Are some lawyers getting an unfair advantage in the bar exam?

1998 ◽  
Author(s):  
S. Sleek
Keyword(s):  
Unfair Advantage

Guilty Minds

2017 ◽  
Author(s):  
Anne C. Dailey
Keyword(s):  
Decision Making ◽  
Criminal Justice ◽  
Criminal Justice System ◽  
Criminal Law ◽  
Rational Decision ◽  
Police Interrogation ◽  
Rational Person ◽  
Unfair Advantage ◽  
Rational Decision Making ◽  
Unconscious Guilt

This chapter examines the puzzling question of why an otherwise rational person would voluntarily confess to a crime, knowing full well that the state will punish in return. Even more puzzling is the phenomenon of false confessions, where an individual inexplicably confesses to a crime she did not commit, in some cases believing in her own guilt. Psychoanalysis gives us important insights into these irrational phenomena. The focus in this chapter is on the ways in which certain deceptive and degrading police interrogation tactics may override a suspect’s conscious rational decision-making powers by enlisting unconscious needs, aggressions, and guilt. Three interrogation tactics are of greatest concern: false sympathy, degradation, and trickery. As this chapter shows, false sympathy and degradation exploit deep-seated, unconscious desires for absolution and punishment that undermine the voluntariness of a suspect’s self-incriminating statements. Similarly, police trickery can take unfair advantage of a suspect’s need to rationalize unconscious guilt for a crime he did not commit. By drawing attention to the risks associated with these methods, psychoanalysis ensures that the most egregious practices can be eliminated from our criminal justice system. Psychoanalytic insights into unconscious processes advances the law’s own best ideals of fundamental fairness in the criminal law.

Proposal for dimensionality testing in QPQ

2018 ◽  
Vol 18 (13&14) ◽  
pp. 1125-1142
Author(s):  
Arpita Maitra ◽  
Bibhas Adhikari ◽  
Satyabrata Adhikari
Keyword(s):  
Information Processing ◽  
Quantum Information ◽  
Quantum System ◽  
Quantum State ◽  
Quantum Information Processing ◽  
Quantum States ◽  
Quantum Private Query ◽  
Unfair Advantage ◽  
Security Proofs

Recently, dimensionality testing of a quantum state has received extensive attention (Ac{\'i}n et al. Phys. Rev. Letts. 2006, Scarani et al. Phys. Rev. Letts. 2006). Security proofs of existing quantum information processing protocols rely on the assumption about the dimension of quantum states in which logical bits are encoded. However, removing such assumption may cause security loophole. In the present paper, we show that this is indeed the case. We choose two players' quantum private query protocol by Yang et al. (Quant. Inf. Process. 2014) as an example and show how one player can gain an unfair advantage by changing the dimension of subsystem of a shared quantum system. To resist such attack we propose dimensionality testing in a different way. Our proposal is based on CHSH like game. As we exploit CHSH like game, it can be used to test if the states are product states for which the protocol becomes completely vulnerable.

For Honor, for Toxicity

2021 ◽  
Vol 5 (CHI PLAY) ◽  
pp. 1-29
Author(s):  
Alessandro Canossa ◽  
Dmitry Salimov ◽  
Ahmad Azadvar ◽  
Casper Harteveld ◽  
Georgios Yannakakis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Random Forests ◽  
Initial Study ◽  
Unfair Advantage ◽  
Offensive Behavior ◽  
Forest Models ◽  
Random Forest Models ◽  
Action Type ◽  
Degree Of Severity

Is it possible to detect toxicity in games just by observing in-game behavior? If so, what are the behavioral factors that will help machine learning to discover the unknown relationship between gameplay and toxic behavior? In this initial study, we examine whether it is possible to predict toxicity in the MOBA gameFor Honor by observing in-game behavior for players that have been labeled as toxic (i.e. players that have been sanctioned by Ubisoft community managers). We test our hypothesis of detecting toxicity through gameplay with a dataset of almost 1,800 sanctioned players, and comparing these sanctioned players with unsanctioned players. Sanctioned players are defined by their toxic action type (offensive behavior vs. unfair advantage) and degree of severity (warned vs. banned). Our findings, based on supervised learning with random forests, suggest that it is not only possible to behaviorally distinguish sanctioned from unsanctioned players based on selected features of gameplay; it is also possible to predict both the sanction severity (warned vs. banned) and the sanction type (offensive behavior vs. unfair advantage). In particular, all random forest models predict toxicity, its severity, and type, with an accuracy of at least 82%, on average, on unseen players. This research shows that observing in-game behavior can support the work of community managers in moderating and possibly containing the burden of toxic behavior.

