Abstract
The rapid and dynamic nature of digital transformation challenges companies that wish to develop and deploy novel digital technologies. Like other actors faced with this transformation, companies need to find robust ways to ethically guide their innovations and business decisions. Digital ethics has recently featured in a plethora of both practical corporate guidelines and compilations of high-level principles, but there remains a gap concerning the development of sound ethical guidance in specific business contexts. As a multinational science and technology company faced with a broad range of digital ventures and associated ethical challenges, Merck KGaA has laid the foundations for bridging this gap by developing a Code of Digital Ethics (CoDE) tailored for this context. Following a comprehensive analysis of existing digital ethics guidelines, we used a reconstructive social research approach to identify 20 relevant principles and derive a code designed as a multi-purpose tool. Versatility was prioritised by defining non-prescriptive guidelines that are open to different perspectives and thus well-suited for operationalisation for varied business purposes. We also chose a clear nested structure that highlights the relationships between five core and fifteen subsidiary principles as well as the different levels of reference—data and algorithmic systems—to which they apply. The CoDE will serve Merck KGaA and its new Digital Ethics Advisory Panel to guide ethical reflection, evaluation and decision-making across the full spectrum of digital developments encountered and undertaken by the company whilst also offering an opportunity to increase transparency for external partners, and thus trust.
Highlights
The digital transformation has resulted in drastic change at every level of society (Floridi 2014)
We considered recent1 literature that fulfilled three criteria: First, all documents included had to refer to the ethical handling of data and/or artificial intelligence (AI), machine learning or other aspects of algorithmic systems
According to our final definition, “In the context of digital solutions, explainability means that users can understand how the results of algorithm-based processes are achieved [,] that users know the reason for their decision wherever they are directly or indirectly affected by automated decisions [and that] users receive sufficient information about the models used and the architecture of the algorithmic system”
Summary
The digital transformation has resulted in drastic change at every level of society (Floridi 2014). Building upon the foundations laid by computer and information ethics since the second half of the Twentieth Century (Moor 1985; Bynum 2018; Floridi 2013), the young
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