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The European General Data Protection Regulation requires the implementation of Technical and Organizational Measures (TOMs) to reduce the risk of illegitimate processing of personal data. For these measures to be effective, they must be applied correctly by employees who process personal data under the authority of their organization. However, even data processing employees often have limited knowledge of data protection policies and regulations, which increases the likelihood of misconduct and privacy breaches. To lower the likelihood of unintentional privacy breaches, TOMs must be developed with employees’ needs, capabilities, and usability requirements in mind. To reduce implementation costs and help organizations and IT engineers with the implementation, privacy patterns have proven to be effective for this purpose. In this chapter, we introduce the privacy pattern Data Cart, which specifically helps to develop TOMs for data processing employees. Based on a user-centered design approach with employees from two public organizations in Germany, we present a concept that illustrates how Privacy by Design can be effectively implemented. Organizations, IT engineers, and researchers will gain insight on how to improve the usability of privacy-compliant tools for managing personal data.
Users should always play a central role in the development of (software) solutions. The human-centered design (HCD) process in the ISO 9241-210 standard proposes a procedure for systematically involving users. However, due to its abstraction level, the HCD process provides little guidance for how it should be implemented in practice. In this chapter, we propose three concrete practical methods that enable the reader to develop usable security and privacy (USP) solutions using the HCD process. This chapter equips the reader with the procedural knowledge and recommendations to: (1) derive mental models with regard to security and privacy, (2) analyze USP needs and privacy-related requirements, and (3) collect user characteristics on privacy and structure them by user group profiles and into privacy personas. Together, these approaches help to design measures for a user-friendly implementation of security and privacy measures based on a firm understanding of the key stakeholders.
The UN Declaration on the Right to Development (UNDRTD) adopted in 1986 and the 2030 Agenda for Sustainable Development adopted in 2015 share a universal concept of development that refers both to individual and collective dimensions of prosperity and thus includes the rights of future generations.2 They thus offer a definition of the relationship between development and human rights that is very relevant for the 21st century. The core norm of the UNDRTD has been defined later as “the right of peoples and individuals to the constant improvement of their wellbeing and to a national and global enabling environment conducive to just, equitable, participatory and human-centred development respectful of all human rights”3.
Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly.
From Conclusion to Coda
(2022)
The future of work
(2021)
Driven by the exponential increase in the computational power of machines, data digitalization and scientific advancement in robotics and automation, the current wave of technological change is seemingly unprecedented in speed and scale. It transforms manufacturing and businesses making them more flexible, decentralized and efficient (Lasi et al. 2014). Even though technological change is nothing new, some argue that it is different this time. The new technologies have not only the potential to substitute labor (Nomaler and Verspagen 2018), they also change the way people work. The trend towards new forms of employment is no longer a marginal phenomenon.
The changing world poses many challenges to public policies, including social policies – among them social protection policies, which are the main focus of this handbook. Here, in this part of the handbook, we take on a number of these challenges: demographic changes and their interaction with social protection policies; roles of social protection in coping with the consequences of the COVID-19 pandemic (both topics discussed in Chapter 39 and 43 by Woodall); the challenges of globalisation (discussed in Chapter 40 by Betz) and the limitations it imposes on state sovereignty and its ability to decide on the size of publicly funded programmes, in particular social protection; challenges to labour markets and social effective protection coverage posed by automation and digitalisation of businesses (discussed in Chapter 41 by Gassmann) and, last but not least, potential roles of social protection in facilitating population’s adjustments to climate change (discussed in Chapter 42 by Malerba).
Policy analysis is the cornerstone of evidence-based policy making.1 It identifies the problems, informs programme design, supports the monitoring of policy implementation and is needed to evaluate programme impacts (Scott 2005). Rigorous and credible policy evidence is necessary to ensure the transparency and accountability of policy decisions, to secure political and public support and, hence, the allocation of financial resources. Sound policy analysis helps design effective and efficient programmes, thereby maximizing programme impact.
In recent years, the basic income grant (BIG) discourse has gained attention worldwide as a potential policy option in social protection as testified by recent public debates, ongoing pilot projects, campaigning efforts,1 policy measures during Covid-19 and the surge in academic research. A BIG refers to regular cash transfers paid to all members of society irrespective of their socio-economic status, their capacity or willingness to participate in the labour market or having to meet pre-determined conditions (Offe 2008; Van Parijs 1995, 2003; Wright 2004, 2006). Despite the recent hype around BIG, Iran is the only country worldwide with a scaled-up BIG (Tabatabai 2011, 2012). Other programmes have never gone beyond pilot programmes. This raises the question why this is the case.