搜索结果：Race and justice

In this paper, we propose Intelligent Environmental Empathy (IEE) as a new driver for climate peace and justice, as an emerging issue in the age of big data. We first show that the authoritarian top-down intergovernmental cooperation, through international organizations (e.g., UNEP) for climate justice, could not overcome environmental issues and crevices so far. We elaborate on four grounds of climate injustice (i.e., teleological origin, axiological origin, formation cause, and social epistemic cause), and explain how the lack of empathy and environmental motivation on a global scale causes the failure of all the authoritarian top-down intergovernmental cooperation. Addressing all these issues requires a new button-up approach to climate peace and justice. Secondly, focusing on the intersection of AI, environmental empathy, and climate justice, we propose a model of Intelligent Environmental Empathy (IEE) for climate peace and justice at the operational level. IEE is empowered by the new power of environmental empathy (as a driver of green obligation for climate justice) and putative decentralized platform of AI (as an operative system against free riders), which Initially, impac

With the reversal of Roe v. Wade in 2022, many U.S. employers announced they would reimburse employees for abortion-related travel expenses. This action complements increasingly common employer policies subsidizing employee access to assisted reproductive technologies such as in-vitro fertilization and egg freezing. This article reflects on why employers offer these benefits and whether they enhance or undermine reproductive justice. From the employer's perspective, abortion and assisted reproductive technologies help women to plan childbearing around the demands of their jobs. Both are associated with delayed childbirth and reduced fertility, which lower the costs of motherhood to employers. However, firm subsidization of these services does not further reproductive justice because it reifies structures which incentivize women to delay childbirth and reduce fertility, and it reinforces economic and reproductive inequalities. We conclude by questioning whether reproductive justice is possible without transforming the economy so that it prioritizes care over profits.

The question of "Justice" still divides social research and moral philosophy. Several Theories of Justice and conceptual approaches compete here, and distributive justice remains a major societal controversy. From an evolutionary point of view, fair and just exchange can be nothing but "equivalent", and this makes "strict" reciprocity (merit, equity) the foundational principle of justice, both theoretically and empirically. But besides being just, justice must be effective, efficient, and communicable. Moral reasoning is a communicative strategy for resolving conflict, enhancing status, and maintaining cooperation, thereby making justice rather a social bargain and an optimization problem. Social psychology (intuitions, rules of thumb, self-bindings) can inform us when and why the two auxiliary principles equality and need are more likely to succeed than merit would. Nevertheless, both equality and need are governed by reciprocal considerations, and self-bindings help to interpret altruism as "very generalized reciprocity". The Meritocratic Principle can be implemented, and its controversy avoided, by concentrating on "non-merit", i.e., institutionally draining the wellsprings of u

This study investigates the interconnectivity of firms and Environmental Justice Organizations (EJOs) involved in socio-environmental conflicts worldwide, using data from the Environmental Justice Atlas (EJAtlas). By constructing a multilayer network that links firms, conflicts, and EJOs, the research applies social network analysis to evaluate the simultaneous involvement of these actors across multiple disputes. Both projected networks of firms and EJOs have been analysed by aggregating nodes by categories and countries to reveal structural differences. Findings reveal a stark contrast between the interconnectedness of firms and EJOs. Multinational corporations form a cohesive global network, enabling them to coordinate strategies and exert influence across regions. Conversely, EJOs are fragmented, often operating in isolated clusters with limited interconnection but forming a robust, decentralized and self-organized global network. Firms network present a strong dependence on pertaining conflict category while EJOs network does not depend on conflict category. This structural difference suggests a risk of systemic and structural coordination for firms towards exploitative expans

