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There is an increasing demand to utilize technological possibilities in the Nordic public sector. Automated decision-making (ADM) has been deployed in some areas towards that end. While ADM is associated with a range of benefits, research shows that its use, with elements of AI, also implicates risks of discrimination and unfair treatment, which has stimulated a flurry of normative guidelines. This article seeks to explore how a sample of these international high-level principled ideas on fairness translate into the specific governance of ADM in national public-sector authorities in Sweden. It does so by answering the question of how ideas about AI ethics and fairness are considered in relation to regulation on anti-discrimination in Swedish public-sector governance. By using a Scandinavian institutionalist approach to translation theory, we trace how ideas about AI governance and public-sector governance translate into state-authority practice; specifically, regarding the definition of ADM, how AI has impacted it as both discourse and technology, and the ideas of ‘ethicsʼ and ‘discriminationʼ. The results indicate that there is a variance in how different organizations understand and translate ideas about AI ethics and discrimination. These tensions need to be addressed in order to develop AI governance practices.

Objectives: Artificial intelligence (AI) is increasingly tested and integrated into breast cancer screening. Still, there are unresolved issues regarding its possible ethical, social and legal impacts. Furthermore, the perspectives of different actors are lacking. This study investigates the views of breast radiologists on AI-supported mammography screening, with a focus on attitudes, perceived benefits and risks, accountability of AI use, and potential impact on the profession.Methods:We conducted an online survey of Swedish breast radiologists. As early adopter of breast cancer screening, and digital technologies, Sweden is a particularly interesting case to study. The survey had different themes, including: attitudes and responsibilities pertaining to AI, and AI’s impact on the profession. Responses were analysed using descriptive statistics and correlation analyses. Free texts and comments were analysed using an inductive approach.Results:Overall, respondents (47/105, response rate 44.8%) were highly experienced in breast imaging and had a mixed knowledge of AI. A majority (n=38, 80.8%) were positive/somewhat positive towards integrating AI in mammography screening. Still, many considered there to be potential risks to a high/somewhat high degree (n=16, 34.1%) or were uncertain (n=16, 34.0%). Several important uncertainties were identified, such as defining liable actor(s) when AI is integrated into medical decision-making.Conclusions:Swedish breast radiologists are largely positive towards integrating AI in mammography screening, but there are significant uncertainties that need to be addressed, especially regarding risks and responsibilities. The results stress the importance of understanding actor-specific and context-specific challenges to responsible implementation of AI in healthcare.

Transparency in artificial intelligence (AI) can mean many things, but at the same time, it is currently a central focus for both scientific and regulatory attention. We seek to critically unpack this conceptual vagueness. This is particularly called for given recent focus on transparency in much of AI policy. To this end, we construct our analysis of AI Transparency into four facets. Firstly, (1) explainability (XAI) has become an expanding field in AI, which we argue needs to be complemented by more explicit focus on the (2) mediation of AI-systems functionality, as a communicated artefact. Furthermore, in the policy discourse on AI, the importance of (3) literacy is underscored. We draw from the rich literacy literature in order to show both promising and troubling consequences of this. Lastly, we unpack transparency as a form of governance, within a (4) legal framework encompassing a structure of trade-offs. By these four facets we aim to bring more clarity to the multifaceted concept of transparency in AI.

The development of artificial intelligence (AI) for medicine and health care is rapidly evolving. However, the automation, scale and data dependency of AI-driven decision-making and decision-support calls for a reassessment of principal ethical and legal norms of transparency, in the light of these novel methodologies. The quality of AI-driven health care, we argue, is depending on it. In this chapter, we provide an overview of novelties that AI in health care bring about, in order to identify key aspects potentially affecting current legal and normative (medical ethical) principles related to transparency and explainability. We develop a conceptual framework on transparency in general and explainability in particular, in relation to AI in health care. Further, we analyse principal and normative legal frameworks of patients’ rights relating to transparency and explainability – e.g., right to information, autonomy and privacy – within Sweden and the EU. Doing so, we outline main challenges in the implementation of AI in, primarily public, health care. We argue that there is an interdependency between health care quality and transparency. As transparency is not a binary state, but something that is situated in information practices, it is important to consider what kind of transparency is needed to safeguard the best possible health care. We find that meaningful and contextual transparency and explainability of AI-systems and methodologies is necessary to adhere to the basic principles of normative and legal frameworks of Swedish health care, including patient autonomy. In addition, meaningful and contextual transparency is also a prerequisite for assessing if the best possible care is given to the one most in need.

The starting point of this thesis is the managing of user-generated data in the online ecosystem and expanding development of big data. Many are worried that companies and authorities are invading their online privacy, and the lack of control by the provider of data, the citizens, can be considered one of our time’s most pressing civil rights issues. At the same time, media and information literacy become more and more important for the ability to actively be part of society. Libraries have an educational role to gain awareness of information issues, which includes privacy issues. The aim of this study is to investigate the power structures of privacy, ownership, gathering, store and use, of user-generated data, through the discourses manifested by privacy and data policies of social media services. This is done by deploying a theoretical framework of power and language with critical discourse analysis, CDA, and of mechanisms of privacy with communication privacy management, CPM, theory, complemented by a discursive understanding of power and normative manifestation in online interfaces. Methodologically the study is conducted by a critical discourse analysis of the privacy and data policies of Facebook, Twitter, Instagram, Google, Youtube, Tumblr, Pinterest, Snapchat, Reddit, Linkedin and Ello. An interface analysis is also conducted on the same social media services’ mobile phone applications and websites, pre and post login. By this, different discourses are identified. The companies claim that the users’ privacy is something valuable and important but this is not mirrored by the interfaces, where links to privacy policies mainly are placed in the bottom of pages and menus. In the policies privacy is constructed as possession, claiming to belong to, and be controlled by, the user. However, later statements contest this by manifesting great restrictions on both ownership and control. At the same time, the language of the policies is used to portray the user as res