic pigs represents a technological bottleneck. Here, we assessed the hyperactive Sleeping Beauty (SB100X) transposon system for enzyme-catalyzed transgene integration into the embryonic porcine genome. The components of the transposon vector system were microinjected as circular plasmids into the cytoplasm of porcine zygotes, resulting in high frequencies of transgenic fetuses and piglets. The transgenic animals showed normal development and persistent reporter gene expression for >12 months. Molecular hallmarks of transposition were confirmed by analysis of 25 genomic insertion sites. We demonstrate germ-line transmission, segregation of individual transposons, and continued, copy number-dependent transgene expression in F1-offspring. In addition, we demonstrate target-selected gene insertion into transposon-tagged genomic loci by Cre-loxP-based cassette exchange in somatic cells followed by nuclear transfer. Transposase-catalyzed transgenesis in a large mammalian species expands the arsenal of transgenic technologies for use in domestic animals and will facilitate the development of large animal models for human diseases