Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem

Artificial Intelligence (AI) systems are increasingly used in high-stakes domains of our life, increasing the need to explain these decisions and to make sure that they are aligned with how we want the decision to be made. The field of Explainable AI (XAI) has emerged in response. However, it faces a significant challenge known as the disagreement problem, where multiple explanations are possible for the same AI decision or prediction. While the existence of the disagreement problem is acknowledged, the potential implications associated with this problem have not yet been widely studied. First, we provide an overview of the different strategies explanation providers could deploy to adapt the returned explanation to their benefit. We make a distinction between strategies that attack the machine learning model or underlying data to influence the explanations, and strategies that leverage the explanation phase directly. Next, we analyse several objectives and concrete scenarios the providers could have to engage in this behavior, and the potential dangerous consequences this manipulative behavior could have on society. We emphasize that it is crucial to investigate this issue now, before these methods are widely implemented, and propose some mitigation strategies

HSPB1 facilitates the formation of non-centrosomal microtubules

The remodeling capacity of microtubules (MT) is essential for their proper function. In mammals, MTs are predominantly formed at the centrosome, but can also originate from non-centrosomal sites, a process that is still poorly understood. We here show that the small heat shock protein HSPB1 plays a role in the control of non-centrosomal MT formation. The HSPB1 expression level regulates the balance between centrosomal and non-centrosomal MTs. The HSPB1 protein can be detected specifically at sites of de novo forming non-centrosomal MTs, while it is absent from the centrosomes. In addition, we show that HSPB1 binds preferentially to the lattice of newly formed MTs in vitro, suggesting that its function occurs by stabilizing MT seeds. Our findings open new avenues for the understanding of the role of HSPB1 in the development, maintenance and protection of cells with specialized non-centrosomal MT arrays

Monetizing Explainable AI: A Double-edged Sword

Algorithms used by organizations increasingly wield power in society as they decide the allocation of key resources and basic goods. In order to promote fairer, juster, and more transparent uses of such decision-making power, explainable artificial intelligence (XAI) aims to provide insights into the logic of algorithmic decision-making. Despite much research on the topic, consumer-facing applications of XAI remain rare. A central reason may be that a viable platform-based monetization strategy for this new technology has yet to be found. We introduce and describe a novel monetization strategy for fusing algorithmic explanations with programmatic advertising via an explanation platform. We claim the explanation platform represents a new, socially-impactful, and profitable form of human-algorithm interaction and estimate its potential for revenue generation in the high-risk domains of finance, hiring, and education. We then consider possible undesirable and unintended effects of monetizing XAI and simulate these scenarios using real-world credit lending data. Ultimately, we argue that monetizing XAI may be a double-edged sword: while monetization may incentivize industry adoption of XAI in a variety of consumer applications, it may also conflict with the original legal and ethical justifications for developing XAI. We conclude by discussing whether there may be ways to responsibly and democratically harness the potential of monetized XAI to provide greater consumer access to algorithmic explanations

Sole prednisolone therapy in canine meningoencephalitis of unknown etiology

Meningoencephalitis of unknown etiology (MUE) is a frequently diagnosed and often fatal disease in veterinary neurology. The aim of this retrospective study was to assess the efficacy of three different sole prednisolone treatment schedules in dogs diagnosed with MUE. The dogs were diagnosed clinically with MUE based on previously described inclusion criteria, and treated with a three-, eight- or eighteen-week-tapering prednisolone schedule. Thirty eight dogs were included in the study. Seventeen, fifteen and six dogs received the three-, eight- and eighteen-week tapering schedule, respectively. Overall, 37% of the dogs died or were euthanized because of MUE, and a significant difference in survival time was seen between the three treatment schedules. Surprisingly, the highest number of dogs that died because of MUE was seen in the eight week treatment schedule (56%), followed by the three-week (26%) and eighteen-week (0%) treatment schedule. Based on the results of this study, no definitive conclusions can be drawn regarding the ideal prednisolone dosing protocol for dogs diagnosed with MUE. However, a more aggressive and immunosuppressive treatment protocol might lead to a better outcome

Tumorbank@uza: A Collection of Tissue, Fluid Samples and Associated Data of Oncology Patients for the Use in Translational Research

Tumorbank@UZA is an academic hospital integrated biobank that collects tissue, blood and urine samples from oncology patients. We work according to a quality management system and have established SOPs for all work procedures in the biobank. Tumorbank@UZA is funded by the National Cancer Plan, an initiative from the Belgian government since 2009. Samples from our biobank are available for both academic as well as commercial researchers, through a well-established access procedure. Currently the collection consists of more than 85.000 samples of more than 8000 patients. Funding statement: Tumorbank@UZA is funded by the National Cancer Plan (initiative 27) from the Ministry of Health of the Belgian Federal Government.</p

Small heat-shock protein HSPB1 mutants stabilize microtubules in Charcot-Marie-Tooth neuropathy

