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The authors address the problem of observability analysis and enhancement for missiles equipped with strapdown seekers, especially considering the line‐of‐sight angle‐only measurements and the constraints on impact angle and terminal acceleration. First, based on the generalised polynomial chaos expansion, a novel data‐driven observability analysis method is proposed to quantitatively analyse the observability of the radome aberration estimation system in real time, including the observability condition, the observability degree of each state and the effect of stochastic noises. The results show that under conventional proportional navigation guidance, the radome aberration estimation system is weakly observable with line‐of‐sight angle‐only measurements. To address this problem, a novel manoeuvre algorithm, the observability‐enhanced guidance law, is introduced to enhance the system observability within specified constraints, thereby improving the estimation performance. This guidance law incorporates a bias term to meet terminal constraints, and optimises the navigation gain using the proposed observability indices. Simulations are conducted to verify the performance of the proposed guidance law in conjunction with a target state estimator. [ABSTRACT FROM AUTHOR]

We identify and present Brazil's most common behavioral and heuristic biases in judicial decision-making. Through bibliographic and specific cases, we notice the occurrence of the representativeness heuristic, availability heuristic, anchoring heuristic (anchoring effect), confirmation bias, and affect heuristic bias in Brazilian judicial decisions. We also present the current state of Brazilian legislation and its amendments that aim at impartiality in the production, the assessment of evidence, and the judge's conviction. Finally, we present the suggestions and initiatives that aim to mitigate biases and heuristics in judicial decision-making in Brazil, especially with awareness techniques, the replacement of judges by algorithms, and the review of judicial decisions by collegiate bodies. [ABSTRACT FROM AUTHOR]

Automated employment decision tools use machine learning, artificial intelligence, predictive analytics, and other data-driven approaches to enhance candidate experiences and streamline employment related decision-making, allowing human resources to be concentrated where they are needed most. However, the use of these tools without appropriate safeguards has resulted in a number of high-profile scandals in recent years, particularly in regard to bias. Accordingly, lawmakers have started to propose laws that require bias audits of automated employment decision tools to examine their outputs for subgroup differences. The first of its kind was New York City Local Law 144, but other US states have since followed suit. In this paper, we examine the concerns about the effectiveness of this and other similar laws, including the suitability of metrics, the scope of the law, and low levels of compliance. We conclude that despite the law being a good initial first step towards greater transparency around automated employment decision tools and reducing bias, examining outcomes alone is not sufficient to prevent bias elsewhere in the tool. Moreover, effective bias prevention will require a multidisciplinary approach that combines expertise in IO psychology, law, and computer science to develop appropriate metrics and maximize the enforceability of such laws. [ABSTRACT FROM AUTHOR]

Are corruption trials that involve the highest ranks in the public sphere and large private companies biased against some groups? Existing research predominantly focuses on corruption prosecutions of politicians, leaving unresolved the extent to which judges apply differential treatment when convicting and sentencing the political class compared to other defendants, including those in the private sector. To address this gap, we investigate judicial bias within Brazil's famous “Operação Lava Jato,” the largest corruption investigation carried out in history. Leveraging an original database that traces the trajectory of the universe of the 3154 cases of Lava Jato, we show that judges' sentencing decisions were not governed by a partisan logic. Instead, judges were more inclined to impose longer prison times and higher fines to elected politicians when compared to all other defendants, particularly those from the private sector. We interpret these findings as evidence of antipolitical class bias. [ABSTRACT FROM AUTHOR]

The article compares the use of facial recognition technology by law enforcement between the U.S. and Europe and the racial biases of facial recognition against people of color. Topics discussed include bias originating from codes and datasets, implications of FRT for human rights, right to privacy, right of assembly and free speech, national and regional regulations and framework to address discrimination, and prospective measures and legislation on law enforcement's use of facial recognition.

The article analyzes the links between risk factors and colonial oppression within dangerous offender proceedings and state accountability for colonial oppression. Topics discussed include possibilities and challenges in addressing colonial oppression in sentencing, risk assessment and management in the Canadian criminal justice system, and state accountability for potential cross-cultural bias in risk assessment tools and for lack of access to programs in prison.

The article explores the intersection of in-group bias and mercy in extreme situations, drawing on examples from Hurricane Katrina's aftermath and literary works like Cormac McCarthy's "The Road." It argues that both legal professionals and individuals facing crisis situations are prone to favoring their own group, which can hinder the exercise of mercy towards outsiders or strangers.

