There might be something in universal InfoDesign as well.
“Classification is an intellectual act, performed as often in the name of theology as in the name of science. The classifications proposed here are an attempt to impose useful differences onto a field of infinite examples. In that sense, it is analogous to classification schemes in the biological sciences. In his explanation of contemporary evolution theory, David Quammen describes how the biologists Robert Whittaker and Lynn Margulis recognized the limits of imposing order on the phenomenon we study.”
Paul Kahn a.k.a. /paulkahn | @pauldavidkahn ~ Nightingale ★
Always keep your principles.
“Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest-profile sets of ethical principles for AI. We assess whether these principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principles for ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts.”
Luciano Floridi and Josh Cowls ~ Harvard Data Science Review Issue 1 ★
Quants are always a bit difficult for qualts. But, there’s no other choice than to marry them.
“As a researcher, I want to understand how technology changes people’s lives, not wade through a bunch of data. Like a lot of people, I think in stories rather than numbers; in the tangible rather than the abstract. So, when I made it a goal to understand all of the data about the experiences people have with technology – not just the kinds of data that I was comfortable with—there were some big gaps in my knowledge.”
(Pamela Pavliscak a.k.a. @paminthelab ~ UXmatters) ★