Distinguishing genetics and eugenics on the basis of fairness.
There is concern that human applications of modern genetic technologies may lead inexorably to eugenic abuse. To prevent such abuse, it is essential to have clear, formal principles as well as algorithms for distinguishing genetics from eugenics. This work identifies essential distinctions between eugenics and genetics in the implied nature of the social contract and the importance ascribed to individual welfare relative to society. Rawls's construction of 'justice as fairness' is used as a model for how a formal systems of ethics can be used to proscribe eugenic practices. Rawls's synthesis can be applied to this problem if it is assumed that in the original condition all individuals are ignorant of their genetic constitution and unwilling to consent to social structures which may constrain their own potential. The principles of fairness applied to genetics requires that genetic interventions be directed at extending individual liberties and be applied to the greatest benefit of individuals with the least advantages. These principles are incompatible with negative eugenics which would further penalize those with genetic disadvantage. These principles limit positive eugenics to those practices which are designed to provide absolute benefit to those individuals with least advantage, are acceptable to its subjects, and further a system of basic equal liberties. This analysis also illustrates how simple deviations from first principles in Rawls's formulation could countenance eugenic applications of genetic technologies.
Ledley
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Department of Cell Biology, Baylor College of Medicine, Houston, TX 77030.
D026689:Ethical Analysis; D004992:Ethics, Medical; D005053:Eugenics; D030342:Genetic Diseases, Inborn / Q000628:therapy; D005818:Genetic Engineering; D024861:Genetic Enhancement; D005826:Genetics, Medical / Q000592:standards*; D006806:Human Rights; D006801:Humans; D018570:Risk Assessment; D012935:Social Justice; D012941:Social Responsibility; D035862:Vulnerable Populations