Abdominal aortic aneurysms (AAAs) are an important cardiovascular disease, but the genetic and environmental risk factors, which contribute to individual's risk to develop an aneurysm, are poorly understood. Histologically, AAAs are characterized by signs of chronic inflammation, destructive remodeling of the extracellular matrix, and depletion of vascular smooth muscle cells. We hypothesized that genes involved in these events could harbor changes that make individuals more susceptible to developing aneurysms. This study identified significant genetic associations between DNA sequence changes in tissue inhibitor of metalloproteinase 1 (TIMP1), TIMP3, matrix metalloproteinase 10 (MMP10) and elastin (ELN) genes, and AAA. The results will require confirmation using an independent set of samples. After replication it is possible that these sequence changes in combination with other risk factors could be used in the future to identify individuals who are at increased risk for developing an AAA.

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Background and Purpose-Genomewide

1 Machine learning models often make predictions that bias against certain subgroups of input data. When undetected, machine learning biases can constitute significant financial and ethical implications. Semi-automated tools that involve humans in the loop could facilitate bias detection. Yet, little is known about the considerations involved in their design. In this paper, we report on an interview study with 11 machine learning practitioners for investigating the needs surrounding semi-automated bias detection tools. Based on the findings, we highlight four considerations in designing to guide system designers who aim to create future tools for bias detection.