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Judging bias is ironically an inherent risk in every competition, which might threaten the fairness and legitimacy of the competition. The patriotism effect represents one source of judging bias as the judge favors contestants who share the same sentiments, such as the nationalistic, racial, or cultural aspects. This study attempts to explore this type of judging bias in a university student competition. In addition, this study tries to expand the literature on judging bias by proposing the term universitarian bias as the judge is suspected to give a higher score to contestants from the same university.

The association rule of data mining is used to accomplish the objective of the study. To demonstrate the applicability of the method, the data set from the annual national university student competition in Indonesia is exploited.

The result strongly discovers that the universitarian bias is likely to be present. Some recommendations are also provided in order to minimize the bias that might happen again in the future.

As the implication of the presence of the universitarian bias, the committee should remove all the university features attributed to the participants. This endeavor is expected to minimize the universitarian bias that might happen.

This research is claimed to be the first attempt in implementing the data mining technique in the field of judging bias.

This paper aims to investigate factors which may influence or bias judges’ decisions to exclude or admit the testimony of accounting expert witnesses, under the US judicial guidelines commonly known as the Daubert/Kuhmo standards. Accounting experts are increasingly providing expert testimony as a part of financial litigation support services.

Judges’ decisions, in which opposing council evoked a Daubert/Kuhmo challenge to the testimony provided by 130 professional accountants serving as expert witnesses, were analyzed. The period of study was 2010 through 2014. Based on prior research, three variables believed to potentially influence or bias judges to systematically exclude expert testimony were examined: gender, complexity and familiarity.

The results of binary logistic regression show that none of the variables has a significant relationship to the accounting expert witnesses’ probability of surviving a challenge to Daubert/Kuhmo standards. Findings suggest that judges are objective in evaluating the testimony provided by accounting experts under Daubert/Kuhmo guidelines and that they may be immune to biases based solely on gender, complexity and familiarity.

These results will be of interest to judges, lawyers and forensic accountants acting as expert witnesses.

The Scottish Government hope to pilot judge only rape trials to increase the woefully low rape conviction rates in Scotland. The reasoning is that by removing jurors, the court will be attenuating the role that rape myths and other cognitive and social biases have on conviction rates. However, a plethora of research from cognitive and social psychology, legal literature and decision-making science has shown that experts, including judges and other legal professionals, may be no less biased than laypeople. This paper aims to outline the research highlighting that experts may also be biased, why biases in judges can be elicited, and potential alternative recommendations (i.e. deselecting jurors who score highly on rape myths and providing training/education for jurors). Furthermore, piloting with real judges, in real trials, may not be best practice. Therefore, the authors recommend that any piloting is preceded by experimental research.

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Furthermore, piloting with real judges, in real trials, may not be best practice; therefore, the authors recommend that any piloting is preceded by experimental research.

To the best of the authors’ knowledge, this research is the first of its kind to directly compared the decision-making of jurors and judges within the current Scottish legal context.

This paper synthesizes the extant research on the outcome effect in the accounting domain, focusing primarily on the context of performance evaluation. It reviews the current state of our knowledge about this phenomenon, including its underlying cognitive and motivational causes, the contexts in which the outcome effect is observed, the factors that influence its various manifestations, and ways in which undesirable outcome effects can be mitigated. It also considers various perspectives about the extent to which outcome effects represent undesirable judgmental bias, and whether this distinction is necessary to motivate research on this topic. The paper is intended to motivate and facilitate future research into the effects of outcome knowledge on judgment in the accounting context. Therefore, we also identify important unanswered questions and discuss opportunities for future research throughout the paper. These include additional consideration of instances in which the outcome effect is reflective of bias, how this bias can be effectively mitigated, ways in which outcome information influences judgment (regardless of whether this influence is considered normative), and how the underlying causes of the outcome effect operate singly and jointly to bring about the outcome effect. We also consider ways that future research can contribute to practice by determining how to encourage evaluators to retain and incorporate the relevant information conveyed by outcomes, while avoiding the inappropriate use of outcome information, and by enhancing external validity to increase the generalizability of experimental results to scenarios frequently encountered in practice.

Opinion polls show that contributions to judicial candidates create an appearance of corruption. This perception damages the institutional legitimacy of the courts. This chapter explores the relationship between integrity ratings of Illinois trial judges and campaign contributions. Specifically, it examines the Illinois State Bar Association judicial poll integrity scores of 253 elected judges seated in 101 Illinois counties during 1994–2012. Regression analysis reveals that judicial candidates’ integrity scores declined as (a) the amount of attorney contributions increased; (b) the number of reported attorney contributors enlarged; and (c) the number of large attorney contributors grew. This chapter also discusses the efficacy and limitations of four policies meant to diminish the appearance of corruption: recusal and disqualification rules; anonymous contributions; public financing; and the elimination of the election of judges. Although a radical solution, the policy of abolishing judicial elections is more likely to overcome the appearance of corruption than the other reforms.

Purpose – To provide strategic management scholars, particularly graduate students and new faculty members, a novel approach, the lens model, to investigate emerging economies phenomena.

Design/methodology/approach – Based on a review of the strategic management literature and a search of the strategy databases and journals, I propose the lens model approach and discuss its origins, development, and designs since its introduction. It has been used extensively in such fields as cognitive psychology, social psychology, medicine, agriculture, human resources management, and organizational behavior. Besides the wide application, it has relevance for strategic management research.

Findings – An illustrative study and a summary of the approach from a previous study in one prominent journal are also provided as guides. I conclude by providing recommendations on what to consider in using the approach for the study of emerging economies.

Research limitations/implications – In addition to the strengths of the approach, its weaknesses are also discussed. Suggestions on maximizing the potential of the approach are also discussed.