The article focuses on the editorial team's reflection on potential biases and underrepresented topics in the AEQ journal. Topics include an analysis of the journal's emphasis on certain groups and methods, a comparison with similar journals to identify gaps, and suggestions for future inclusivity and representation in AEQ's publications.

Whichever way you spin the record, rap music and courtrooms don’t mix. On one side, rap records are well known for their unapologetic lyrical composition, often expressing a blatant disregard for legal institutions and authorities. On the other, court records reflect a Van Gogh’s ear for rap music, frequently allowing rap lyrics—but not similar lyrics from other genres—to be used as criminal evidence against the defendants who authored them. Over the last thirty years, this immiscibility has engendered a legal landscape where prosecutors wield rap lyrics as potent instruments for criminal prosecution. In such cases, color-blind courts neglect that rap is a genre originating from and predominantly performed by Black musicians, hence becoming complicit in catalyzing this targeted weaponization of rap music, chilling Black artistic expression, and effectively stripping the genre of its creative license. Though hardly a novel challenge for rap artists, the pending criminal conspiracy case against Jeffrey Williams, professionally known as Young Thug, prompts a renewed sense of urgency in drawing a bright line between fictional rap lyrics and probative criminal evidence. Building from notable scholarship in this field, this Note situates the law’s disproportionate maltreatment of rap music within the broader canvas of systemic racism to propose avenues for how the law may evolve to accommodate the persistent intersections between rap music and the judicial system. By delineating clear parameters for the admissibility of rap lyrics as evidence to address troubling bias in juror perceptions, this framework seeks to find a balance between protecting Black artistic expression and preserving the integrity of criminal proceedings. [ABSTRACT FROM AUTHOR]

The article analyzes how the Court of Criminal Appeals of Tennessee incorrectly allowed rap lyrics evidence to prove the character of the accused in the case State v. Williams. Topics discussed include the Strong Nexus test of the direct connection between lyrics and the crime in question, stigmas and prejudicial effects of rap music when allowed into evidence, and potential of the Williams test under the Federal Rules of Evidence to create prejudicial bias in juries.

This paper aims to carry out empirical analysis of the viability of large language models (LLMs), specifically ChatGPT, in simulating the common law system and facilitating its evolutionary processes. Drawing on the Theory of Rules Evolution, it is understood that common law generates efficient rules by natural selection through constant litigation. Nonetheless, this evolutionary mechanism faces several hindrances. The process of change is typically slow and incremental. Courts often have to wait for a case that's deemed 'appropriate' before they can change the law, leading to extended delays. Additionally, courts frequently struggle to make efficient decisions due to limited information. Other factors that decelerate the creation of efficient rules include judicial bias, unequal distribution of resources among litigating parties, and the diminishing presence of a competitive legal order. This study first assesses ChatGPT's capability to embrace the essence of the common law system, namely the doctrine of stare decisis. We then assess its potential to overcome the hindrances in common law development and promote efficient rules. Through a series of meticulously designed hypothetical cases set in a virtual jurisdiction called the "Matrix Kingdom," we observed that ChatGPT mimic the functions of a common law court by citing, following, and distinguishing its own precedents, but it accomplishes this with significantly fewer resources and in less time. This implies that humans can introduce hypothetical legal situations, enabling LLMs to replicate the natural selection process observed in the common law system but with a significantly accelerated pace. Given that LLMs are trained with diverse information sources, not just the factual contexts of cases, they could potentially lower the informational constraints in decision-making. As such, LLMs might significantly contribute to the evolutionary processes of common law development. However, it is important to remain cautious of certain limitations, such as the potential for AI Hallucination and inherent biases in LLMs, which require careful consideration and management. [ABSTRACT FROM AUTHOR]

Algorithms are often tainted with bias that could negatively affect subjects' lives in many aspects. But while policymakers and scholars tend to focus their attention on methods to Jix the bias within the algorithm or its output, they neglect a crucial piece of the puzzle: when companies discover bias and fix it in the algorithmic output, they do so behind the scenes. While such a move might debias the output, society remains in the dark about its existence or how it might have affected the outcome. Sociely thus misses a unique opportunity to study social and cognitive biases that shape our civil rights and liberties today-and might become more important in the future with the rise of automation. We thus propose a new mechanism to aid in the study of bias and its elimination, not only in algorithms but also in society. We call it Bias Notification Duty (BND). This article lays the groundwork for this new social-legal responsibility by offering a dilfevent viewpoint on bias, algorithms, and society. We propose that the state imposes a duty (BND) on companies and their employees to, when bias is found, provide a notice to a selected governing body that will further study it and, upon evaluation of its source and impact, will notify those who are both directly and indirectly affected by it accordingly. Affected parties may include other companies that could be subjected to similar bias, individuals that were potentially harmed by it, researchers in relevant fields, and society as a whole. Upon setting the proposal, we examine the rationales behind it and discuss its hurdles. With such acknowledgment, we argue that BNDs are an important legal tool that should be used to aid in de-biasing our society, especially if the world will eventually be algorithmically driven and bias will be even more obscured by technological companies. [ABSTRACT FROM AUTHOR]

