the Catholic Church in the United States does not approve of alkaline hydrolysis as a method of final disposal of human remains. In 2011, Donald Cardinal Wuerl, Archbishop of Washington and then chairman of the Committee on Doctrine of the United States Conference of Catholic Bishops (USCCB), determined it "unnecessarily disrespectful of the human body." The Archdiocese of St. Louis explained that it was considered this way because the Church took concern with the final disposal of the liquid solution, which is typically to the sewer system. This was considered disrespectful of the sanctity of the human body. Additionally, when alkaline hydrolysis was proposed in New York state in 2012, the New York State Catholic Conference condemned the practice, stating that hydrolysis does not show sufficient respect for the teaching of the intrinsic dignity of the human body.

However, a closer examination of the model’s saliency methods revealed that the single most influential thing this model was looking for in a picture of someone’s skin was the presence of a ruler. Because medical images of cancerous lesions include a ruler for scale, the model learned to identify the presence of a ruler as a marker of malignancy, because that’s much easier than telling the difference between different kinds of lesions.

Dermatology images are the easiest to capture of all medical images, but also the least standardized. Standardization of images is difficult, even with dermoscopic images, as shown in Figure 1. Variability must be incorporated into training algorithms to create capacity to handle noisy data. This includes multiple camera angles, different orientations, blurry photos, multiple skin backgrounds, pen markings or rulers included in the photo, or variations in lighting. Otherwise, the algorithm will use features of nonstandardized photos to guide decision making. For instance, in our work, we noted that the algorithm appeared more likely to interpret images with rulers as malignant. Why? In our dataset, images with rulers were more likely to be malignant; thus the algorithm inadvertently “learned” that rulers are malignant. These biases in AI models are inherent unless specific attention is paid to address inputs with variability.