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An increasing number of reports highlight the potential of machine learning (ML) methodologies over the conventional generalised linear model (GLM) for non-life insurance pricing. In parallel, national and international regulatory institutions are accentuating their focus on pricing fairness to quantify and mitigate algorithmic differences and discrimination. However, comprehensive studies that assess both pricing accuracy and fairness remain scarce. We propose a benchmark of the GLM against mainstream regularised linear models and tree-based ensemble models under two popular distribution modelling strategies (Poisson-gamma and Tweedie), with respect to key criteria including estimation bias, deviance, risk differentiation, competitiveness, loss ratios, discrimination and fairness. Pricing performance and fairness were assessed simultaneously on the same samples of premium estimates for GLM and ML models. The models were compared on two open-access motor insurance datasets, each with a different type of cover (fully comprehensive and third-party liability). While no single ML model outperformed across both pricing and discrimination metrics, the GLM significantly underperformed for most. The results indicate that ML may be considered a realistic and reasonable alternative to current practices. We advocate that benchmarking exercises for risk prediction models should be carried out to assess both pricing accuracy and fairness for any given portfolio.
The uneven distribution of income that emerged during China’s reform can be primarily attributed to gradual dual-track reform. Measures adopted during this period include suppressing interest rates and other factor prices to subsidize non-viable SOEs. In a market economy, adopting a comparative advantage-following (CAF) strategy can lead to fairness and efficiency in the primary distribution of income. Furthermore, through secondary distribution, the income inequality can be further reduced.
This article analyses whether and how fairness considerations affect citizens’ support of European Union (EU) policies and integration. While past literature has revealed that perceptions of procedural and substantive fairness impact on public opinion at the level of the nation state, we know less about the fairness‐support nexus when it comes to international cooperation. We here make use of the case of differentiated integration (DI) to experimentally dissect normative and utility‐oriented considerations in the evaluation of EU policies. DI as an instrument to overcome heterogeneity‐induced gridlock has been linked to both autonomy and dominance, and it can generate winners and losers in the EU. Our experiments reveal that citizens largely support DI. However, they are opposed to forms of DI which impose negative externalities on a subgroup of EU member states. This holds irrespective of the affectedness of citizens’ own member states. We take these findings as a first experimental confirmation that citizens, indeed, care about the fairness of the EU and its policies.
Jan Zielonka's Counter-Revolution: Liberal Europe in Retreat (Oxford University Press, 2018) is a furious, worried pamphlet on the challenges that European democracies are currently facing, on the apparent rise of illiberalism. This article critically reviews the book and seeks to offer a somewhat different and perhaps more optimistic picture of the current predicaments of European politics. The main point of reference in this respect is Finland, a country whose political institutions have managed, by and large, to uphold a sense of coherence in society. A commitment to participatory, equality-based, and freedom-generating institutions can indeed be seen as a primary means to counter the decline of liberalism.
In Julia Maskivker’s recent “Justice and Contribution,” she argues that, under normal circumstances, the failure to guarantee that life-sustaining workers are above the non-struggle point is not merely disrespectful and a failure of beneficence, but a violation of the norms of fair play and, as such, a “low blow.” In this article, I offer a critical reply to Maskivker. I begin by explaining her reasoning. Then I turn to critique, focusing on two key weaknesses and, in so doing, drawing out two larger lessons.
Orthodox contractualists and rule consequentialists think that, for any action, the consequences of everyone performing that action determine whether that action is permissible. For them, “what if everyone did that?” is the fundamental moral question. By making “what if everyone did that?” the fundamental question of good moral reasoning, these moral theories can easily justify prohibitions on free-riding. But it also makes them face the ideal world problem. I argue that it was a mistake for moral theorists to generalize from an objection appropriate to cases of free-riding to all of morality. In short, we should understand the question “what if everyone did that?” to give expression to one, and only one, kind of objection to one’s action—namely, that, by performing that action, one would be making an exception of oneself. If we limit the scope of “what if everyone did that?” in this way, we can justify prohibitions on free-riding while avoiding the ideal world problem.
This chapter explores the historical, legal, and regulatory landscape of employment testing bias and fairness in Canada. Canada’s history of colonization and immigration has resulted in a multicultural society. In 1984, the landmark Abella Report, and the subsequent Employment Equity Act, established key protections for historically disadvantaged groups, shaping modern employment practices. The chapter discusses the jurisdictional complexities of employment law, detailing federal and provincial regulations that prohibit discrimination based on race, sex/gender, disability, and other characteristics. Legal frameworks (e.g., the Canadian Charter of Rights and Freedoms, the Canadian Human Rights Act, and the Employment Equity Act) define bias and fairness in employment testing. Key court case decisions illustrate legal principles guiding test validity and adverse impact. We also examine professional guidelines, burden of proof requirements, regulatory oversight, and emerging challenges such as AI-driven assessments and balancing validity with diversity. The legal landscape continues to evolve, with growing emphasis on fairness, transparency, and inclusion.
