No longer should we relegate the responsibilities of making difficult human and social decisions to quantitative methodologies. Clearly, it is easier to propose and corroborate a numerical method to an issue than it is to qualitatively argue for ethics. However, when we start abandoning the basic fiber of our being for numbers, whatever quality of life and technological advancement that numbers once provided becomes a mere phantom of the repercussions of surrendering the human soul.
We suffer from an inherent addiction to which there is no cure: numbers. One may argue that numbers are the very core to a myriad of academic disciplines that have catalyzed an indelible impact on the technological and economical advancement of human civilization. Indeed, without numbers, mathematics never would have been conceived, and without mathematics, we would still be living in caves, our best pastime would be carving racy rave drawings, and wars would still be fought with sticks and stones.
Amidst our increasingly computerized and information-reliant civilization, using mathematics to provide solutions to qualitative problems has become rampant. Mathematics is a powerful tool that yields wondrous results - from calculating down payment on a dream car to determining the amount of thrust required for a spacecraft. However, numbers ultimately have the potential to yield treacherous and dehumanizing results and cloud our instincts to make humane decisions. Especially for large-scale corporate and government decisions, such as aerospace ventures - which could have rippling effects on human lives - only careful and painstaking analyses of human and social considerations in an ethical context should be employed.
Aerospace risk analysis studies typically quantify a single human life to a value of approximately 4,000,000 U.S. dollars (USD). This is an attempt to quantitatively assess the risk criticality of total systems failure of an aircraft in question. After simple deduction, this value coincides with the average value of labor a typical American citizen contributes to the domestic economy during his or her lifespan. It should be noted that the current and following paragraphs assume a growth domestic product (GDP) per capita of 50,000 USD, 80 year life expectancy, and no discernible aberrations in the economic terrain.
This approximate value of human life can be misleading and easily misconstrued. For example, a risk management study of a manned space mission may conclude that utilizing a “cheaper” human life would contribute to risk mitigation. Rather than sending an American astronaut, the study might propose training and utilizing an astronaut of a third world country, such as Somalia. Because both the GDP per capita and the expected life span is nowhere near that of an average American, the contribution of an Somalian astronaut to the local economy would be calculated as a mere fraction to that of an American astronaut. As such, the risk criticality of this mission would no longer be as “severe,” while no consideration has been given to the moral dilemma of assigning different monetary values to human beings.
Another problem with quantifying the value of human life may arise in developing automatic emergency crash landing subsystems for aircraft. Consider a scenario where the subsystem deduces two available sites for a crash landing: a sparsely populated wealthy neighborhood and a populous slum. This subsystem, upon careful deliberation of quantifying the monetary value of each site, may conclude that crashing into a populous slum may inflict less damage because the high property value of a wealthy neighborhood exceeds the “value” of numerous living and breathing human beings. Indiscriminate application of mathematics to decision making involving human lives is bound to induce serious ethical fallouts.
Granted, this short study is not based entirely upon a methodology that had been derived by an author who possess limited knowledge in risk management or is bound by unsubstantiated assumptions and conditions. However, considering the enormous responsibility of risk management and decision making for real-life scenarios that high-tech sectors would have to analyze, are the possible ramifications of errors in quantitative judgment derived from such rudimentary analysis that easy to ignore?
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