This article argues that effective corporate social responsibility (CSR) of multinational pharmaceutical companies in developing countries should reflect context, opportunity, proximity, time and impact in accordance with the social integration and ethical approaches to CSR. It proposes a CSR model expressed as CSR=COPTI+SI+E, which acknowledges access-to-medicines as a matter in the global public domain, a public choice problem and a moral responsibility issue for multinational pharmaceutical companies. This model recognises the globalisation of the principle of humanity in communities of place (...) and communities of interest as highlighted by the Global Economic Ethic Manifesto 2009 as an integral part of the responsibilities of multinational pharmaceutical companies. The model reflects a global application of the concept of disadvantaged consumer already known to some national laws. The article suggests an access-to-medicines CSR framework for pharmaceutical companies which may include pricing, patents, testing and clinical trials, research and development, joint public private initiative and appropriate use of drugs. (shrink)

Here, we provide an ethical analysis of discrimination in private insurance to guide the application of non-discriminatory algorithms for risk prediction in the insurance context. This addresses the need for ethical guidance of data-science experts, business managers, and regulators, proposing a framework of moral reasoning behind the choice of fairness goals for prediction-based decisions in the insurance domain. The reference to private insurance as a business practice is essential in our approach, because the consequences of discrimination and predictive inaccuracy in (...) underwriting are different from those of using predictive algorithms in other sectors. Here we focus on the trade-off in the extent to which one can pursue indirect non-discrimination versus predictive accuracy. The moral assessment of this trade-off is related to the context of application—to the consequences of inaccurate risk predictions in the insurance domain. (shrink)