Same-race mentorship preference refers to mentors or mentees forming connections significantly influenced by a shared race. Although racial diversity in science has been well-studied and linked to favorable outcomes, the extent and effects of same-race mentorship preferences remain largely underexplored. Here, we analyze 465,355 mentor-mentee pairs from more than 60 research areas over the last 70 years to investigate the effect of same-race mentorship preferences on mentees' academic performance and survival. We use causal inference and statistical matching to measure same-race mentorship preferences while accounting for racial demographic variations across institutions, time periods, and research fields. Our findings reveal a pervasive same-race mentorship propensity across races, fields, and universities of varying research intensity. We observe an increase in same-race mentorship propensity over the years, further reinforced inter-generationally within a mentorship lineage. This propensity is more pronounced for minorities (Asians, Blacks, and Hispanics). Our results reveal that mentees under the supervision of mentors with high same-race propensity experience significantly low

The Advancing Data Justice Research and Practice (ADJRP) project aims to widen the lens of current thinking around data justice and to provide actionable resources that will help policymakers, practitioners, and impacted communities gain a broader understanding of what equitable, freedom-promoting, and rights-sustaining data collection, governance, and use should look like in increasingly dynamic and global data innovation ecosystems. In this integrated literature review we hope to lay the conceptual groundwork needed to support this aspiration. The introduction motivates the broadening of data justice that is undertaken by the literature review which follows. First, we address how certain limitations of the current study of data justice drive the need for a re-location of data justice research and practice. We map out the strengths and shortcomings of the contemporary state of the art and then elaborate on the challenges faced by our own effort to broaden the data justice perspective in the decolonial context. The body of the literature review covers seven thematic areas. For each theme, the ADJRP team has systematically collected and analysed key texts in order to tell the critic

This paper introduces the Environmental Justice in Technology (EJIT) Principles, a framework to help reorient technological development toward social and ecological justice and collective flourishing. In response to prevailing models of technological innovation that prioritize speed, scale, and profit while neglecting systemic injustice, the EJIT principles offer an alternative: a set of guiding values that foreground interdependence, repair, and community self-determination. Drawing inspiration from the 1991 principles of environmental justice, this framework extends their commitments into the technological domain, treating environmental justice not as a peripheral concern but as a necessary foundation for building equitable and regenerative futures. We situate the EJIT principles within the broader landscape of environmental justice, design justice, and post-growth computing, proposing them as a values infrastructure for resisting extractive defaults and envisioning technological systems that operate in reciprocity with people and the planet. In doing so, this article aims to support collective efforts to transform not only what technologies we build, but how, why, and for whom.

We use place of birth information from the Social Security Administration linked to earnings data from the Longitudinal Employer-Household Dynamics Program and detailed race and ethnicity data from the 2010 Census to study how long-term earnings differentials vary by place of birth for different self-identified race and ethnicity categories. We focus on foreign-born persons from countries that are heavily Hispanic and from countries in the Middle East and North Africa (MENA). We find substantial heterogeneity of long-term earnings differentials within country of birth, some of which will be difficult to detect when the reporting format changes from the current two-question version to the new single-question version because they depend on self-identifications that place the individual in two distinct categories within the single-question format, specifically, Hispanic and White or Black, and MENA and White or Black. We also study the USA-born children of these same immigrants. Long-term earnings differences for the 2nd generation also vary as a function of self-identified ethnicity and race in ways that changing to the single-question format could affect.

Despite inextricable ties between race and language, little work has considered race in NLP research and development. In this work, we survey 79 papers from the ACL anthology that mention race. These papers reveal various types of race-related bias in all stages of NLP model development, highlighting the need for proactive consideration of how NLP systems can uphold racial hierarchies. However, persistent gaps in research on race and NLP remain: race has been siloed as a niche topic and remains ignored in many NLP tasks; most work operationalizes race as a fixed single-dimensional variable with a ground-truth label, which risks reinforcing differences produced by historical racism; and the voices of historically marginalized people are nearly absent in NLP literature. By identifying where and how NLP literature has and has not considered race, especially in comparison to related fields, our work calls for inclusion and racial justice in NLP research practices.