This Paper addresses the use of artificial intelligence ("AI") in the judicial system, specifically its application in making predictions that influence judicial decisions, and the legal and ethical concerns stemming from AI biases. First, this Paper will explore the various roles that AI can play in this domain, with a particular emphasis on AI in risk assessment and recidivism prediction. This involves analyzing data related to a crime and a defendant to generate predictions. A significant concern in this area revolves around biases. While biases in criminal justice have long been recognized as a critical issue, there has been optimism that AI could mitigate these biases. However, this may not necessarily be the case. There is potential for risk assessment AI to enhance sentencing accuracy and reduce human error and bias. However, there is apprehension that it could perpetuate or exacerbate existing biases and even undermine fundamental principles of fairness in the justice system. Several factors contribute to this risk, including biased coding and incomplete and inaccurate training and testing datasets. Additionally, the presence of dynamic algorithms and the lack of transparency and explainability make it challenging to identify and address biases effectively. The forthcoming European regulation on artificial intelligence, known as the AI Act ("AIA"), aims to mitigate biases. However, it is acknowledged by experts that biases cannot be completely eradicated, like biases that are inherent in human decisions. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has become increasingly prevalent in various domains. However, ML algorithms sometimes give unfair outcomes and discrimination against certain groups. Thereby, bias occurs when our results produce a decision that is systematically incorrect. At various phases of the ML pipeline, such as data collection, pre-processing, model selection, and evaluation, these biases appear. Bias reduction methods for ML have been suggested using a variety of techniques. By changing the data or the model itself, adding more fairness constraints, or both, these methods try to lessen bias. The best technique relies on the particular context and application because each technique has advantages and disadvantages. Therefore, in this paper, we present a comprehensive survey of bias mitigation techniques in machine learning (ML) with a focus on in-depth exploration of methods, including adversarial training. We examine the diverse types of bias that can afflict ML systems, elucidate current research trends, and address future challenges. Our discussion encompasses a detailed analysis of pre-processing, in-processing, and post-processing methods, including their respective pros and cons. Moreover, we go beyond qualitative assessments by quantifying the strategies for bias reduction and providing empirical evidence and performance metrics. This paper serves as an invaluable resource for researchers, practitioners, and policymakers seeking to navigate the intricate landscape of bias in ML, offering both a profound understanding of the issue and actionable insights for responsible and effective bias mitigation. [ABSTRACT FROM AUTHOR]

The article delves into the challenges facing federal courts in terms of their legitimacy, especially in light of the public's declining trust in the judiciary, and explores the complexities surrounding the law of preliminary injunctions. It highlights the varied standards applied by federal courts when deciding preliminary injunction questions, potential biases affecting decision-making, and proposes a modified threshold test to evaluate preliminary injunctions more effectively.

An introduction is presented in which the editor discusses articles in the issue on topics including international law textbooks and biases, the invisible college of post-Soviet Eurasian international lawyers and neo-liberal global economic governance.

The article discusses a grassroot strategy to restore neutrality in civil procedure and address the discriminatory effects of judicial discretion in the U.S. Topics discussed include the history of the 1938 Federal Rules of Civil Procedure, court cases that have increased judicial discretion in civil procedure, and the impact of explicit and implicit bias in judicial decision-making.

The rapid advancement of Artificial Intelligence (AI) technology has catalyzed significant transformations across various sectors, notably impacting employment dynamics and raising concerns about bias and fairness. This qualitative research aims to delve into user perceptions regarding AI technology and its ethical implications on employment dynamics and bias. Through in-depth interviews and thematic analysis, this study explores the multifaceted dimensions of how users perceive AI's impact on employment patterns, potential biases embedded within AI systems, and the ethical considerations surrounding its implementation. Findings suggest a complex interplay between technological advancements, societal values, and ethical dilemmas, underscoring the need for comprehensive policies and frameworks to address emerging challenges. By shedding light on user perspectives, this research contributes to a deeper understanding of the ethical implications of AI technology on employment dynamics and bias. [ABSTRACT FROM AUTHOR]

The article explains how conflicts of interest are addressed in equal employment opportunity (EEO) complaint processing in the U.S. Army. Topics discussed include legal framework for EEO discrimination complaints under Title VII of the Civil Rights Act, role of the Army labor counselor in the EEO complaint process, conflict between Army regulation and the EEO-Management Directive 110, and the proposed optimal standard to reduce actual or perceived bias in complaint processing.

