Colloquium - 03/07/14
Date: March 7, 2014
Time: 3:00 p.m. to 4:00 p.m.
Location: Shelby Center Room 218
Dr. Gregory S. Reed, Modeling and Simulation, University of Alabama in Huntsville
"A Quantitative Model of Relative Ethical Violation for Use in Military Decision-Making"
As the embodiment of rational cause-and-effect, game and decision theory m. dominated the second half of the 20th century and continue to flourish today. Machine ethics, a very nascent field, involves developing machines (either as tangible hardware or as mathematical or logical models) with ethics codified as principles and procedures, in turn allowing them to consider moral "cause-and-effect" of potential actions.
A model known as the Metric of Evil (the "Metric") was first conceived by a branch of the United States Army, primarily intended for use by the Army itself. The Metric was inspired by a perceived gap in military course of action analysis: ethical dilemmas arising from the shift from conventional soldier-to-soldier combat to modern asymmetrical warfare. The Metric compared and suggested courses of action by incorporating their tangible, concrete, direct consequences---such as the expected number of international treaties broken, facilities destroyed, and combatant and civilian casualties expected to be caused by each action.
The Army consulted a team of researchers at the University of Alabama in Huntsville, led by the author of this dissertation, to refine the Metric so that it would simulate the “behavior” of ethics and military experts in evaluating courses of action. The Metric's evaluation was reduced to a single consequence---whether or not civilian casualties were involved. Using this single consequence, the Metric was able to match expert assessments. Thus, results were excellent "on paper"; however, intuition indicated that this did not meaningfully capture how ethical assessments are made.
This research involves the development of an alternate approach---the Relative Ethical Violation (REV) model. This model evaluates potential actions based upon the principles they may violate rather than the tangible consequences that they may cause. In developing the model, the author first conducted an extended review of the literature, which provided insight on ethics and psychological factors, model design and validation, and solicitation of information via survey. Then, he carefully chose a potentially meaningful set of ethical principles as input to the model. Finally, he designed and implemented the REV, the survey process through which expert assessments would be collected, and the process of validating and calibrating both the REV and the Metric so that both approaches could be compared.
Ultimately, this research found that human raters, including experts, disagreed greatly amongst themselves, which complicated the process of calibrating the model. However, amid this disagreement emerged several meaningful results. First, the REV outperformed a re-calibrated Metric, the Metric outperformed experts, experts outperformed non-experts, and non-experts outperformed simple random selection of actions. Second, human raters tended to value some principles over others; that is, no given ethical principle---even "civilian non-maleficence"---completely overshadowed the others. Third, there was a clear difference between how military experts, humanities experts, and non-experts assessed ethical dilemmas and valued certain principles. Collectively, these results indicate that the principles-based approach behind the REV can provide a clearer ethical picture than can a checklist of tangible consequences and that such an approach can provide ethical support for decision-making, and that aspects of this research can contribute to machine ethics, decision analysis, and modeling and simulation.
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