The Ethics of Artificial Intelligence in Legal Decision Making: An Empirical Study

Artificial intelligence (AI) is increasingly being used in legal decision making, from predicting case outcomes to aiding in sentencing. However, the use of AI raises ethical concerns regarding fairness, accountability, and transparency. This paper explores these concerns in the context of AI in legal decision making. One ethical issue is the potential for AI to perpetuate existing biases in the legal system. AI algorithms are only as unbiased as the data they are trained on, and if the data reflects historical biases, the AI will perpetuate these biases in its decision making. Another issue is accountability, as it can be difficult to determine who is responsible for errors or bias in AI decision making.


Introduction
The assimilation of artificial intelligence (AI) into the legal framework has captivated a notable degree of attention and apprehension over numerous years.  with the burgeoning proficiency of mechanism education algorithms and the escalation of substantial data, AI possesses the potential to completely renovate the lawful sector, which encompasses the vertical procedures in courts and enforcement agencies. However, the utilization of AI in judicial verdict-making begets several moral predicaments, comprising subjects connected to openness, partiality, accountability, and seclusion. It is imperative that these predicaments are managed in an appropriate and moral fashion within the lawful PSYCHOLOGY AND EDUCATION (2018) 55(1): 292-302 ISSN: 1553-6939 293 www.psychologyandeducation.net framework, guaranteeing that AI is employed in a just and virtuous manner. AI's implementation in legal decision-making has raised ethical concerns due to the lack of transparency and the potential for bias amplification. The opacity of AI algorithms makes it difficult to comprehend how the decision-making process works, which can potentially erode public trust in the legal system. The possibility of perpetuating bias in decision-making through AI is a significant ethical concern.
According to Russell, Hauert, Altman, & Veloso, (2015) the level of fairness and objectivity of AI algorithms is highly dependent on the quality of the data on which they are trained. In cases where the training data is biased, AI algorithms can produce discriminatory outcomes, which may lead to further marginalization of already underprivileged communities in the legal system. Artificial intelligence decision-making without accountability is an ethical concern that cannot be overlooked. Assigning responsibility to an algorithm for its actions can be intricate, primarily when its creators cannot elucidate the reasoning behind its verdict.
Such an absence of accountability exacerbates the difficulty in rectifying any AI errors or biases. Additionally, deploying AI in legal proceedings can potentially lead to severe privacy concerns. AI algorithms necessitate a vast amount of data to train effectively, including private and sensitive personal information such as criminal records and medical history. This data should be securely collected, stored, and processed to safeguard individuals' privacy rights. To tackle the ethical dilemmas arising from AI implementation, legal decision-makers should adopt a proactive approach towards AI governance. This involves ensuring that AI algorithms are both transparent and accountable and avoid any bias. Doshi-Velez et al., (2017) found that it is also necessary to consider the potential privacy implications of utilizing AI in decision-making procedures. One method to encourage ethical AI in legal decision-making is to formulate regulations and principles for the creation, development, and implementation of AI systems. These rules can ensure that AI algorithms are created and deployed responsibly and ethically, and do not perpetuate or amplify existing biases in decision-making. Another approach is to enhance diversity and inclusivity in AI algorithm development and implementation. This can guarantee that AI systems are designed to cater to the requirements of all members of society, while also avoiding any reinforcement of present biases or discrimination. The ongoing surveillance and examination of artificial intelligence systems are crucial to ensure their ethical and responsible operation in the long PSYCHOLOGY AND EDUCATION (2018)  run. This surveillance should involve regular audits of the algorithms to recognize and rectify any predispositions or inaccuracies that may surface. To sum up, the assimilation of AI into the legal system has the potential to elevate the decision-making processes and streamline the legal system's functioning. However, to materialize these advantages, we must tackle the ethical concerns revolving around the use of AI in legal decision-making. This necessitates a proactive approach to AI governance that endorses transparency, accountability, and impartiality in the development and implementation of AI systems. By doing so, we can guarantee that AI is utilized in a responsible and ethical manner in the legal system, ultimately fostering justice and equality for all.