The Ghanaian employment space prioritizes procedural fairness, the basis on which the Labour Act, 2003 (Act 651) and the National Labour Commission were established. Other regulations govern certification and employment testing to uphold professional standards and worker rights. For instance, the Ghana Psychology Council regulates the certification and practice of psychologists who are also mindful of other guidelines such as the American Psychological Association (APA) Standards and Society for Industrial and Organizational Psychology (SIOP) Principles. The 1992 Constitution and the Labour Act, 2003 (Act 651) of Ghana further guarantee equality, prohibit employment discrimination based on race, sex, disability, religion, and age, with specific protection for children, the disabled, and women. For instance, women in Ghana are under-represented in the workplace, in response to which the Affirmative Action Law (Act 2024) was passed, aimed at improving equality and participation of women in decision making positions. With the increasing use of artificial intelligence in employment testing worldwide, Ghana has yet to establish formal regulations for the utilization of artificial intelligence in employee selection to ensure ethical standards and data protection.
What happened when people did not pay their debts? Debts Unpaid argues that conflicts over small-scale unpaid debts were a stress test for the economic order. To ensure the wheels of petty commerce continued to turn in Mexico, everyday debtors and creditors had to believe that their interests would be protected relatively fairly when agreements soured. A resounding faith in economic justice provided the bedrock of stability necessary for the expansion of capitalism over the longue durée. Introducing the two-hundred-year period of massive economic transformation explored throughout the book, this chapter presents the text’s key historical and theoretical interventions from the late eighteenth century to the first decade of the twenty-first. As the capitalist credit economy grew, especially through modern financial institutions, ordinary people used new financial tools and navigated increasingly opaque and impersonal credit relations. This Introduction outlines the dynamics of change and the challenges and opportunities they posed for the world of small-scale debtors and creditors.
This book places the troubles of ordinary people at the centre of economic change in Mexico, arguing that conflicts over small-scale unpaid debts were a stress test for the economic and political order. Studying malfunction – what happened when contracts broke or soured – exposes the ways in which debt trouble became a driving force in the history of accumulation and justice in the modern world. This concluding chapter offers final thoughts on the book’s core proposal: that a broad sense of fairness and justice provided a bedrock of stability that allowed for massive economic transformation over a long chronological horizon.
The chapter examines bias and fairness in employment testing in Italy, comparing the public and private sectors. Public sector hiring is strictly regulated, based on transparency, equality, and meritocracy, as stated in the Constitution. Hiring occurs through public competitions with standardized exams focused on qualifications and technical skills, with growing attention to soft skills. The private sector is more flexible, adapting selection to business needs and emphasizing practical skills, experience, and cultural fit, enabling quicker hiring. Private companies often use innovative methods, including AI tools and social media screening, and value diversity and international profiles. Italian labor laws, aligned with EU directives, prohibit discrimination based on sex/gender, ethnicity, religion, sexual orientation, or disability. Employers must ensure fair, compliant selection processes. Professional guidelines stress the use of valid, unbiased tools. The rise of technology in hiring highlights the need to manage algorithmic bias, with final decisions remaining a human responsibility.
This chapter explores some of the key practices, trends and issues associated with executive reward. We begin by considering the role of executives in corporate governance as well as three influential theories of executive motivation, behaviour and reward: tournament theory, agency theory and managerial power theory. We then review the main components of executive reward, as well asrecent trends in CEO reward level and composition in a number of developed countries. Attention then turns to the various short-term and long-term incentive plans and associated techniques, including performance targets or ‘hurdles’, currently applied to executives. Next, we examine the academic research evidence and arguments regarding the effectiveness of executive reward practices, particularly the extent of the association between company performance and executive pay outcomes. Applying a multi-stakeholder perspective, the concluding section canvasses some of the wider implications of executive reward practice, as well as outlining illustrative configurations for aligning executive performance management and reward with organisational strategic priorities in the case of listed for-profit firms.
This chapter explores the legal frameworks that govern employment testing in Australia, including federal and state anti-discrimination legislation, and evaluates their impact on employment testing in the country. Overall, despite the existence of legal protections for individuals from diverse demographic groups (e.g., culturally and linguistically diverse backgrounds, sex/gender, age), judicial scrutiny of discrimination in employment testing remains limited. Practical challenges, such as difficulties in gathering evidence of discrimination, and the prospect of limited financial compensation, may discourage legal action. Moreover, statistical evidence is neither widely used nor required to demonstrate discrimination, resulting in a regulatory environment where employment testing practices are often guided more by organizational discretion and international perspectives than by legal mandates. However, as hiring technologies continue to evolve, this chapter highlights the opportunity for stronger regulatory oversight and empirical rigor to ensure employment testing remains both equitable and legally defensible.
Employment testing is routinely performed in South Africa today, but this was not always the case. Turning its back on its apartheid history of racial segregation and discrimination, South Africa has developed a progressive legal system to thwart bias and promote fairness in employment testing. This chapter explores employment-related testing in the public and private sectors, beginning with an overview of South Africa’s apartheid history, followed by a discussion of how the current legal system addresses fairness. A distinctive aspect of South African law is that preferential treatment, including lower cutoffs and within-group norming for protected groups, is not only mandated but also widely practised as the norm rather than the exception. Our review concludes that South Africa has enacted an extensive legal framework to promote equality and prevent unfair discrimination.