Scholars investigating ethical AI, especially in high stakes settings like child welfare, have arguably been seeking ways to embed notions of justice into the design of these critical technologies. These efforts often operationalize justice at the upper and lower bounds of its continuum, defining it in terms of progressiveness or reform. Before characterizing the type of justice an AI tool should have baked in, we argue for a systematic discovery of how justice is executed by the recipient system: a method the Value Sensitive Design (VSD) framework terms Value Source analysis. The present work asks: how is justice operationalized within current child welfare administrative policy and what does it teach us about how to develop AI? We conduct a mixed-methods analysis of child welfare policy in the state of New York and find a range of functional definitions of justice (which we term principles). These principles reflect more nuanced understandings of justice across a spectrum of contexts: from established concepts like fairness and equity to less common foci like the proprietary rights of parents and children. Our work contributes to a deeper understanding of the interplay between AI

Frequent disruptions like highway constructions are common now-a-days, often impacting environmental justice communities (communities with low socio-economic status with disproportionately high and adverse human health and environmental effects) that live nearby. Based on our interactions via focus groups with the members of four environmental justice communities impacted by a major highway construction, a common concern is a sense of uncertainty about project activities and loss of social connectedness, leading to increased stress, depression, anxiety and diminished well-being. This paper addresses this concern by developing a localized social media system called PureConnect with a goal to raise the level of awareness about the project and increase social connectedness among the community members. PureConnect has been designed using active engagement with four environmental justice communities affected by a major highway construction. It has been deployed in the real world among the members of the four environmental justice communities, and a detailed analysis of the data collected from this deployment as well as surveys show that PureConnect is potentially useful in improving com

Energy justice, at the intersection of energy and societal ethics, studies the origins, quantification, and resolution of persistent and potential inequities within the energy sector, serving as a foundational pillar for societal harmony. In this review, we overview the historical and modern definitions of energy equity and frameworks of energy justice. We highlight the tools adopted to measure equity in the energy context, unveiling multifaceted inequities that permeate global energy landscapes. We discuss the limitations of prevalent metrics such as the Gini coefficient and Generalized Entropy Indices in the evaluation of energy justice concerns. Finally, we analyze publications that examined current practices and proposed improving methods towards a more equitable energy market for the society from policy, planning, and operation perspectives.

Algorithmic intermediaries govern the digital public sphere through their architectures, amplification algorithms, and moderation practices. In doing so, they shape public communication and distribute attention in ways that were previously infeasible with such subtlety, speed and scale. From misinformation and affective polarisation to hate speech and radicalisation, the many pathologies of the digital public sphere attest that they could do so better. But what ideals should they aim at? Political philosophy should be able to help, but existing theories typically assume that a healthy public sphere will spontaneously emerge if only we get the boundaries of free expression right. They offer little guidance on how to intentionally constitute the digital public sphere. In addition to these theories focused on expression, we need a further theory of communicative justice, targeted specifically at the algorithmic intermediaries that shape communication and distribute attention. This lecture argues that political philosophy urgently owes an account of how to govern communication in the digital public sphere, and introduces and defends a democratic egalitarian theory of communicative just

Most social media platforms implement content moderation to address interpersonal harms such as harassment. Content moderation relies on offender-centered, punitive approaches, e.g., bans and content removal. We consider an alternative justice framework, restorative justice, which aids victims in healing, supports offenders in repairing the harm, and engages community members in addressing the harm collectively. To assess the utility of restorative justice in addressing online harm, we interviewed 23 users from Overwatch gaming communities, including moderators, victims, and offenders; such communities are particularly susceptible to harm, with nearly three quarters of all online game players suffering from some form of online abuse. We study how the communities currently handle harm cases through the lens of restorative justice and examine their attitudes toward implementing restorative justice processes. Our analysis reveals that cultural, technical, and resource-related obstacles hinder implementation of restorative justice within the existing punitive framework despite online community needs and existing structures to support it. We discuss how current content moderation system