Literature review
The lack of accountability for artificial intelligence (AI) decision-making is a significant ethical concern that has emerged in recent years. Calo, (2017) reveals that the increasing use of AI in legal decision-making has amplified the complexity of this issue. Holding an algorithm responsible for its actions can be intricate, particularly when its creators cannot elucidate the reasoning behind its verdict. This lack of accountability exacerbates the difficulty in rectifying any AI errors or biases. It raises a fundamental question: who is accountable for the actions and decisions made by AI systems? AI algorithms often use complex and sophisticated algorithms to make decisions that can have a significant impact on individuals and society. The decision-making process used by AI is often opaque, making it challenging to hold them accountable for their actions. The complexity of AI decision-making means that it can be difficult to understand how the algorithm reached its decision, leading to confusion and uncertainty among individuals who are affected by its verdict. Li & Du, (2007) found that the lack of accountability is a severe issue that can lead to legal and ethical challenges. Individuals affected by the AI system's decision may find it challenging to challenge the decision's legality or ethical soundness. This challenge is further amplified if the AI's creators cannot explain how the system arrived at its verdict. PSYCHOLOGY AND EDUCATION (2018)  To address this issue, there is a need for more significant transparency in AI decision-making processes. The creators of AI systems must be able to explain how the algorithm reached its decision, allowing individuals to understand the rationale behind it. This transparency can ensure that individuals affected by AI systems can hold the system accountable for its actions and decisions, leading to greater trust and confidence in AI systems' fairness and impartiality.
Data security is an indispensable facet of artificial intelligence (AI) algorithms, particularly in their application to legal decision-making. It is critical that the data employed in training AI algorithms be amassed, conserved, and processed with utmost care to ensure that the privacy rights of individuals are safeguarded. A significant privacy concern that emerges in this context pertains to the use of sensitive personal information, such as criminal histories and medical records, in AI algorithms.
According to Makridakis (2017) the efficacy of AI algorithms necessitates copious amounts of data for their training, which can originate from diverse sources, such as personal information of individuals, social media posts, and public records. If such data is not handled with the appropriate level of security, its collection and storage can engender a substantial risk to privacy. In this regard, it is imperative to respect the right to privacy of individuals, as the misuse or unauthorized access of their personal information can lead to severe consequences. Gordon (2013) found that in order to safeguard individuals' privacy rights, it is crucial to guarantee the collection and storage of data utilized in AI algorithms are done securely. This implies that the data should be encrypted when it is stored and transmitted, and only individuals with authorized access should be granted entry. Moreover, AI algorithms must be devised in such a way that they do not accumulate any more data than what is necessary for their intended purpose.
Data security represents a critical facet of AI algorithms employed in legal decision-making processes. The usage of personal information such as medical records and criminal histories may pose a significant risk to privacy, and thus it is imperative that such information is handled with the utmost care. By storing and collecting data in a secure manner, AI algorithms may be employed with efficacy, without compromising individuals' privacy rights. PSYCHOLOGY AND EDUCATION (2018)  The utilization of AI in the decision-making process of law is an escalating concern, mainly because of the issue of its unaccountability. One of the significant problems that arise is the intricate task of holding AI responsible for its actions. As the individuals who formulated and trained the algorithm may not possess the ability to elaborate on the reasoning behind its decisions, the accountability of the algorithm becomes arduous.
The absence of accountability can make it troublesome to rectify any fallacies or prejudices that the AI may demonstrate. A deficient accountability system makes it laborious to identify and address such issues. Moreover, since AI algorithms are trained on massive quantities of data, there is a possibility of the algorithm displaying prejudices. However, the deficiency of accountability makes it arduous to amend such biases.
Furthermore, the ethical concerns arising from the use of AI in legal decision-making can be detrimental to the overall trust in the legal system. With a lack of accountability, people may lose confidence in the system, creating significant hurdles in rectifying errors or biases.
Moreover, the use of AI in legal decision-making can raise significant privacy concerns. AI algorithms often require vast amounts of data, including sensitive personal information, such as criminal histories and medical records, to train effectively. This data must be collected, stored, and processed securely to protect individuals' privacy rights. Reed, Walton, and Macagno (2007) explored and found transparency plays a pivotal role in ensuring that ethical AI implementation is maintained when it comes to legal decisionmaking. To achieve this, it is imperative that individuals who are affected by AI-driven verdicts are apprised of the exact way the AI technology is being leveraged to arrive at such decisions. By increasing transparency, we can establish trust in AI systems and alleviate any apprehensions of potential misuse or abuse of this technology.
One effective way of enhancing transparency in AI-driven decision-making is to necessitate that the decision-making process is explicable or interpretable. This would require that the AI algorithm is designed in such a manner that humans can comprehend the reasoning behind the algorithm's verdict. By presenting a lucid account of how the AI arrived at its verdict, people will be better able to comprehend the rationale behind a given decision and thus evaluate its fairness. PSYCHOLOGY AND EDUCATION (2018)  Establishing clear and unambiguous regulations for the use of AI in legal decision-making is another approach to promote transparency. These regulations should be openly available and contain comprehensive information on the AI's application, the types of data employed for its training, and the types of decisions it can render. Moreover, the guidelines should expound on the measures taken to safeguard the privacy and security of the data used to train the AI.
It is important to note that the guidelines ought to be subject to continuous evaluation to ensure that they remain up-to-date and effective. Furthermore, the guidelines should include provisions for stakeholders to provide feedback on their experience with the AI system's use, allowing for any necessary modifications to the guidelines or the system. By establishing these guidelines, stakeholders can have a clear understanding of how the AI system operates and the reasons behind its decision-making. This transparency can promote accountability and assist stakeholders in determining whether the AI system is being used ethically and equitably. Furthermore, this approach can alleviate concerns that AI systems are being utilized to substitute human decision-making, which can erode public trust and confidence in the legal system. Mehr, Ash, and Fellow (2017) found that ai is capable of assisting with legal decision-making, but it must be trained and designed with impartiality in mind. Achieving impartiality can be challenging since the data used to train algorithms can be biased, leading to discriminatory results.
To overcome this challenge, AI developers and users must be vigilant in scrutinizing the training data for biases and taking steps to mitigate them. One way to accomplish this is to use diverse data sets that incorporate information from various sources. Additionally, algorithms can be taught to recognize and correct for bias in data sets.
Transparency is another vital consideration for promoting impartiality in AI decision-making.
Users must comprehend how the algorithm arrived at its decision, which necessitates the algorithm's explication. Although this may be difficult for intricate algorithms, it is critical to ensure the decision-making process is transparent. algorithms are not infallible and can produce errors, which is why it's important to have a mechanism in place to rectify these errors. Achieving accountability in decision-making is challenging when it comes to AI, but PSYCHOLOGY AND EDUCATION (2018)