The article examines developments in mediator liability in case law, codes of conduct and custom. Topics discussed include duty of mediators to respect confidentiality, disclose conflicts of interest and report sexual abuse of children, coercive mediation to settle disputes, coercion rooted in racism, disputant incapacity due to duress, racial discrimination and retaliation, numerical mistakes in settlement agreements such as estate entitlement, incorrect advice by mediators and mediator bias.

Peremptory challenges in jury selection are being used in a biased and discriminatory manner. The Batson v. Kentucky safeguards are not working as intended and have not resolved the problem of jury bias. States now need to decide: will they follow Arizona's bold lead in 2022 and abolish peremptory challenges, or will they follow Washington and try to improve on Batson? This Article presents a compelling argument for abolishing peremptory strikes in jury selections. The authors trace the historical development of peremptory challenges, highlighting their evolution from a mechanism to ensure impartiality to their current use in shaping a biased jury. After examining Washington's "neutral observer" standard and Arizona's complete elimination of peremptory challenges, the authors advocate for a shift to the English model--where "the first twelve in the box" become jurors. The Article includes a helpful chart showing the direction in which key states are leaning. It raises thought-provoking questions about implicit bias, the limitations of current methods, and the advantages of a simplified approach to jury selection. [ABSTRACT FROM AUTHOR]

When can scientific findings from experimental psychology be confidently applied to legal issues? When applications have clear limits, do legal commentators readily acknowledge them? To address these questions, we survey recent findings from an emerging field of research on research (i.e., metaresearch). We find that many aspects of experimental psychology's research and reporting practices threaten the validity and generalizability of legally relevant research findings, including those relied on by courts and policy-setting bodies. As a case study, we appraise the empirical claims relied on by commentators claiming that measures designed to estimate implicit bias are valid, that implicit bias causes behavior, and that interventions such as trainings can reduce implicit bias--and therefore problematic behavior presumed to be caused by implicit bias. We show that the scientific findings used to support these claims have been persuasively disputed or carry many indicia of unreliability. For example, only limited evidence indicates that interventions designed to reduce prejudicial behavior through implicit bias training are effective. In addition, the research area shows signs of publication bias, suggesting that published estimates of correlations between implicit bias measures and behavior and estimates of the effects of interventions on implicit bias measures do not reflect true effects and causal relationships. To examine whether law journal article authors acknowledge these limits, we collect a sample of one hundred law journal articles mentioning "implicit bias training" published from 2017-2021. Of those one hundred articles, fifty-eight recommend implicit bias training and only eight of those fifty-eight express any skepticism about its effectiveness. Overall, only nineteen of the one hundred articles express skepticism about implicit bias training. We end with recommendations for psychology researchers, law school administrators, legal scholars, law journal editors, courts, and policymakers towards more credible applications of psychology findings in law research and policy. Our focus is on how empirical research can be best used to solve our most important social issues including racism. [ABSTRACT FROM AUTHOR]

What is the effect of caseload volume on case outcome disparities based on a litigant's gender or ethnicity? This paper presents three nonexclusive mechanisms to explain possible effects. The first mechanism relates to a litigant's inclination to settle or withdraw claims; the second mechanism concerns the strategic preemption of appeals by judges; and the third mechanism relates to the implicit biases of judges. To document the effect and test the mechanisms, we exploited a natural, near‐randomized experiment in the Israeli judicial framework. In 2012, six senior registrars were appointed to two of the country's six magistrate court districts. The choice of districts was not related to judicial performance. In these two districts, the civil caseload per judge was substantially reduced. We find that while this reduced caseload had a significant impact on the judicial process for all litigants, it had a particularly salient and beneficial effect on outcomes for female and Arab plaintiffs. The exogenous reduction in court caseloads was associated with positive effects for female Jewish plaintiffs in terms of the likelihood of winning the claim, recovery amount (from the claim), and cost‐shifting outcomes. The change was also associated with positive effects for Arab female plaintiffs in terms of the likelihood of winning the claim and the recovery amount (from the claim). Finally, the reduction in caseloads was associated with a positive effect for Arab male plaintiffs in terms of the fraction of the claim that was recovered. Our findings suggest that when judges are able to invest more time and resources in resolving individual cases, they tend to be less influenced by stereotypes about gender and ethnicity. We discuss the contribution of our findings to the literature on judicial bias and the implications for different policies designed to reduce or manage congested courts. [ABSTRACT FROM AUTHOR]

