Technology Assessments and Effective Risk Management: A Plea for More Reflection and Rationality

Technology Assessments and Effective Risk Management: A Plea for More Reflection and Rationality

Christian Hugo Hoffmann (Karlsruhe Institute of Technology, Germany)
Copyright: © 2022 |Pages: 10
DOI: 10.4018/IJT.306639
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Abstract

It is a common misconception in risk management, from classical purely quantitative risk assessments as being coined in banking to qualitative approaches to evaluating new technologies such as AI, and hybrids like scenario techniques in the middle, that a certain toolkit and a specific set of methods serve as a panacea for all challenges risk managers may face. Given the lurking pitfalls in this area of risk management, the call for an avoidance strategy cannot be ignored. In this article, we outline six guidelines that should be followed on the way to more decision-making competency in risk management.
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1. Introduction

“Risk” and “risk management” are central concepts as well as competencies of technology assessment (Böschen et al., 2021: p. 18, p. 27). A general skill that is sometimes lost in the context of risk management that is more or less formalized in different applications is the art of reflection in the sense of critical self-assessment.1 The challenge of what the use of reflection for risk management means for assessing technologies constitutes the subject matter of this article.

Daniel Kahneman differentiates between fast thinking and slow thinking. But both instinctive or emotional thinking or heuristics (what he calls fast thinking) and more deliberative, logical or analytical thinking (what he calls slow thinking) can be superficial, but also premature and insufficient. In our time, when analytical and fast thinking is often praised, rewarded and overrated, it might in fact turn out to be misguided or essential only because we started thinking about a topic too late (Hoffmann, 2019). For this reason, deep thinking, systemic or holistic thinking, which takes time, ought to be used and encouraged more. Occasionally, we just have to step out of the stream of direct experience and our immediate reactions to it (ibid.). Otherwise, there is a real danger that one might just end up being quick on the avoidable road to doom and disaster.2

Through deep reflection, we can expose illusions in risk management, namely that methodological rigor and quantification are guarantees for overcoming (all) challenges that risk managers face. A telling example of such a trap would be the use of a standard risk model that almost always comes with quantitative probability values, such as Delphi scenario studies (Cuhls, 2021: pp. 326-330). Such models are obtained from past data of an imperfect world, whereby in practice the idealizing assumption is often unquestioned that these data points are stochastically independent and/or normally distributed. Yet, similarity between model and depicted reality is not the same as congruence (Bernstein, 1996: p. 335). In other words, some risk analysts or “quants” neglect the question of whether the assumptions they have to establish after selecting and applying a particular model are truly satisfied in their particular real-world context (e.g. Brose et al., 2014: p. 369; MacKenzie, 2006). Despite the frequent lip service paid to more critical (Taleb et al., 2009; Power, 2007: p. 122) and holistic (Pergler & Freeman, 2008) risk management, especially after the global financial crisis, there are still fundamental and conceptual problems and many more examples of shortcomings in the use of technical risk assessment (Hoffmann, 2019). In other words, it is not generally quantitative methods in risk management per se that are in disrepute, but applications of them that are to be criticized, for example because idealizing assumptions are made as indicated above.

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