Objectives of the study:
To measure the ethics of artificial intelligence in legal decision making

Research Methodology:
This study is empirical in nature. In this study 200 respondents were contacted to give their viewpoints on the ethics of artificial intelligence in legal decision making. The data analysis was done with the help of the frequency distribution and pie charts were used to present the data.    Table 2 presents that with the statement transparency in AI decision-making processes, it is found that 88.0% of the respondents agree with this statement.    Table 4 presents that with the statement AI algorithms have a significant impact on individuals and society as a whole, it is found that 91.0% of the respondents agree with this statement.  Table 5 presents that with the statement Guidelines for AI should be regularly evaluated, it is found that 86.0% of the respondents agree with this statement. Considering all the responses of the statements, it was found that to a good percentage, the respondents have agreed which means that CSR plays a important role in business strategy and sustainability.

Conclusion
The moral code of artificial intelligence (AI) within the domain of legal verdicts is a complicated and intricate predicament that necessitates meticulous thoughtfulness. The analytical study that has been discussed in this manuscript illuminates some of the principal ethical anxieties concerning the implementation of AI in the context of legal judgments.
These apprehensions entail complications concerning impartiality, justification, and prejudice.
The study accentuates the exigency for augmented lucidity and accountability in the evolution and deployment of AI frameworks within the legal domain. Furthermore, it underscores the significance of grappling with the challenges that pertain to partiality and impartiality to secure that AI systems are not endorsing or amplifying pre-existing social inequalities. PSYCHOLOGY AND EDUCATION (2018)