Legal prediction presents one of the most significant challenges when applying artificial intelligence (AI) to the legal field. The legal system is a complex adaptive system characterized by the ambiguity of legal language and the diversity of value functions. The imprecision and procedural knowledge inherent in law makes judicial issues difficult to be expressed in a computer symbol system. Current semantic processing and machine learning technologies cannot fully capture the complex nature of legal relations, thereby raising doubts about the accuracy of legal predictions and reliability of judicial models. Cognitive computing, designed to emulate human brain functions and aid in enhancing decision-making processes, offers a better understanding of legal data and the processes of legal reasoning. This paper discusses the advancements made in cognitive methods applied to legal concept learning, semantic extraction, judicial data processing, legal reasoning, understanding of judicial bias, and the interpretability of judicial models. The integration of cognitive neuroscience with law has facilitated several constructive attempts, indicating that the evolution of cognitive law could be the next frontier in the intersection of AI and legal practice. [ABSTRACT FROM AUTHOR]

Communities across the country have implemented mediation programs as an alternative dispute resolution process for civilian complaints against police officers. These programs vary from state to state, but certain challenges exist in each, such as ensuring neutrality, encouraging participants to engage fully in the mediation, and navigating subconscious biases held by officers and civilians. In response to these issues, this article considers whether conflict coaching opportunities within these programs have the potential to improve their effectiveness in resolving disputes and better support mediation participants. Conflict coaching is an emerging conflict navigation tool and thus there is limited research on its effectiveness. To determine its potential impact on policecivilian mediations, a group of key stakeholders in the police-civilian mediation program in St. Louis, Missouri were interviewed to determine the potential impact of conflict coaching on the program. These representatives discussed the potential benefits and drawbacks of implementing conflict coaching opportunities for civilians and officers in their own community. Despite potential costs, interviewees overall agreed that a conflict coaching program aligns with community policing standards and meets civilian demand for empathetic conflict resolution [ABSTRACT FROM AUTHOR]

The article discusses the history and context of the American Bar Association (ABA) Model Rule of Professional Conduct 8.4. Topics explored include the guidelines for the ethical behavior of lawyers with regard to their relationship with clients, other lawyers, and the courts, the revision of the Model Rule to address issues such as harassment, bias, and diversity, and the adoption of the Model Rule by various U.S. states particularly Idaho, Texas, and South Carolina.

We report on the findings of five controlled experiments that test the effectiveness of proposed interventions aiming to improve both the process and outcomes of decision making when performing the newsvendor task. We use eye‐tracking technology aided by interviews to gain new insights into people's cognitive processes. Our results confirm that human newsvendors exhibit the tendency of mean‐reverting demand prediction using past demand even when the demand is independent and identically distributed (i.i.d.). This points toward the presence of the cognitive bias known as the law of small numbers in the decision‐making process. The interventions involving ordering multiple products instead of the same product repeatedly, feedback frequency reduction, training and provision of optimal solution, all reduce reliance on the past demand, thereby reducing the extent of the mean‐reverting tendency. Yet, none of these interventions eliminated this tendency completely; overall, judgmental forecasting persisted, albeit at a lower extent or in a different form. This tendency provides a plausible explanation for the fact that the interventions (and the resultant reduction in reliance on past demand) fail to lead to performance improvements. While the qualitative analysis of the interviews shows the subjects' intention to not rely on past demand in some instances, their gaze behavior shows that the past demand continued to impact their orders to varied degrees. Mere reduction (or even removal) of irrelevant information does not automatically make the subjects shift their focus to the relevant information and use them as intended. [ABSTRACT FROM AUTHOR]

