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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., Author(s): Paul B. de Laat [sup.1] Author Affiliations: (1) grid.4830.f, 0000 0004 0407 1981, University of Groningen, , Groningen, Netherlands Introduction Out of concern for the unprecedented pace of AI [...]

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves ('gaming the system' in particular), the potential loss of companies' competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante., Author(s): Paul B. de Laat [sup.1] Author Affiliations: (Aff1) 0000 0004 0407 1981, grid.4830.f, University of Groningen, , Groningen, Netherlands Introduction Two decades ago, Helen Nissenbaum sounded the alert about [...]

2001, Oxford: Oxford University Press. 346 pages. In order to develop a 'broadly conceived structural approach' towards the problem of cooperation among peers, the author has conducted an in-depth case [...]

Matrix management is an alternative form of management that involves imposing a horizontal form of management over the traditional organizational hierarchy. Instead of a single central authority, power is divided among the required number of managers in charge of equally important dimensions of the business. Matirx management has been hypothesized to lead to normative conflict that engenders nonconformist conflict behavior that threatens the matrix division of power. The application of this managerial arrangement to projects is analyzed in terms of the power struggles that ensue. A project refers to a temporary activity with specific goals to be accomplished within a definite time span. A case study of a R and D department within a multinational firm using a dual (balanced) matrix is undertaken. The results indicate that each manager will attempt to encroach on the other side's authority until the antagonism gives way to covert war and cooperation.

Background The mitochondrial DNA (mDNA) 3243A>G variant is the most common pathogenic variant of the mDNA. To interpret results of clinical trials in mitochondrial disease, it is important to have a clear understanding of the natural course of disease. To obtain more insight into the disease burden and the progression of disease in carriers of the mDNA 3243 A>G variant, we followed a cohort of 151 carriers from 61 families prospectively for up to 6 years. Methods The disease severity was scored using the Newcastle Mitochondrial Disease Adult Scale (NMDAS), including SF-36 quality of life (QoL) scores. Heteroplasmy levels were measured in urinary epithelial cells (UEC), leucocytes and saliva. The progression of the disease was studied using linear mixed model analysis. Results One hundred twenty-four carriers (out of 151) were symptomatic. Four clinical groups were identified: 1) classical mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes syndrome (n=7), 2) maternally inherited diabetes deafness syndrome (n=60), 3) 'other' (n=57) and 4) dormant carriers (n=27). A yearly increase of NMDAS score of 0.47 point was measured in the total group. Heteroplasmy levels in both leucocytes and UEC were only weakly correlated with disease severity. Physical QoL declined with age. The most important determinants of QoL decline were hearing loss, speech problems, exercise intolerance, gait instability, psychiatric problems and gastrointestinal involvement. Conclusion The mDNA 3243 A>G variant causes a slowly progressive disease, with a yearly increase of NMDAS score of ~0.5 point overall with the clinical phenotype being the only determinant of disease progression. [ABSTRACT FROM AUTHOR]