Amidst the burgeoning backlog of unresolved legal cases, many countries are actively seeking to upgrade their legal system by integrating the intelligent technology within the judiciary to expedite the disposal of legal matters. This endeavour calls for the role of artificial intelligence (AI) in the legal system. From legal inquiries to AI systems to the challenges in determining responsibility for AI-driven actions, this article expounds on ethical considerations which cannot be ignored in the intersection of automated justice with law.

An article by Ashutosh Kumar

The integration of artificial intelligence (AI) has the potential to transform traditional legal practices. Generally, we all know that law books answer legal questions. But imagine AI-law books that also construct arguments, translate legislative texts by giving illustrations and examples for laymen, anticipate and formulate legal reasoning, and offer insights. This integration can offer a more efficient, accessible and transparent legal system. However, it can bring a host of societal and ethical considerations, such as legal decisions made by digital entities, privacy concerns, and determining legal liability within the digital realm.


AI allow us to close the gap between human languages, in which legislation is expressed, and the computational capabilities of machines. While legal professionals have transitioned from printed texts to digital formats, digitization alone doesn’t harness the full potential of AI. To unlock the true power of AI in law, we must convert legal provisions into a format that computers can comprehend and process. By doing so, we enable computers to interpret and apply the law more effectively.

AI offers the ability to create computational models of the law. These models can represent legal rules in a manner that computers can understand, by translating legislative language into algorithmic form. This is advantageous because many laws are conditional in nature, making them well-suited for computational analysis. AI can assess whether specific conditions within a law are met, providing clear and precise legal conclusions as outputs. This not only enhances the efficiency of legal research but also makes the law more accessible to individuals who may not have legal expertise.

Furthermore, AI can facilitate the creation of active connections between different legislative provisions, revealing the intricate superstructures of the law. It can link legal texts to precedents interpreting those laws, thus surpassing the traditional internet links. This interconnectedness can help legal professionals and the public alike navigate legal systems with greater ease.

AI in law also has the potential to offer legal predictions based on specific scenarios. It can be valuable to understand how their actions might be interpreted under the law. Additionally, judges can use it to supplement thorough interpretation of the legal rules and their application.

AI complements human expertise in law by enhancing coherence, fairness, and transparency. Properly designed and supervised, it can improve legal accessibility, accuracy, and can aid autonomous legal administration.


AI’s role in law poses a set of distinctive challenges and opportunities. One significant issue would be to determine who should be held responsible when intelligent autonomous machines cause harm. For instance, if a self-driving car injures a pedestrian or a robotic doctor makes an incorrect diagnosis, assigning liability becomes complex. Our traditional legal systems are rooted in human responsibility, free will, and control – doctrinal figures that don’t apply to machines acting independently. Deciding on the accountability of developers is tricky, given that machine learning systems can evolve beyond human understanding and control. This leads to debates about fairness and feasibility when attributing responsibility to those who create these autonomous systems.

AI’s presence in criminal law, particularly its use in surveillance and facial recognition, offers both advantages and drawbacks. AI-powered surveillance, such as Closed Circuit Television (CCTV) cameras with facial recognition, can aid in capturing suspects. However, research shows that most advanced AI facial recognition tools make mistakes more often with black women than white men. It also raises concerns regarding privacy, data protection, and misuse of such technology. So, there is a need to strike a balance between the benefits of AI-assisted surveillance, discrimination based on colourism and safeguarding civil liberties for public law.

While Artificial intelligence is also a double-edged sword for administrative law, on the one hand, it offers faster, more efficient, and accessible public services, aligning with the demands for good administration. It can help to eliminate corruption and reduce human bias by making decisions based solely on objective data. For instance, AI applications can be used to assess the need for social care for children, and studies suggest they can outperform humans. On the other hand, there are significant challenges in implementing AI in administrative law. A significant challenge lies in the autonomous evolution of machine learning. For instance, an AI system is employed for hiring processes within a government agency. The AI, utilizing historical employment data, might inadvertently learn biases present in past hiring decisions. This could lead to the perpetuation of disparities, favoring certain demographics over others. The lack of transparency in the AI’s decision-making makes it challenging for applicants to understand the basis of their rejection, hindering accountability. It would cause difficulty for administrators to identify and rectify the biased patterns, reflecting historical inequalities rather than promoting diversity and equal opportunity. It can turn out to be challenging to pinpoint which data points were most influential, making it harder to detect and rectify biases and flaws.

