This State-of-the-Art review examines second language (L2) writing assessment research over the past 25 years through a framework of fairness, justice, and criticality. Recognizing the socio-political implications of assessment, the authors argue for a shift toward more equitable and socially conscious approaches. Drawing from a corpus of 869 peer-reviewed articles across leading journals, the review identifies five major themes: (1) features of writing performance, (2) rating and scoring, (3) integrated assessment, (4) teacher and learner perspectives, and (5) feedback. Each theme is reviewed for foundational findings, then critiqued through questions related to fairness and justice using a critical lens. The authors advocate for a multilingual turn in writing assessment, greater attention to teacher and student voices, and questioning dominant norms embedded in assessment practices. The review concludes with a call for future research to engage with fairness, justice, and criticality in both theory and practice, ensuring that writing assessments serve as tools for empowerment rather than exclusion.

In meritocratic societies, inequality is considered just if it reflects factors within but not outside individuals’ control. However, individuals often benefit differentially from other people’s efforts. Such passive inequality is simultaneously just and unjust by meritocratic standards, confronting meritocrats with a dilemma. We conducted an experiment with a representative US sample to investigate how people deal with this dilemma. In the experiment, impartial spectators redistribute payments between pairs of individuals. We vary whether initial payments result from luck or effort and whether spectators redistribute between individuals who worked themselves or individuals who benefited from the work of real-life friends. We find that spectators treat inequality based on the efforts of individuals’ friends as if individuals had worked themselves, and very different from inequality resulting from differential luck. This indicates that most people accept inequality if it is merited at some stage, which may explain opposition to redistributive policies.

How should the responsibility for refugees be distributed among states? While scholars have proposed various sources of responsibility to make the distribution more equitable, they have not provided guidance on how to weigh each principle within a composite scheme. This is an important problem to resolve because the principles often implicate different actors, resulting in distinct distributions of responsibility. Moreover, states are particularly able to obfuscate their level of responsibility when multiple principles exist. To remedy this problem, I specify the range of possible solutions to the weighting problem, based on the principles of liability, community, and capacity. This argument identifies the relative importance of each principle based on the stated goals of a particular framework. These goals include whether the scheme is intended to operate under ideal or non-ideal assumptions, or if it intends to optimize state or refugee interests. By focusing on how to weigh various sources of responsibility, this paper paves the way for scholars to develop determinate schemes that can identify each state’s fair share in contexts where multiple principles apply.

Many societies allocate wealth and status through competitions. These competitions may be seen as unfair if the playing field is uneven or if the competitors are of unequal strength. We run two experiments to document the extent to which people are willing to compete against others in situations with varying fairness concerns. In a between-subject experiment, we show that people’s willingness to compete is largely unaffected by the impact their choice has on the payoff of an opponent, no matter whether the opponent had a choice about whether to compete or not. In a within-subject experiment, we show that most people are willing to compete against opponents who have been exogenously disadvantaged or are known to be weaker. People who choose competition against weak or disadvantaged opponents are also more willing to give themselves an advantage by sabotaging the performance of their opponent.

Many language assessments – particularly those considered high-stakes – have the potential to significantly impact a person’s educational, employment and social opportunities, and should therefore be subject to ethical and regulatory considerations regarding their use of artificial intelligence (AI) in test design, development, delivery, and scoring. It is timely and crucial that the community of language assessment practitioners develop a comprehensive set of principles that can ensure ethical practices in their domain of practice as part of a commitment to relational accountability. In this chapter, we contextualize the debate on ethical AI in L2 assessment within global policy documents, and identify a comprehensive set of principles and considerations which pave the way for a shared discourse to underpin an ethical approach to the use of AI in language assessment. Critically, we advocate for an “ethical-by-design” approach in language assessment that promotes core ethical values, balances inherent tensions, mitigates associated risks, and promotes ethical practices.

This article proposes a framework to understand questions of fairness in EU law. It builds on the scholarly literature on the meaning and scope of this concept, to then consider its relevance to legal orders and specifically the European Union’s. Having set out an umbrella definition of fairness as a legal principle, the article applies it to a specific example: namely, the system of border procedures introduced by the new asylum and migration pact. The objective of this paper is twofold. Firstly, it aims to provide a blueprint for discussing issues of fairness in the EU beyond a specific area of law and policy. Secondly, in concretely adapting that blueprint to the specificities of the ‘case study’ analysed in the paper, it sheds light on the degree of its (un)fairness.

