Date of Award
2013
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science Department of Modeling and Simulation
Committee Chair
Mikel D. Petty
Committee Member
John P. Ballenger
Committee Member
Nicholaos J. Jones
Committee Member
Harry S. Delugach
Committee Member
Anthony W. Morris
Abstract
As the embodiment of rational cause-and-effect, game and decision theory methods 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 ethical "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, the model's calibrated weights indicated that all chosen principles factored into human raters' assessments to some degree. Collectively, these results indicate that a quantitative model can capture the ethical tradeoffs made by experts, that the principles-based approach behind the REV can provide a clearer ethical picture than can a checklist of tangible consequences, that such an approach can provide ethical support for decision-making, and that aspects of this research can contribute greatly to the fields of machine ethics, philosophy of science, decision analysis, and modeling and simulation.
Recommended Citation
Reed, Gregory S., "A quantitative model of relative ethical violation for use in military decision-making" (2013). Dissertations. 13.
https://louis.uah.edu/uah-dissertations/13