Access to justice remains a global challenge, with many citizens still finding it difficult to seek help from the justice system when facing legal issues. Although the internet provides abundant legal information and services, navigating complex websites, understanding legal terminology, and filling out procedural forms continue to pose barriers to accessing justice. This paper introduces the LegalWebAgent framework that employs a web agent powered by multimodal large language models to bridge the gap in access to justice for ordinary citizens. The framework combines the natural language understanding capabilities of large language models with multimodal perception, enabling a complete process from user query to concrete action. It operates in three stages: the Ask Module understands user needs through natural language processing; the Browse Module autonomously navigates webpages, interacts with page elements (including forms and calendars), and extracts information from HTML structures and webpage screenshots; the Act Module synthesizes information for users or performs direct actions like form completion and schedule booking. To evaluate its effectiveness, we designed a benchmark

The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, governance, and use. This is the consultation draft of a guide for developers and organisations, which are producing, procuring, or using data-intensive technologies.In the first section, we introduce the field of data justice, from its early discussions to more recent proposals to relocate understandings of what data justice means. This section includes a description of the six pillars of data justice around which this guidance revolves. Next, to support developers in designing, developing, and deploying responsible and equitable data-intensive and AI/ML systems, we outline the AI/ML project lifecycle through a sociotechnical lens. To support the operationalisation data justice throughout the entirety of the AI/ML lifecycle and within data innovation ecosystems, we then present five overarching principles of responsible, equitable, and trustworthy data research and innovation practices, the SAFE-D principles-Safety, Accountability, Fairn

Dynamic data race detection has emerged as a key technique for ensuring reliability of concurrent software in practice. However, dynamic approaches can often miss data races owing to nondeterminism in the thread scheduler. Predictive race detection techniques cater to this shortcoming by inferring alternate executions that may expose data races without re-executing the underlying program. More formally, the dynamic data race prediction problem asks, given a trace σof an execution of a concurrent program, can σbe correctly reordered to expose a data race? Existing state-of-the art techniques for data race prediction either do not scale to executions arising from real world concurrent software, or only expose a limited class of data races, such as those that can be exposed without reversing the order of synchronization operations. In general, exposing data races by reasoning about synchronization reversals is an intractable problem. In this work, we identify a class of data races, called Optimistic Sync(hronization)-Reversal races that can be detected in a tractable manner and often include non-trivial data races that cannot be exposed by prior tractable techniques. We also propose a

Successful attempts to predict judges' votes shed light into how legal decisions are made and, ultimately, into the behavior and evolution of the judiciary. Here, we investigate to what extent it is possible to make predictions of a justice's vote based on the other justices' votes in the same case. For our predictions, we use models and methods that have been developed to uncover hidden associations between actors in complex social networks. We show that these methods are more accurate at predicting justice's votes than forecasts made by legal experts and by algorithms that take into consideration the content of the cases. We argue that, within our framework, high predictability is a quantitative proxy for stable justice (and case) blocks, which probably reflect stable a priori attitudes toward the law. We find that U. S. Supreme Court justice votes are more predictable than one would expect from an ideal court composed of perfectly independent justices. Deviations from ideal behavior are most apparent in divided 5-4 decisions, where justice blocks seem to be most stable. Moreover, we find evidence that justice predictability decreased during the 50-year period spanning from the W

Dynamic data race detectors are indispensable for flagging concurrency errors in software, but their high runtime overhead limits their adoption. This overhead stems primarily from pervasive instrumentation of memory accesses - a significant fraction of which is redundant. We addresses this inefficiency through a static, compiler-integrated approach that identifies and eliminates redundant instrumentation, drastically reducing the runtime cost of dynamic data race detectors. We introduce a suite of interprocedural static analyses reasoning about memory access patterns, synchronization, and thread creation to eliminate instrumentation for provably race-free accesses and show that the completeness properties of the data race detector are preserved. We further observe that many inserted checks flag a race if and only if a preceding check has already flagged an equivalent race for the same memory location - albeit potentially at a different access. We characterize this notion of equivalence and show that, when limiting reporting to at least one representative for each equivalence class, a further class of redundant checks can be eliminated. We identify such accesses using a novel domin