Risk Analysis Technique for the Evaluation of AI Technologies with Respect to Directly and Indirectly Affected Entities (Practitioner Track)

AI technologies are often described as being transformative to society. In fact, their impact is multifaceted, with both local and global effects which may be of a direct or indirect nature. Effects can stem from both the intended use of the technology and its unintentional side effects. Potentially affected entities include natural or juridical persons, groups of persons, as well as society as a whole, the economy and the natural environment. There are a number of different roles which characterise the relationship with a specific AI technology, including manufacturer, provider, voluntary user, involuntarily affected person, government, regulatory authority, and certification body. For each role, specific properties must be identified and evaluated for relevance, including ethics-related properties like privacy, fairness, human rights and human autonomy as well as engineering-related properties such as performance, reliability, safety and security. As for any other technology, there are identifiable lifecycle phases of the deployment of an AI technology, including specification, design, implementation, operation, maintenance and decommissioning. In this paper we will argue that all of these phases must be considered systematically in order to reveal both direct and indirect costs and effects to allow an objective judgment of a specific AI technology. In the past, costs caused by one party but incurred by another (so-called ‚externalities') have often been overlooked or deliberately obscured. Our approach is intended to help remedy this. We therefore discuss possible impact mechanisms represented by keywords such as resources, materials, energy, data, communication, transportation, employment and social interaction in order to identify possible causal paths. For the purpose of the analysis, we distinguish degrees of stakeholder involvement in order to support the identification of those causal paths which are not immediately obvious.