Belgium follows global standards in psychological assessments, and great attention is paid to issues concerning bias and fairness by legal authorities, test developers, and researchers. Anti-discrimination laws cover around nineteen protected grounds and align with European Union directives, but hiring discrimination persists. This chapter illustrates the tension between the law, test developers and researchers who promote proper test use, and practitioners who continue to rely on tools that can perpetuate bias, such as unstructured interviews and intuition-based decision-making. Despite comprehensive anti-discrimination regulations and affirmative action measures such as gender quotas, there are no legal requirements for the use of valid selection procedures in Belgium. Balancing validity and diversity is emphasized more in the public sector than the private sector. Although professional bodies offer guidelines for appropriate test use, they mainly target clinical settings rather than employment settings.
This chapter explores bias and fairness in Swedish employment testing from legal, historical, and practical perspectives. Swedish labor laws, influenced by trade unions and the welfare state, emphasize non-discrimination under the Discrimination Act. The law prohibits bias based on sex, gender identity, ethnicity, religion, disability, sexual orientation, and age, and requires preventive action. It is enforced by the Equality Ombudsman and Labour Court. Although validity evidence is not explicitly required, selection decisions should be based on a job analysis. No proof of intent is required in discrimination claims, and the burden of proof is shared. Quotas are banned, but positive action is allowed for gender balance when qualifications are equal. Psychological test certification is voluntary in Sweden; the Psychological Association offers guidelines on validity, reliability, and fairness. However, these are not mandatory, and many employers develop their own policies. International standards offer best-practice guidance for fair assessments, including for emerging artificial intelligence tools.
Selection processes in France are governed by a comprehensive legal and regulatory framework that prioritizes fairness, non-discrimination, and equal opportunity. French labor laws explicitly prohibit discrimination based on twenty-five criteria, including sex/gender, ethnicity, disability, and age. Despite these protections, disparities persist, fueling ongoing policy debates and legislative refinements. Regulatory bodies such as the Defender of Rights oversee compliance and promote unbiased hiring practices. However, implicit biases and structural barriers continue to influence employment decisions, challenging efforts to achieve true workplace equality. Employers must balance legal obligations, diversity objectives, and test validity while adapting to evolving EU regulations, such as the AI Act (2024). The introduction of diversity labels and corporate social responsibility initiatives reflects a proactive commitment to fostering inclusive workplaces. Yet enforcement challenges remain, as rising reports of workplace discrimination highlight persistent gaps. Several recommendations have been proposed to mitigate discrimination without compromising the quality and effectiveness of selection methods.
This chapter examines bias and fairness in employment testing in the Netherlands, addressing twenty key questions related to historical and cultural developments, legal frameworks, professional guidelines, and psychometric issues. Although equal treatment is a fundamental legal principle, perceptions of hiring discrimination remain widespread. The chapter explores demographic shifts that have shaped discussions on employment fairness and outlines the Dutch legal framework, focusing on the Equal Treatment Act and the role of the Netherlands Institute for Human Rights in handling discrimination complaints. It also highlights the relatively limited attention given to fairness in professional guidelines for practitioners. Furthermore, the chapter evaluates how psychological tests are assessed for bias, particularly through analyses of score differences, differential item functioning, and measurement invariance, while noting the scarcity of research on predictive bias. Emerging challenges, such as algorithmic bias, are also examined. Finally, the chapter discusses recent legislative efforts to promote fairness in employment testing, including a proposed law that was rejected in 2024.
The use of tests and assessments in employment-related decision making has the potential to benefit organizations and individuals. However, their use is frequently criticized because of their adverse potential for bias and unfairness. The saliency of and attention to these issues may also vary from one country to another. Therefore, in addition to an overview of the handbook and its objectives, the present chapter presents a synthesis of the twenty-three chapters organized around four themes pertaining to bias and unfairness in employment testing, specifically, (1) historical and/or cultural issues, (2) legal and professional guidelines and issues, (3) psychometric issues, and (4) future- and forward-looking issues. Furthermore, the theory of cultural tightness-looseness is used in an exploratory manner to gain additional insights into patterns, or the lack thereof, across countries as reported in the chapters. The patterns of associations indicated that, relative to tight countries, loose countries were generally more attune to and have in place practices and regulations addressing employment testing bias and unfairness. Finally, some thoughts and suggestions for future research are discussed.
Employment testing is a key tool for selection and placement in China’s public and private sectors. Rooted in a tradition of rigorous exams and shaped by modern workforce demands, such testing significantly influences access to job opportunities. Yet concerns about bias and fairness persist, driven by cultural norms, legal structures, and changes in the labor market. This chapter examines key issues related to bias and fairness in Chinese employment testing, exploring historical and cultural contexts, legal regulations, professional standards, and enforcement mechanisms. It also addresses measurement bias, challenges to diversity, and the growing influence of machine learning and advanced psychometrics in assessment design. By analyzing these dimensions, the chapter offers a comprehensive view of current challenges and highlights opportunities to improve equity in hiring practices. The discussion provides insights for employers, policymakers, and researchers navigating the complexities of employment testing in China.