The Hidden Inconsistencies Introduced by Predictive Algorithms in Judicial Decision Making

Algorithms, from simple automation to machine learning, have been introduced into judicial contexts to ostensibly increase the consistency and efficiency of legal decision making. In this paper, we describe four types of inconsistencies introduced by risk prediction algorithms. These inconsistencies threaten to violate the principle of treating similar cases similarly and often arise from the need to operationalize legal concepts and human behavior into specific measures that enable the building and evaluation of predictive algorithms. These inconsistencies, however, are likely to be hidden from their end-users: judges, parole officers, lawyers, and other decision-makers. We describe the inconsistencies, their sources, and propose various possible indicators and solutions. We also consider the issue of inconsistencies due to the use of algorithms in light of current trends towards more autonomous algorithms and less human-understandable behavioral big data. We conclude by discussing judges and lawyers' duties of technological ("algorithmic") competence and call for greater alignment between the evaluation of predictive algorithms and corresponding judicial goals

Artificial Intelligence as Evidence

This article explores issues that govern the admissibility of Artificial Intelligence (“AI”) applications in civil and criminal cases, from the perspective of a federal trial judge and two computer scientists, one of whom also is an experienced attorney. It provides a detailed yet intelligible discussion of what AI is and how it works, a history of its development, and a description of the wide variety of functions that it is designed to accomplish, stressing that AI applications are ubiquitous, both in the private and public sectors. Applications today include: health care, education, employment-related decision-making, finance, law enforcement, and the legal profession. The article underscores the importance of determining the validity of an AI application (i.e., how accurately the AI measures, classifies, or predicts what it is designed to), as well as its reliability (i.e., the consistency with which the AI produces accurate results when applied to the same or substantially similar circumstances), in deciding whether it should be admitted into evidence in civil and criminal cases. The article further discusses factors that can affect the validity and reliability of AI evidence, including bias of various types, “function creep,” lack of transparency and explainability, and the sufficiency of the objective testing of AI applications before they are released for public use. The article next provides an in-depth discussion of the evidentiary principles that govern whether AI evidence should be admitted in court cases, a topic which, at present, is not the subject of comprehensive analysis in decisional law. The focus of this discussion is on providing a step-by-step analysis of the most important issues, and the factors that affect decisions on whether to admit AI evidence. Finally, the article concludes with a discussion of practical suggestions intended to assist lawyers and judges as they are called upon to introduce, object to, or decide on whether to admit AI evidence

The Landscape of Artificial Intelligence Ethics: Analysis of Developments, Challenges, and Comparison of Different Markets

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementArtificial Intelligence has become a disruptive force in the everyday lives of billions of people worldwide, and the impact it has will only increase in the future. Be it an algorithm that knows precisely what we want before we are consciously aware of it or a fully automized and weaponized drone that decides in a fraction of a second if it may strike a lethal attack or not. Those algorithms are here to stay. Even if the world could come together and ban, e.g., algorithm-based weaponized systems, there would still be many systems that unintentionally harm individuals and whole societies. Therefore, we must think of AI with Ethical considerations to mitigate the harm and bias of human design, especially with the data on which the machine consciousness is created. Although it may just be an algorithm for a simple automated task, like visual classification, the outcome can have discriminatory results with long-term consequences. This thesis explores the developments and challenges of Artificial Intelligence Ethics in different markets based on specific factors, aims to answer scientific questions, and seeks to raise new ones for future research. Furthermore, measurements and approaches for mitigating risks that lead to such harmful algorithmic decisions and identifying global differences in this field are the main objectives of this research

An Approach For Detecting Online Dating Scams

Online dating scam has been rapidly increasing the internet’s rapid growth synchronically. However, there is no such tool that is available for the public to use it and prevent online dating scams. In this paper, techniques for scam detection in online dating websites profiles are described. A tool for automatically identifying fake profiles on dating websites such as e-Harmony, OkCupid, match.com is used in this paper. The web application generates a scam likelihood regarding the input profile’s description by using the scam action components. Regarding National Public Radio’s news recently, online dating scams had an impact of $143 million in the United States (“Americans Lost$143 Million In Online Relationship Scams Last Year,” 2019). This number indicates the link between the number of users that use online dating websites and the number of scams on these websites. The primary purpose of this paper is creating public awareness and alerting users for whom they might be contacting online dating websites

Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

Ethical, Legal and Social Challenges of Predictive Policing

While being an innovative way to use data and statistical methods to forecast the probability of crime and improve the effectiveness of resource deployment, Predictive Policing is based on many underpinning assumptions. The main ethical issues relating to PP revolve around such topics as data selection and machine bias, visualisation and interpretation of forecasts, transparency and accountability, time and effectiveness, as well as the problem of stigmatisation of individuals, environments and community areas. This translates into the legal domain and particularly into questions relating to privacy. The current legislative framework only partly addresses these issues, focusing mainly on individual rights and not on groups and how they might be affected. The main societal concerns relating to the use of Predictive Policing regard the establishment of trust. In this overview developed in cooperation with several European law enforcement agencies and members of civil society, we argue that, if Predictive Policing’s main target is to reduce crime rates, then, its effectiveness is still unclear.Embora o policiamento preditivo (PP) seja uma modo inovador de usar dados e métodos estatísticos para prever a probabilidade de criminalidade e melhorar a eficácia do uso de recursos, baseia-se em muitas suposições subjacentes. As principais questões éticas relacionadas com o PP relacionam-se com temas como a seleção de dados e viés da máquina (machine bias), visualização e interpretação de previsões, transparência e responsabilidade, tempo e eficácia, assim como o problema da estigmatização de indivíduos, ambientes e áreas da comunidade. Estes problemas têm repercussões no domínio jurídico e, particularmente, no direito à privacidade. O atual quadro legislativo trata apenas parcialmente dessas questões, concentrando-se principalmente nos direitos individuais e não nos dos grupos e em como eles podem ser afetados. As principais preocupações sociais relacionadas com o uso do policiamento preditivo têm que ver com o estabelecimento da confiança. Neste nosso contributo, desenvolvido em cooperação com várias agências policiais e membros da sociedade civil, alegamos que não está ainda demonstrada a eficácia do policiamento preditivo na redução das taxas de criminalidade

Ethical, Legal and Social Challenges of Predictive Policing

While being an innovative way to use data and statistical methods to forecast the probability of crime and improve the effectiveness of resource deployment, Predictive Policing is based on many underpinning assumptions. The main ethical issues relating to PP revolve around such topics as data selection and machine bias, visualisation and interpretation of forecasts, transparency and accountability, time and effectiveness, as well as the problem of stigmatisation of individuals, environments and community areas. This translates into the legal domain and particularly into questions relating to privacy. The current legislative framework only partly addresses these issues, focusing mainly on individual rights and not on groups and how they might be affected. The main societal concerns relating to the use of Predictive Policing regard the establishment of trust. In this overview developed in cooperation with several European law enforcement agencies and members of civil society, we argue that, if Predictive Policing’s main target is to reduce crime rates, then, its effectiveness is still unclear.Embora o policiamento preditivo (PP) seja uma modo inovador de usar dados e métodos estatísticos para prever a probabilidade de criminalidade e melhorar a eficácia do uso de recursos, baseia-se em muitas suposições subjacentes. As principais questões éticas relacionadas com o PP relacionam-se com temas como a seleção de dados e viés da máquina (machine bias), visualização e interpretação de previsões, transparência e responsabilidade, tempo e eficácia, assim como o problema da estigmatização de indivíduos, ambientes e áreas da comunidade. Estes problemas têm repercussões no domínio jurídico e, particularmente, no direito à privacidade. O atual quadro legislativo trata apenas parcialmente dessas questões, concentrando-se principalmente nos direitos individuais e não nos dos grupos e em como eles podem ser afetados. As principais preocupações sociais relacionadas com o uso do policiamento preditivo têm que ver com o estabelecimento da confiança. Neste nosso contributo, desenvolvido em cooperação com várias agências policiais e membros da sociedade civil, alegamos que não está ainda demonstrada a eficácia do policiamento preditivo na redução das taxas de criminalidade