Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support

Computer Vision, and hence Artificial Intelligence-based extraction of information from images, has increasingly received attention over the last years, for instance in medical diagnostics. While the algorithms' complexity is a reason for their increased performance, it also leads to the "black box" problem, consequently decreasing trust towards AI. In this regard, "Explainable Artificial Intelligence" (XAI) allows to open that black box and to improve the degree of AI transparency. In this paper, we first discuss the theoretical impact of explainability on trust towards AI, followed by showcasing how the usage of XAI in a health-related setting can look like. More specifically, we show how XAI can be applied to understand why Computer Vision, based on deep learning, did or did not detect a disease (malaria) on image data (thin blood smear slide images). Furthermore, we investigate, how XAI can be used to compare the detection strategy of two different deep learning models often used for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron. Our empirical results show that i) the AI sometimes used questionable or irrelevant data features of an image to detect malaria (even if correctly predicted), and ii) that there may be significant discrepancies in how different deep learning models explain the same prediction. Our theoretical discussion highlights that XAI can support trust in Computer Vision systems, and AI systems in general, especially through an increased understandability and predictability

Early and Late Postnatal Myocardial and Vascular Changes in a Protein Restriction Rat Model of Intrauterine Growth Restriction

Intrauterine growth restriction (IUGR) is a risk factor for cardiovascular disease in later life. Early structural and functional changes in the cardiovascular system after IUGR may contribute to its pathogenesis. We tested the hypothesis that IUGR leads to primary myocardial and vascular alterations before the onset of hypertension. A rat IUGR model of maternal protein restriction during gestation was used. Dams were fed low protein (LP; casein 8.4%) or isocaloric normal protein diet (NP; casein 17.2%). The offspring was reduced to six males per litter. Immunohistochemical and real-time PCR analyses were performed in myocardial and vascular tissue of neonates and animals at day 70 of life. In the aortas of newborn IUGR rats expression of connective tissue growth factor (CTGF) was induced 3.2-fold. At day 70 of life, the expression of collagen I was increased 5.6-fold in aortas of IUGR rats. In the hearts of neonate IUGR rats, cell proliferation was more prominent compared to controls. At day 70 the expression of osteopontin was induced 7.2-fold. A 3- to 7-fold increase in the expression of the profibrotic cytokines TGF-β and CTGF as well as of microfibrillar matrix molecules was observed. The myocardial expression and deposition of collagens was more prominent in IUGR animals compared to controls at day 70. In the low-protein diet model, IUGR leads to changes in the expression patterns of profibrotic genes and discrete structural abnormalities of vessels and hearts in adolescence, but, with the exception of CTGF, not as early as at the time of birth. Invasive and non-invasive blood pressure measurements confirmed that IUGR rats were normotensive at the time point investigated and that the changes observed occurred independently of an increased blood pressure. Hence, altered matrix composition of the vascular wall and the myocardium may predispose IUGR animals to cardiovascular disease later in life

Transcriptional Profiling of Human Liver Identifies Sex-Biased Genes Associated with Polygenic Dyslipidemia and Coronary Artery Disease

Sex-differences in human liver gene expression were characterized on a genome-wide scale using a large liver sample collection, allowing for detection of small expression differences with high statistical power. 1,249 sex-biased genes were identified, 70% showing higher expression in females. Chromosomal bias was apparent, with female-biased genes enriched on chrX and male-biased genes enriched on chrY and chr19, where 11 male-biased zinc-finger KRAB-repressor domain genes are distributed in six clusters. Top biological functions and diseases significantly enriched in sex-biased genes include transcription, chromatin organization and modification, sexual reproduction, lipid metabolism and cardiovascular disease. Notably, sex-biased genes are enriched at loci associated with polygenic dyslipidemia and coronary artery disease in genome-wide association studies. Moreover, of the 8 sex-biased genes at these loci, 4 have been directly linked to monogenic disorders of lipid metabolism and show an expression profile in females (elevated expression of ABCA1, APOA5 and LDLR; reduced expression of LIPC) that is consistent with the lower female risk of coronary artery disease. Female-biased expression was also observed for CYP7A1, which is activated by drugs used to treat hypercholesterolemia. Several sex-biased drug-metabolizing enzyme genes were identified, including members of the CYP, UGT, GPX and ALDH families. Half of 879 mouse orthologs, including many genes of lipid metabolism and homeostasis, show growth hormone-regulated sex-biased expression in mouse liver, suggesting growth hormone might play a similar regulatory role in human liver. Finally, the evolutionary rate of protein coding regions for human-mouse orthologs, revealed by dN/dS ratio, is significantly higher for genes showing the same sex-bias in both species than for non-sex-biased genes. These findings establish that human hepatic sex differences are widespread and affect diverse cell metabolic processes, and may help explain sex differences in lipid profiles associated with sex differential risk of coronary artery disease