Practical implications – The approach is an invaluable source particularly for graduate students of strategy who often are unfamiliar with microlevel approaches. They can use it to supplement approaches for strategic management.

Originality/value – To my knowledge, this chapter is the first to discuss the lens model approach in the strategic management literature. In that regard, it fills a gap in the research methodology literature. It can therefore help graduate students improve their careers.

The purpose of this study is to examine public perceptions and attitudes toward using artificial intelligence (AI) in the US criminal justice system.

The authors took a quantitative approach and administered an online survey using the Amazon Mechanical Turk platform. The instrument was developed by integrating prior literature to create multiple scales for measuring public perceptions and attitudes.

The findings suggest that despite the various attempts, there are still significant perceptions of sociodemographic bias in the criminal justice system and technology alone cannot alleviate them. However, AI can assist judges in making fairer and more objective decisions by using triangulation – offering additional data points to offset individual biases.

Other scholars can build upon the findings and extend the work to shed more light on some problems of growing concern for society – bias and inequality in criminal sentencing. AI can be a valuable tool to assist judges in the decision-making process by offering diverse viewpoints. Furthermore, the authors bridge the gap between the fields of technology and criminal justice and demonstrate how the two can be successfully integrated for the benefit of society.

To the best of the authors’ knowledge, this is among the first studies to examine a complex societal problem like the introduction of technology in a high-stakes environment – the US criminal justice system. Understanding how AI is perceived by society is necessary to develop more transparent and unbiased algorithms for assisting judges in making fair and equitable sentencing decisions. In addition, the authors developed and validated a new scale that can be used to further examine this novel approach to criminal sentencing in the future.

This study aims to provide an overview of studies concerning bias in law, particularly in judges’ decisions. The authors intend to bring to light the factors that can most frequently lead to unequal decisions to enable judges to better perform their function.

A literature review was used as a methodology based on studies involving judges and juries.

The evidence reported by this review suggests how difficult the judge’s job is and how they can be unconsciously influenced by inferences, deductions and biases. The results show that the pleasantness of the witness and the confidence they exhibit during testification are crucial factors in influencing the decisions of judges and jurors. From these studies, it can be assumed that different personal aspects – smiling, pleasantness and the witness’s credibility – can be positively associated with each other, which could compromise the ongoing evaluation. Gender is another factor that can influence evaluations; in fact, witnesses are evaluated based on their own “gender” as well as that of the jurors. Another essential factor is self-confidence. Also, the age of both of the judge and of the witness can be a factor that influences decisions in court. Other factors such as communication effectiveness, degree of accuracy of reported information and non-verbal behaviour were also found to be important.

Among the limitations of this research, the authors have to consider the low number of available research and that the most of these derive from a specific cultural context – the American one. There may also be limits to the way in which certain concepts are used in different parts of the world, particularly through a very broad construct, such as the credibility of witness.

This study highlights which inferences and biases can characterise decision-making processes and, above all, highlights the need for specific training courses aimed at managing the many processes involved in influencing human decisions.

The authors believe that this work can raise awareness about the series of unconscious reasoning that may happen in the legal field, which has a major impact on people’s lives and on the general perception of justice.

In this research, the authors have considered some of the criteria that may intervene in the evaluation of witnesses, those present in the current scientific literature. From the research, it seems appropriate and necessary to provide judges with adequate training aimed at the recognition of their cognitive processes and bias. In fact, when they were made aware of them, they were less affected by bias, resulting in more objective and limiting improper inferences.

This study examines whether various judicial demographic and political characteristics have an influence on case outcomes in transgender employment discrimination cases. Specifically, it assesses whether the race, sex or political party of federal judges result in significantly different employment case outcomes for transgender employees in the US district courts.

Utilizing a legal database of all federal employment discrimination cases over the past five decades, the study ultimately identified 97 cases with transgender plaintiffs. Chi-square and frequency analyses were employed to test the hypotheses regarding the effect of race, sex and political party of federal judges on transgender employment case outcomes.

The results intimate that both the political party and sex of the judge have an effect on case outcomes. Specifically, the transgender plaintiffs in employment discrimination cases have a greater chance for success when such cases are presided before Democratic and female judges.

The study's findings of significant differences in case outcomes suggest that characteristics of judges should be taken into account by potential plaintiffs and defendants, as they consider if/how to proceed with their cases.

Such research focuses more attention on the fair and equal treatment principle of the American judicial system due to the significant differences found in case outcomes as a result of judges' characteristics.

No research till date has examined the outcomes of transgender employment discrimination cases in the US despite national surveys indicating the pervasiveness and severity of such discrimination.

This study aims to examine whether the demographics of the US federal judiciary and the type of employment discrimination charge influence federal employment discrimination case outcomes.

The outcomes of 401 randomly selected employment discrimination cases were examined by utilizing chi square analysis to test the interaction effects of race and gender along with four different charges of employment discrimination.

The findings suggest that the outcomes of employment discrimination cases are a function of the interaction of the judges' gender and race along with the type of discrimination charge (e.g. gender, race, age, or disability discrimination) involved in the case.

More research studies with larger cell sample sizes for certain discrimination claims should be conducted to ascertain the validity of the current results.

Potential litigants in employment discrimination cases (both plaintiffs and defendants) may find these results relevant in determining their chances for success in the courtroom.

These findings could help judges become more aware of potential biases and help guard against being influenced by them. These findings may also have implications for the selection and appointment of judges and suggest that judicial bodies that are more diverse may render more unbiased rulings.

Previous research regarding the influence of the sex and race of the judge on court case outcomes has yielded contradictory and confusing findings. However, by controlling for the possible influence of the type of charge involved in the cases, the findings of the current study suggest that judges' rulings are a function of the interaction of the judges' demographic characteristics with the type of discrimination charge.