MinMax fairness: from Rawlsian Theory of Justice to solution for algorithmic bias

This paper presents an intuitive explanation about why and how Rawlsian Theory of Justice (Rawls in A theory of justice, Harvard University Press, Harvard, 1971) provides the foundations to a solution for algorithmic bias. The contribution of the paper is to discuss and show why Rawlsian ideas in their original form (e.g. the veil of...

What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms

In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or...

Algorithms and Values in Justice and Security

This article presents a conceptual investigation into the value impacts and relations of algorithms in the domain of justice and security. As a conceptual investigation, it represents one step in a value sensitive design based methodology (not incorporated here are empirical and technical investigations). Here, we explicate and analyse the...

Toward an ethics of digital government: A first discussion

In this panel, scholars discuss involving data, computational analysis, and information technology that hasthe potential to present ethical quandaries in the course of decision making related to digital government. More specifically, the presentations focus on algorithm-based decision making, personally identifiable information, and the...