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AbstractThe term 'responsible AI' has been coined to denote AI that is fair and non-biased, transparent and explainable, secure and safe, privacy-proof, accountable, and to the benefit of mankind. Since 2016, a great many organizations have pledged allegiance to such principles. Amongst them are 24 AI companies that did so by posting a commitment of the kind on their website and/or by joining the 'Partnership on AI'. By means of a comprehensive web search, two questions are addressed by this study: (1) Did the signatory companies actually try to implement these principles in practice, and if so, how? (2) What are their views on the role of other societal actors in steering AI towards the stated principles (the issue of regulation)? It is concluded that some three of the largest amongst them have carried out valuable steps towards implementation, in particular by developing and open sourcing new software tools. To them, charges of mere 'ethics washing' do not apply. Moreover, some 10 companies from both the USA and Europe have publicly endorsed the position that apart from self-regulation, AI is in urgent need of governmental regulation. They mostly advocate focussing regulation on high-risk applications of AI, a policy which to them represents the sensible middle course between laissez-faire on the one hand and outright bans on technologies on the other. The future shaping of standards, ethical codes, and laws as a result of these regulatory efforts remains, of course, to be determined.

The term 'responsible AI' has been coined to denote AI that is fair and non-biased, transparent and explainable, secure and safe, privacy-proof, accountable, and to the benefit of mankind. Since 2016, a great many organizations have pledged allegiance to such principles. Amongst them are 24 AI companies that did so by posting a commitment of the kind on their website and/or by joining the 'Partnership on AI'. By means of a comprehensive web search, two questions are addressed by this study: (1) Did the signatory companies actually try to implement these principles in practice, and if so, how? (2) What are their views on the role of other societal actors in steering AI towards the stated principles (the issue of regulation)? It is concluded that some three of the largest amongst them have carried out valuable steps towards implementation, in particular by developing and open sourcing new software tools. To them, charges of mere 'ethics washing' do not apply. Moreover, some 10 companies from both the USA and Europe have publicly endorsed the position that apart from self-regulation, AI is in urgent need of governmental regulation. They mostly advocate focussing regulation on high-risk applications of AI, a policy which to them represents the sensible middle course between laissez-faire on the one hand and outright bans on technologies on the other. The future shaping of standards, ethical codes, and laws as a result of these regulatory efforts remains, of course, to be determined.

Big Data are increasingly used in machine learning in order to create predictive models. How are predictive practices that use such models to be situated? In the field of surveillance studies many of its practitioners assert that "governance by discipline" has given way to "governance by risk". The individual is dissolved into his/her constituent data and no longer addressed. I argue that, on the contrary, in most of the contexts where predictive modelling is used, it constitutes Foucauldian discipline. Compliance to a norm occupies centre stage; suspected deviants are subjected to close attention-as the precursor of possible sanctions. The predictive modelling involved uses personal data from both the focal institution and elsewhere ("Polypanopticon"). As a result, the individual re-emerges as the focus of scrutiny. Subsequently, small excursions into Foucauldian texts discuss his discourses on the creation of the "delinquent", and on the governmental approach to smallpox epidemics. It is shown that his insights only mildly resemble prediction as based on machine learning; several conceptual steps had to be taken for modern machine learning to evolve. Finally, the options available to those subjected to predictive disciplining are discussed: to what extent can they comply, question, or resist? Through a discussion of the concepts of transparency and "gaming the system" I conclude that our predicament is gloomy, in a Kafkaesque fashion.

Matrix management of projects, it is argued, may generate normative conflict, prompting project and functional managers to engage in nonconformist conflict behavior which challenges the division of authority in matrix. An analysis of such struggles has been lacking yet. The present study fills this gap. Distinguishing between retreatism, ritualism, manipulation and rebellion, forms and outcomes of such power struggles are explored in a case study of the R&D establishment of a multinational corporation. A so-called balanced matrix was applied, in which power between both kinds of matrix managers is equally balanced. Analysis shows, how each party tried to shift the balance of power toward its side -mostly in vain however; in the end, the "hot" war between contending parties gave way to a mixture of "cold" war and willing cooperation. From the case data, organizational features other than matrix design as such are identified, that seem to affect the chances of mutual conflict escalating. Finally, it is conjectured, that types of matrix other than the balanced matrix are less susceptible to such intense power struggles, as the balance of power is not nearly equal.