Is being responsible good enough? Stone (1975) argued that we need corporate moral responsibility because neither law nor market is adequate to forestall harmful effects of business activities. However, it is not possible for businesses to become responsible for all forms of foreseeable, preventable harm that they produce. This is illustrated here by cases from insurance, television programming, automobiles and weapons production. Reflection on these examples leads to the formulation of a new conception of unintended harms as moral externalities of (...) business activities. Although one might argue that these (negative) moral external effects are outweighed by the desirable end products of business activities, three reasons not to accept the results of such a moral subtraction(or double effect) argument are presented. Instead, the article concludes by offering four techniques for a qualitative, ethical analysis of produced artefacts and their consequences; intended not to displace but to supplement the study of moral responsibility in business. (shrink)

Race matters in the criminal justice system. Black defendants appear to fare worse than similarly situated white defendants. Why? Implicit bias is one possibility. Researchers, using a well-known measure called the implicit association test, have found that most white Americans harbor implicit bias toward Black Americans. Do judges, who are professionally committed to egalitarian norms, hold these same implicit biases? And if so, do these biases account for racially disparate outcomes in the criminal justice system? We explored these two research (...) questions in a multi-part study involving a large sample of trial judges drawn from around the country. Our results - which are both discouraging and encouraging - raise profound issues for courts and society. We find that judges harbor the same kinds of implicit biases as others; that these biases can influence their judgment; but that given sufficient motivation, judges can compensate for the influence of these biases. (shrink)