This system of governance inevitably results in over criminalizing because it equates so many unrelated violations of law into a single category of a crime requiring a prison sentence. We may need these laws, and for argument’s sake I’ll grant that prison sentences are a valid form of punishment. I question the need for all of these laws to require prison penalties. I question the wisdom of equating any violation of such a wide range of laws to be a single category: an imprisonable convict.
Although we lack the technology to adopt a comprehensive government by data, it may be worthwhile to start thinking about government by data as a political movement. I propose a new political party named Dedomenocratic Party. Unlike the other political parties that base their platform on specific goals, the Dedomenocratic Party’s platform is based on process of selecting short-lived rules based solely on most current data and algorithms. The other parties assert some claim about deep truth of human nature or proper government. In contrast, the Dedomenocratic Party claims such truths or understanding is irrelevant to the project of government when we have sufficient data and algorithms to make decisions automatically.
Although the types of punishments that may occur in government by data are similar to older traditional punishments we witness in parts of the world today, a future system of automated government by data can avoid some of the abuses. The government by data encourages forgiveness and clemency to minimize the need for punishment. This government requires any punishment to be quick in order to return the person back to the community as soon as possible (such as within a day). The punishment decision is automated using algorithms and data to assure consistent and fair treatment of cases. The algorithms and data for the punishment decision are to assess what minimum level of punishment for a particular person will be sufficient to encourage that cooperation instead of having some broader goal of extracting justice. The public will have access to evaluate and criticize the data and algorithms that impose the punishments, just like they will interact with the data and algorithms that generate new rules. In government by data, the automatic decision-making based on data and algorithms includes the decision making for assigning punishments to people who disobey the rules.
This form of government does not have a goal of justice for the sake of justice. The goals are to make the best use of resources to exploit the latest opportunities or to mitigate potential hazards. In its most ideal form, government by data will make and select rules solely on available data and algorithms. Inherent in the monotheistic religions is that the God does not interfere with man’s free will. Consequently, God does not provide data. Government by data is a very complete separation of church and state.
Government by data and urgency will operate very different from the present governments. The focus shifts to immediate issues that can be informed by recent data. Unlike the present government with accumulating perpetual laws, this new form of government exclusively enacts short-lived rules that get updated when new data becomes available or get retired when priorities change. Similarly, the government views the population in terms of future possibilities instead of past performance.
Governing by data dispenses with the notions of causality, justice, accountability. In their places, we can have more comprehensible rules based on current data available to everyone. The recent advancement and maturity of big data technologies makes possible this new form of government. Taking the most advantage of the most recent information requires us to dismiss old decisions, including decisions involving punishment. There is good reason to expect that such nimble government can be much more beneficial to society. The frequency of making new decisions on newer information will make the consequences of bad decisions more tolerable. The result will be a society with fewer rules and where all of the rules have immediate relevancy that everyone can verify by checking the data.
Within a big data context, we need to obtain a more complete picture of current stories within a population in order to provide the opportunity to discover new hypothesis by comparing and contrasting different stories or story-elements. Relying only on voluntary story-telling or rapport-based journalism is not sufficient. Stories will remain that people will strongly protect as secrets. Part of that protection is to avoid talking at all. Coercion can compel them to talk and even if they succeed in protecting their secrets, their attempts to construct a compelling fabrication will require supplying credible details drawn from their experiences or education. The individual stories and their elements may be very unreliable data, but when combined we may observe useful patterns to suggest new hypotheses that we can test by seeking out new sources of information.