Workplace exclusion – often subtle and difficult to detect – significantly contributes to employee disengagement and turnover, costing US organizations over $1 trillion annually. This study examines how exclusionary behaviors (EBs) influence turnover intentions (TOIs) through disruption of psychological needs, using Rock’s SCARF model (Status, Certainty, Autonomy, Relatedness, Fairness) and self-determination theory. A two-wave survey of full-time US employees (N = 277) assessed EB, SCARF-based need satisfaction, and TOI. Partial least squares structural equation modeling revealed that EB significantly undermines all five SCARF domains, but only fairness and status mediated the EB–TOI link. Certainty, autonomy, and relatedness did not have significant effects. These findings suggest turnover risk intensifies when employees feel unfairly treated or socially devalued, rather than merely disempowered or disconnected. The study advances theoretical integration between SCARF and SDT and offers practical guidance for managers seeking to reduce attrition by fostering inclusive, respectful, and psychologically safe workplace environments.

Fairness in service robotics is a complex and multidimensional concept shaped by legal, social and technical considerations. As service robots increasingly operate in personal and professional domains, questions of fairness – ranging from legal certainty and anti-discrimination to user protection and algorithmic transparency – require systematic and interdisciplinary engagement. This paper develops a working definition of fairness tailored to the domain of service robotics based on a doctrinal analysis of how fairness is understood across different fields. It identifies four key dimensions essential to fair service robotics: (i) furthering legal certainty, (ii) preventing bias and discrimination, (iii) protecting users from exploitation and (iv) ensuring transparency and accountability. The paper explores how developers, policymakers and researchers can contribute to these goals. While fairness may resist universal definition, articulating its core components offers a foundation for guiding more equitable and trustworthy human–robot interactions.

Industrial concentration has increased in recent years with large companies consolidating their dominant positions. Concentrated markets are thought to benefit large firms as they earn elevated profits and gain political influence. Antitrust law is the main policy tool to reduce concentration. Calls to strengthen antitrust have come from the political left and the right, yet we know little about public support for such policies. We test how economic, moral, and democratic concerns influence support for antitrust. We find that the public does not respond to the consumer price benefits of antitrust but is moved by arguments invoking concerns for fairness and the importance of maintaining democratic institutions. We find that Republicans and Democrats often respond in divergent ways, with Republicans being less supportive of antitrust when informed that it could punish successful companies, whereas Democrats are more concerned about using antitrust to curb corporate influence. The findings accord with a general concern on the left for limiting business influence in politics and a concern on the right for maintaining business growth.

The adoption of AI is pervasive, often operating behind the scenes and influencing decisions without our explicit awareness. It impacts different aspects of our lives, from personalized recommendations to crucial determinations like hiring decisions or credit approvals. Yet, even to their developers, AI algorithms’ opacity raises concerns about fairness. The biases inherent in our data further complicate matters, as current AI systems often lack moral or logical judgment, relying solely on predictive outputs derived from learned data patterns. Efforts to address fairness in AI models face significant challenges, as different definitions of fairness can lead to conflicting outcomes. Despite attempts to mitigate biases and optimize fairness criteria, achieving a universal and satisfactory solution remains elusive. The multidimensional nature of fairness, with its roots in philosophy and evolving concepts in organizational justice, underscores the complexity of the task. Technology is inherently political, shaped by various societal factors and human biases. Recognizing this, stakeholders must engage in nuanced discussions about the types of fairness relevant in specific contexts and the potential trade-offs involved. Just as in other spheres of decision-making, navigating trade-offs is inevitable, requiring a flexible approach informed by diverse perspectives.

This study acknowledges that achieving fairness in AI is not about prescribing a singular definition or solution but adapting to evolving needs and values. Embracing ambiguity and tension in decision-making can lead to more inclusive outcomes. An interdisciplinary examination of application-specific and consensus-driven frameworks is adopted to consider fairness in AI. By evaluating factors such as application nuances, procedural frameworks, and stakeholder dynamics, this study demonstrates the framework’s expansive potential applicability in understanding and operationalizing fairness by the way of two illustrations.

In response to the emergence of COVID in England in 2020, the government declared a suspension of face-to-face education. To deal with the cancellation of exams, then minister of education Gavin Williamson assigned the Office of Qualifications (OfQual) with devising a method for awarding grades to students based on the standards they would have met had the examinations not been cancelled. As a response, OfQual implemented a prediction matrix based on the resources available to them: this system is wildly referred to as the OfQual algorithm. However, as the points were delivered on 13 August 2020, disputes arose leading to the cancellation of the algorithm. This paper will focus on the OfQual algorithm as a particularly relevant case to highlight the tensions around the notion of fairness in the implementation of such systems. Drawing on Science and Technology Studies, I will start by opening the black box of the OfQual algorithm. This will, in turn, allow me to identify the conflicting accounts of what is considered as fair in such system and how such accounts were inscribed within this algorithm, questioning what it means for such system to be fair in contexts marked by inequalities.