Both the literature and practice of constitutional rights adjudication around the world strongly suggest that we live in a binary. Only two "models" are realistically available for us to choose from when deciding how to organize systems for adjudicating rights. The first model is proportionality analysis. In this model, which is extremely common around the world, constitutional rights are defined expansively. And Courts then make highly granular and context-specific determinations on defending rights based on a familiar, single, three or four-step protocol. By contrast, the second model is categorical reasoning. In this model, which is primarily associated with the United States, rights are defined much more narrowly. And Courts then review rights claims based on predetermined but varied tiers of scrutiny or bespoke tests which (1) limit the considerations judges are allowed to weigh and (2) are often meant to be rigid and outcome-determinative. Since the domain of constitutional rights in this model is relatively narrow and because some of the outcome-determinative tests judges use under it tend to sharply bias results in favor of the right being protected, the categorical reasoning model is closely associated with Ronald Dworkin's conception of rights as "trumps." This Article argues that the set of choices available to us is broader than the binary. There is another model around which we can choose to organize systems for adjudicating constitutional rights. And this alternative model is importantly distinct from the existing models: on the one hand, it allows systems to combine key elements of proportionality and categorical reasoning in surprising and previously unexplored ways. On the other hand, this new model diverges from proportionality and categorical reasoning along several crucial dimensions, including the degree of deference to political decision-makers it institutionalizes, the judicial technique and remedy for protecting rights it supplies, and this model's consistent focus on protecting rights endangered by governmental inaction. Perhaps surprisingly, the origins of this new model are also found in the US system, much like categorical reasoning. It is just that it operates in a different corner of American public law than the one we tend to focus on: that of administrative law. This Article describes this new "administrative law model" of constitutional rights adjudication, highlights its distinctive features, and identifies its primary strengths and costs. The Article then argues that it is already possible to identify where the administrative law model would prove attractive and should displace the reliance on the existing models, either in whole or in part. Most clearly, the administrative law model seems especially suited for the system from which it originates--the United States. And in fact, this Article suggests that recognizing that this model exists can increase the prospects of achieving meaningful and desirable change in domestic US constitutional law. However, signs of dissatisfaction with the state of constitutional rights adjudication around the world, among other things, indicate that the model could prove attractive also in other domestic jurisdictions, and even at the international level. Going forward, the administrative law model therefore deserves a permanent place in the global and comparative constitutional toolkit. [ABSTRACT FROM AUTHOR]

Notwithstanding the claims to neutrality of the law and the systems and stakeholders who enforce it, social science research and the lived experience of our primarily Black youth clients reinforce how assumptions and biases - conscious and unconscious - undermine such claims. These assumptions and biases too often become the frames through which our clients and their behavior are perceived, flattening their narratives to fit more neatly into the box of "delinquent" and reinforcing existing systems of power, control, marginalization, and oppression. As a result, our job as youth defenders in the Georgetown Law Juvenile Justice Clinic is often to shift the frame through which we and others view our clients and to develop a counternarrative that advances our clients' expressed interests. To that end, over the last ten years, we have developed a peda- gogical approach designed to prepare our students as nascent attorneys to engage more strategically in the work of frames analysis, critical reflection, and narrative reconstruction. The approach centers around the use of "pedagogical frames" or explicit schemata through which students intentionally and critically examine and interpret everything they encounter in furtherance of their clients' expressed interests. While professors can identify different pedological frames best suited to their course and client work, the Georgetown Law Juvenile Justice Clinic uses adolescence; race; trauma; and sexual orientation, gender identity, and gender expression (SOGIE) as its principal frames. This essay will explain in further detail what we mean by "pedagogical frame," how we selected our principal frames, and how we apply them in our course and client work. Ultimately, as this essay explains, this pedagogical approach seeks to translate the essence of critical legal theory into critical legal praxis and to integrate such praxis into the clinical model. [ABSTRACT FROM AUTHOR]

This article will examine whether the category of religiously motivated terrorism serves justice in Australia, first considering its lawfulness from a human rights perspective and, secondly, examining its operation in the courtroom. Judicial comment in two cases, the subject of national media attention and complaints to the (New South Wales) NSW Judicial Commission, were used as a basis. This article finds that efforts to establish a ‘religious cause’ were stifled by complexity and ambiguity about the difference between Islamic adherence and violent extremism. Biasprone assumptions had observable implications for the judicial assessment of the defendant’s culpability and rehabilitation prospects. Moreover, judicial reasoning seemed to overlook evidence of an intent to coerce the government or intimidate the public, treating religious beliefs and motives as a vehicle to establish intent. The article concludes that judicial education could help. Still, those measures would not fix the core of the problem. By removing the motive element, the issues would be avoided while focusing attention on the remaining intention elements. An alternative option is to remove ‘religious cause’ so that terrorism cases must demonstrate ‘ideological or political cause’, encouraging more precise and comparable reasoning across offending contexts. [ABSTRACT FROM AUTHOR]