Mutations in the small heat shock protein HSPB1 (HSP27) are causative for Charcot-Marie-Tooth (CMT) neuropathy. We previously showed that a subset of these mutations displays higher chaperone activity and enhanced affinity to client proteins. We hypothesized that this excessive binding property might cause the HSPB1 mutant proteins to disturb the function of proteins essential for the maintenance or survival of peripheral neurons. In the present work, we explored this hypothesis further and compared the protein complexes formed by wild-type and mutant HSPB1. Tubulin came out as the most striking differential interacting protein, with hyperactive mutants binding more strongly to both tubulin and microtubules. This anomalous binding leads to a stabilization of the microtubule network in a microtubule-associated protein-like manner as reflected by resistance to cold depolymerization, faster network recovery after nocodazole treatment, and decreased rescue and catastrophe rates of individual microtubules. In a transgenic mouse model for mutant HSPB1 that recapitulates all features of CMT, we could confirm the enhanced interaction of mutant HSPB1 with tubulin. Increased stability of the microtubule network was also clear in neurons isolated from these mice. Since neuronal cells are particularly vulnerable to disturbances in microtubule dynamics, this mechanism might explain the neuron-specific CMT phenotype caused by HSPB1 mutations

Biobanking for Viral Hepatitis Research

Introduction: Viral hepatitis is a worldwide, important health issue. The optimal management of viral hepatitis infections faces numerous challenges. In this paper, we describe how biobanking of biological samples derived from viral hepatitis patients collected both in-hospital and during community outreach screenings provides a unique collection of samples. Materials and Methods: All samples and materials were provided with a study code within the SLIMS system Study protocols and an informed consent form were approved by the Antwerp University Hospital/University of Antwerp Ethical Committee. Systematic biobanking was initiated in October 2014. Collected sample types include: (1) serum and plasma of all newly diagnosed HBV, HCV, HDV, and HEV positive patients; (2) left-over serum and plasma samples from all PCR analyses for HBV and HCV performed in the context of routine clinical care; (3) left-over liver tissue not needed for routine histological diagnosis after liver biopsy; and (4) additional virus-specific, appropriate sample types using a scientific rationale-based approach. A community outreach screening program was performed in three major Belgian cities. Serum, EDTA, Tempus Blood RNA and BD Vacutainer CPT were collected. CPT tubes were centrifuged on-site and mononuclear cells collected within 24 h. Results: Concerning community screening: 298 individuals supplied all 4 sample types. Samples were stored at −150°C and were logged in the biobank SLIMS database. Samples were used for HBV-related immunological and biomarker studies. DNA isolated from plasma samples derived from chronic HBV patients was used to investigate Single Nucleotide Polymorphism rs 1790008. Serum samples collected from chronic hepatitis C patients were used to assess the efficacy of HCV treatment. Peripheral Blood Mononuclear Cells (PBMC) isolated from chronic HBV patients and healthy controls were used for different immunological study purposes. Virus isolated from biobanked stool of a chronic hepatitis E patient was used to establish a mouse model for Hepatitis E infections, allowing further HEV virology studies. Conclusion: The establishment of a biobank with samples collected both in-hospital and during community-outreach screening resulted in a unique, continuously expanding collection of biological samples which provides an excellent platform for prompt answers to clinically and translational relevant research questions

Mutant HSPB8 causes motor neuron-specific neurite degeneration

Missense mutations (K141N and K141E) in the α-crystallin domain of the small heat shock protein HSPB8 (HSP22) cause distal hereditary motor neuropathy (distal HMN) or Charcot-Marie-Tooth neuropathy type 2L (CMT2L). The mechanism through which mutant HSPB8 leads to a specific motor neuron disease phenotype is currently unknown. To address this question, we compared the effect of mutant HSPB8 in primary neuronal and glial cell cultures. In motor neurons, expression of both HSPB8 K141N and K141E mutations clearly resulted in neurite degeneration, as manifested by a reduction in number of neurites per cell, as well as in a reduction in average length of the neurites. Furthermore, expression of the K141E (and to a lesser extent, K141N) mutation also induced spheroids in the neurites. We did not detect any signs of apoptosis in motor neurons, showing that mutant HSPB8 resulted in neurite degeneration without inducing neuronal death. While overt in motor neurons, these phenotypes were only very mildly present in sensory neurons and completely absent in cortical neurons. Also glial cells did not show an altered phenotype upon expression of mutant HSPB8. These findings show that despite the ubiquitous presence of HSPB8, only motor neurons appear to be affected by the K141N and K141E mutations which explain the predominant motor neuron phenotype in distal HMN and CMT2L

Beyond accuracy-fairness : stop evaluating bias mitigation methods solely on between-group metrics

Abstract: Artificial Intelligence (AI) finds widespread applications across various domains, sparking concerns about fairness in its deployment. While fairness in AI remains a central concern, the prevailing discourse often emphasizes outcome-based metrics without a nuanced consideration of the differential impacts within subgroups. Bias mitigation techniques do not only affect the ranking of pairs of instances across sensitive groups, but often also significantly affect the ranking of instances within these groups. Such changes are hard to explain and raise concerns regarding the validity of the intervention. Unfortunately, these effects largely remain under the radar in the accuracy-fairness evaluation framework that is usually applied. This paper challenges the prevailing metrics for assessing bias mitigation techniques, arguing that they do not take into account the changes within-groups and that the resulting prediction labels fall short of reflecting real-world scenarios. We propose a paradigm shift: initially, we should focus on generating the most precise ranking for each subgroup. Following this, individuals should be chosen from these rankings to meet both fairness standards and practical considerations