My point is that we have the technology to debate over this volume of data and this debate is likely to be more productive than elevating the issues into something that fits the ancient model of sophistic debate. With modern data mining and visualization tools, the public can discuss the details of all of the relevant features of the healthcare debate. We should learn from how learning arises in machine learning from feature selection among a large set of features with diverse measurements. In analogy to neural networks, for example, the same volume of data can be presented to networks of humans each with access to tools empowering them to observe the vast richness of the data and then use debate to mimic forward propagation of accumulating conclusions and backward propagation of derivatives of errors.
The automated decision making of mandatory euthanasia of symptomatic Ebola patients may be more ethical. Not only does it prevent a huge number of new cases, but it also avoids a collapse of social order where all commerce will stop out of widespread epidemic. However, December 15 is only a month away and the latest data suggests there are still just a few thousand cases of the illness spread in multiple areas. One possibility is that the problem is so bad we can’t get reliable data, but another possibility is that we are indeed coping with the cases. The catastrophic predictions increasingly appears unlikely. If we had automated the decision-making on settled-science of just a few weeks ago, we may have ended up with more deaths than we are going to experience using the human-decision to struggle with usual health care with the available resources.