This article contains a critical reflection on eXplainable Artificial Intelligence (XAI): the idea that AI based decision-making using AI should be transparent to people faced with these decisions. We discuss the main objections to XAI. XAI focuses on a variety of explainees, with different expectations and values; XAI is not a neutral activity, but very value-sensitive; AI is dynamic and so XAI quickly becomes obsolete; many problems are ‘wicked’, which further complicates XAI. In addition, the context of XAI matters – a high level of politicization and a high perceived impact of AI-based decisions, will often result in much criticism of AI and will limit the opportunities of XAI. We also discuss a number of alternative or additional strategies – more attention to negotiated algorithms; to competing algorithms; or to value-sensitive algorithms, which may contribute to more trust in AI-based decision-making.

The police use all sorts of information to fulfil their tasks. Whereas collection and interpretation of information traditionally could only be done by humans, the emergence of ‘Big Data’ creates new opportunities and dilemmas. On the one hand, large amounts of data can be used to train algorithms. This allows them to ‘predict’ offenses such as bicycle theft, burglary, or even serious crimes such as murder and terrorist attacks. On the other hand, highly relevant questions on purpose, effectiveness, and legitimacy of the application of machine learning/‘artificial intelligence’ drown all too often in the ocean of Big Data. This is particularly problematic if such systems are used in the public sector in democracies, where the rule of law applies, and where accountability, as well as the possibility for judicial review, are guaranteed. In this article, we explore the role transparency could play in reconciling these opportunities and dilemmas. While some propose making the systems and data they use themselves transparent, we submit that an open and broad discussion on purpose and objectives should be held during the design process. This might be a more effective way of embedding ethical and legal principles in the technology, and of ensuring legitimacy during application.

‘Nudging’ has been introduced as a policy and management tool as a way to influence behaviour without limiting choice. Nudging is mainly used to influence citizens’ behaviour but can also be used to influence the behavior of healthcare professionals. Examples include posters used to improve hand-hygiene compliance, or ‘default’ options in systems to reduce excessive prescriptions of specific medication. However, using nudges raises major worries and ethical issues, also in relation to the independence of healthcare professionals. While the scientific discussion about the desirability of nudges is extensive, the voices of healthcare professionals, who are the subjects of nudges, remain unheard. In this qualitative research we explore the perceptions of nudging held by various healthcare professionals. The interviews reveal that healthcare professionals are generally unfamiliar with the concept of nudging, but they do recognize nudges in their own field of practice. Furthermore, while they are predominantly positive about nudging, they also express concerns about the pressure on their autonomy. These concerns are related to changing professionalism and regulatory pressures in healthcare.

Hoeveel nieuwe methoden en technieken van sociaalwetenschappelijk onderzoek zijn er sinds de jaren zestig van de vorige eeuw bij gekomen? Moderne ICT heeft het allemaal wel sneller en makkelijker gemaakt, maar in wezen gaat het nog steeds om dezelfde methoden, wellicht op een enkele uitzondering na. Maar misschien levert deze column reacties op die dit tegenspreken.

In 2003, the WRR suggested in the ‘Deciding on Biotechnology’ report that individuals should not be given ownership of their personal genetic information (DNA) to prevent risk selection and exclusion. Yet, that does not seem politically and ethically feasible. From that perspective, the research question of this article is how to regulate ownership and use of genetic information. Better predictability of health and longevity risk, through genetic information and thanks to new developments in geno-economics, possibly in combination with ‘big data’, makes risk selection by insurance companies attractive. That holds more specifically for the pension system. In addition to insurance for investment risk, the pension system also offers insurance for longevity risk. In itself, selection for longevity risk via premium setting is not unwarranted since the ‘bad risks’ with long life expectancy are the highly educated, so that the current implicit risk solidarity in pension insurance in The Netherlands, unlike in health insurance, brings about more inequality. But an unrestricted risk selection does raise ethical and social questions and does not fit into the new pension system in the Netherlands. That is why good and extensive legislation is needed for property rights and the utilization of genetic information of individual citizens. A similar focus on regulation is also in order for the use of DNA information of the government in criminal cases. Here the question is what additional options the government should have to use the information to solve crimes.

In the Netherlands and surrounding countries, there is reason to ask the question whether levels of segregation according to country of origin (mainly non-western) and in terms of socioeconomic position (mainly social arrears) are sufficiently high to legitimate anti-segregation policy. When will segregation become problematic? If segregation is regarded a problem, what, then, would be the best remedy? Spatial intervention? Or broader societal intervention? In this article developments and mechanisms will be discussed that lead to segregation; also political views on segregation and the framing of segregation will be scrutinized. A confrontation of knowledge, insights, visions, and framings offers material for new perspectives on residential segregation and is reason to argue for a more relaxed attitude towards segregation. We should acknowledge that the process of matching households to residential environments results in some – generally unproblematic – segregation. Only if segregation causes problems that pass certain intensity and/or a certain spatial range, non-spatial or spatial interventions are becoming a necessity. Levels of segregation are relatively moderate still. We ought to be more aware of the fact that strong negative framing actually stimulates segregation, social exclusion, division, discrimination, marginalisation, stigmatisation, fear, estrangement, and the development of first- and second-rate citizens.