With the reduced human participation in the decision-making process, it raises accountability issues for inaccurate results. Moreover, part of good administration involves human interaction, which assures citizens that their concerns are being heard and considered by a public servant who then makes a lawful decision.


AI’s integration into private law has implications, yet comes with key applications and challenges. For example, in IP law, AI raises various concerns. Firstly, the issue of incentivising innovation arises. Determining how society values human creativity while competing with AI depends on the autonomy of AI and its ability to replicate human economic behaviour. Secondly, the question of authorship arises when AI contributes to creative arts or inventions. Currently, legal systems recognize only human persons as authors or inventors, posing challenges when AI’s creative process conflicts with these requirements. Lastly, the issues of infringement further complicate matters, as identifying responsibility becomes complex when AI entities lack legal personality. Despite these challenges, AI can be involved in IP office administration for registration and maintenance of granted IP rights, enhancing enforcement through infringement detection mechanisms, and optimizing the process of granting intellectual property rights to enable more effective enforcement measures.

Furthermore, AI has the capacity for early identification of suspicious transactions in fraud detection within financial institutions. With data analysis, including both conventional (e.g., income, wealth, payment history) and non-conventional financial information (e.g., purchasing behaviour, social media activity, cultural preferences), alongside historical transaction data, AI algorithms excel at recognizing patterns indicative of fraud. Machine learning enables continuous adaptation, making AI adept at identifying emerging threats (that might elude human scrutiny) and automating the detection process. However, while AI offers benefits in fraud detection, it also poses great challenges. These include the perpetuation of historical discrimination, lack of transparency in AI decision-making processes, regulatory and legal issues, and the use of AI by fraudsters to develop new evasion techniques. Addressing these challenges is crucial to ensuring that AI-driven fraud detection remains accurate, fair, and unbiased.

Furthermore, AI’s predictive capabilities extend to legal outcomes, where it can analyze court pleadings, complaints, and relevant laws to forecast judgments. While this offers a revolutionary approach to legal decision-making, practical difficulties emerge.

Let’s take an example. Envision a scenario in the age of social media, where a judge, perhaps reluctantly, consumes an apple for breakfast and subsequently shares an image of this meal on a platform such as Twitter, either personally or through their spouse or friend’s account. On such a day, the judge’s disposition might be adversely affected, leading to irritability and the possibility of rendering unfavourable judgments. Conversely, if the judge were to enjoy a meal of rice, their mood might be notably improved.

In such instances, machine learning algorithms, akin to unguided arrows, possess the capacity to assimilate data autonomously, including connections between a judge’s social media activity and their judicial decisions on specific days. The inherent unpredictability of what such systems may learn presents a significant ethical quandary, particularly concerning the integrity of judicial processes.

Thus, the impartiality of these AI systems towards data origins may contribute to the incorporation of extrinsic data, such as judges’ breakfast preferences or data from personal social media accounts, into the evaluation of legal matters. This integration raises ethical considerations surrounding privacy and its implications for legal prediction. It could undermine confidence in legal decision-making and prompt a call for novel legal safeguards upholding ideals and aspirations.


The integration of AI into various domains of law is transforming the landscape of legal theory and practice. AI has the potential to bridge the gap between human language and computational capabilities. It can improve the efficiency of legal research and the accessibility of legal information. It can also enhance decision-making processes and provide insights into legal outcomes.

However, the integration of AI in law is not without challenges and ethical considerations. Questions of responsibility and accountability arise when AI systems act autonomously, and concerns about transparency and bias persist.

As we move forward in this era of AI in law, it is crucial to balance between technological advancements and human oversight. AI should not replace human expertise but rather enhance it, making legal systems more coherent, fair, and transparent. The evolving legal landscape must adapt to the transformative power of AI, ensuring principles of justice, accountability, and protection of individual rights are upheld.

Published under licence CC BY-NC-ND. 

This Blogpost was written by


  • Ashutosh Kumar

    Ashutosh Kumar is a third-year law student pursuing B.Sc.Ll.B (law with data science) from National Forensic Sciences University, Gandhinagar.

Ashutosh Kumar Written by:

Ashutosh Kumar is a third-year law student pursuing B.Sc.Ll.B (law with data science) from National Forensic Sciences University, Gandhinagar.