An Electronic Contract Signing Protocol Using Fingerprint Biometrics

2011 ◽  
pp. 201-228
Author(s):  
Harkeerat Bedi ◽  
Li Yang ◽  
Joseph M. Kizza
Keyword(s):  
User Authentication ◽  
The Other ◽  
Fair Exchange ◽  
Unfair Advantage ◽  
Replay Attacks ◽  
Cryptographic Algorithms ◽  
Fingerprint Biometrics ◽  
Authentication System ◽  
Contract Signing ◽  
Password Management

Fair exchange between a pair of parties can be defined as the fundamental concept of trade where none of the parties involved in the exchange have an unfair advantage over the other once the transaction completes. Fair exchange protocols are a group of protocols that provide means for accomplishing such fair exchanges. In this chapter we analyze one such protocol which offers means for fair contract signing, where two parties exchange their commitments over a pre-negotiated contract. We show that this protocol is not entirely fair and illustrate the possibilities of one party cheating by obtaining the other’s commitment and not providing theirs. We also analyze a revised version of this protocol which offers better fairness by handling many of the weaknesses. Both these protocols however fail to handle the possibilities of replay attacks where an intruder replays messages sent earlier from one party to the other. Our proposed protocol improves upon these protocols by addressing to the weaknesses which leads to such replay attacks. We implement a complete working system which provides fair contract signing along with properties like user authentication and efficient password management achieved by using a fingerprint based authentication system and features like confidentiality, data-integrity and non-repudiation accomplished through implementation of cryptographic algorithms based on elliptic curves.

Artificial Intelligence and Trademark Assessment

2021 ◽  
pp. 266-291
Author(s):  
Anke Moerland ◽  
Conrado Freitas
Keyword(s):  
Artificial Intelligence ◽  
Intellectual Property ◽  
Research Methods ◽  
Interview Data ◽  
Third Party ◽  
Distinctive Character ◽  
Trademark Law ◽  
Unfair Advantage ◽  
Present Information ◽  
Near Future

Artificial intelligence (AI) has an unparalleled potential for facilitating intellectual property (IP) administration processes, in particular in the context of examining trademark applications and assessing prior marks in opposition and infringement proceedings. Several stakeholders have developed AI-based algorithms that are claimed to enhance the productivity of trademark professionals by carrying out, without human input, (parts of) the legal tests required to register a trademark, oppose it, or claim an infringement thereof. The goal of this chapter is to assess the functionality of the AI tools currently used and to highlight the possible limitations of AI tools to carry out autonomously the legal tests enshrined in trademark law. In fact, many of these tests are rather subjective and highly depend on the facts of the case, such as an assessment of the distinctive character of a mark, whether the relevant public is likely to be confused or whether a third party has taken unfair advantage of a mark. The chapter uses doctrinal research methods and interview data with fourteen stakeholders in the field. It finds that AI tools are so far unable to reflect the nuances of the subjective legal tests in trademark law and, it is argued, even in the near future, AI tools are likely to carry out merely parts of the legal tests and present information that a human will have to assess, taking prior doctrine and the circumstances of the case into account.

England, 1919–21

2020 ◽  
pp. 44-58
Author(s):  
Ann Jefferson
Keyword(s):  
Unfair Advantage ◽  
David Copperfield

This chapter introduces England as a part of Natalia Ilyinichna Tcherniak's mental geography through the books “David Copperfield” and “The Prince and the Pauper.” It explains how England acquired a new reality for Natalia with the arrival of the nannies hired by her step-mother Vera Sheremetievskaya to teach English to her half-sister, Lili. It also points how keen Vera was to have Lili learn English in order to redress the unfair advantage that she saw Nathalia had since she already spoke three languages and excelled at school. The chapter recounts Nathalia's time in Oxford, England, which she considered the happiest time of her life. It also mentions Nathalia's abrupt return to Paris when her father, Ilya Evseevich Tcherniak, refused to approve of her plan of continuing her studies in history while giving private French lessons as he did not wish to see his daughter become a bluestocking.

3. Incorporation

Company Law ◽  
2019 ◽  
pp. 51-76
Author(s):  
Lee Roach
Keyword(s):  
General Rule ◽  
Private Companies ◽  
Public And Private ◽  
Unfair Advantage ◽  
Secretary Of State ◽  
Different Types ◽  
Royal Charter ◽  
A Company

This chapter examines the various ways by which a company can be created and the different types of company that can be created. The process of creating a company is known as ‘incorporation’. There are four principal methods of incorporating a company: by royal charter; by Act of Parliament; by delegated authority; or by registration. The general rule is that the Companies Act 2006 (CA 2006) only applies to registered companies. However, in order to prevent unregistered companies being under-regulated and having an unfair advantage over registered companies, the CA 2006 provides that the Secretary of State may pass regulations that set out how the CA 2006 is applied to unregistered companies. There are a number of different company types that can suit a wide array of businesses. These include public and private companies. Companies can change their status by a process called re-registration.

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