In the past decade, there has been significant debate around the design, development, and implementation of artificial intelligence (AI) (AI). For instance, in India, the NITI Aayog recently published an approach paper on the need to harness AI in a responsible and ethical manner. The Indian judiciary, which has already put in place basic information and communication technology infrastructure through the eCourts Project, is now looking to take advantage of AI's potential.
Given that the foundations of an ecosystem are currently being laid, the Supreme Court should ideally steer the discussions surrounding such a roadmap. Its production could be haphazard and uncoordinated if there is no such supervision. Recognizing this goal and assisting the judiciary in achieving it, this section lays out a broad agenda divided into three sections: conceptualizing AI integration in the justice system, establishing operational support to enable such integration, and deploying AI in the justice system.
THE INTEGRATION OF AI IN THE JUSTICE SYSTEM: CONCEPTUALIZING IT
The most important task for the Supreme Court's AI committee is to determine the short, medium, and long-term applications of AI. This exercise must define clear ground ethical rules for AI design and deployment that are both responsible and ethical. Simultaneously, it must determine the judiciary's logistical capacity to integrate such technologies (for instance, how can judicial data be archived and made more openly accessible). The conceptualization stage must involve, among other things, the following:
a. Adopt a governing charter that lays out core principles for ensuring due process, civil and legal rights, and addressing concerns about openness, bias, and accountability in AI-driven technologies. The Institute of Electrical and Electronics Engineers (IEEE), for example, has developed Four Principles for the Trustworthy Adoption of Artificial Intelligence in Legal Systems. Also, the European Commission for the Efficiency of Justice (CEPEJ) has produced an ethical Charter for the use of AI in judicial systems.
b. Consult with a wide range of stakeholders to ensure that the legal community is kept informed and confident throughout the process. It is critical that the lessons learned from the COVID-19 pandemic inform future judicial and justice system digitization efforts. Importantly, different stakeholders' buy-in is required, and input on the scope, potential uses, and challenges that will be encountered during the AI integration process must be sought. The AI Committee, in its expanded form as mentioned below, can be responsible for establishing these channels of engagement. The AI Charter may be the first text to be discussed.
c. To resolve the crucial challenges that AI poses, promote research on AI governance for the justice system. High-quality, interdisciplinary research evidence is needed to help policymakers make more informed decisions about the incorporation and use of emerging technologies. In this regard, the AI Committee should encourage and commission independent research studies, with an emphasis on the Indian background and grassroots realities. While there is some international literature on similar topics, India's social and cultural contexts are highly subjective, necessitating indigenous research.
d. Plan for capacity building through adequate training and skill development. A key deficiency that typically impedes the scalability of automation is the absence of accompanying capacity building. Inadequate training can also raise suspicions about the technology intervention and erode trust in it even before it is implemented. AI is an advanced technology, despite its complexity. It is critical in a country like India to train not only the users of such technology but also the recipients. This multi-stakeholder educational project will be complicated, and it will necessitate careful preparation before implementing such technologies.
IMPLEMENTING OPERATIONAL SUPPORT IN ORDER TO ALLOW INTEGRATION
The practical implementation of AI-driven justice technologies would necessitate careful preparation. There are three major actions that the judiciary should take in particular:
a. Increase the size of the Supreme Court's AI Committee to oversee AI's incorporation into the justice system. A core group of current or retired Supreme Court or High Court judges should make up this enlarged AI Committee. It should be led by a sitting Supreme Court Judge, as it is now. Expert members such as technologists (with a focus on AI design and innovation), ethicists, policy analysts, and scholars should all be inducted on a professional basis. Even if some of these people are still on the commission, they aren't multi-dimensional or completely engaged. The core group of judges should determine the rules of their engagement, such as eligibility, remuneration, tenure, and the number of expert members. Instead of a body that meets on a regular basis to discuss this agenda, the goal is to have a permanent entity that is solely focused on the task of AI integration.
b. Publish openly accessible datasets as they are sine qua non for any meaningful AI innovation. As discussed earlier in this paper, presently, both the judiciary and other wings of the justice system collate and archive a huge amount of data. However, there are no overarching protocols that dictate the sharing and usage of these. The AI Committee can consider a data trust created by the Supreme Court for the collection, storage, and sharing of non-sensitive judicial data which can aid innovation of ML (machine learning) algorithms while safeguarding the public interest. The data trusts should subscribe to the data security laws that have and will be enforced in India. The data trust can be established in a variety of institutional forms, such as as a non-profit corporation. The best organization for performing core functions while upholding a fiduciary duty to the public interest must be chosen for incorporation.
Furthermore, the judiciary should consider developing an open data policy for data access that preserves personal privacy while encouraging technologists to maximize the value of existing datasets. The development of viable open databases necessitates the judiciary partnering with reliable partners who can help with the method. The development of viable open databases necessitates the judiciary partnering with reliable partners who can help with the method. The Supreme Court's e-Courts Committee should do this in coordination with the AI Committee.
c. Leverage public-private partnerships (PPPs) to develop and implement AI-based solutions. A public-private partnership (PPP) model that includes private social enterprises will maintain state oversight while also using the private sector's capital and expertise. Until now, the National Informatics Centre (NIC) has been the primary entity in charge of India's overall digitization of public sector institutions over the last two decades. However, as with developing AI for the justice system, designing and developing public-centric AI would be an expensive and logistically difficult endeavor.
DEPLOYMENT OF AI IN THE INDIAN JUSTICE SYSTEM IN STAGES
The actual implementation of AI must be achieved in phases to achieve the twin-pronged goals of enhancing administrative performance and decision-making processes. This will include testing AI interventions, monitoring their success, and iteratively improving these first-generation technologies. Instead of being used haphazardly and piecemeal, AI should be deployed in a scheduled and gradual manner. The following are key points that must be applied for this phased design and deployment:
a. The first round of AI pilots is already underway, with SUVAAS and SUPACE (an AI tool to assist judges in legal research) being evaluated in various courts under the AI committee's auspices. In addition, AI innovation will expand on this to develop more machine learning and deep learning algorithms to automate more processes. As previously mentioned in section I of this paper, the emphasis of first-generation AI tech interventions may be on improving administrative performance. This will necessitate identifying the various administrative procedures that occur during the life cycle of a case and prioritizing which of these can be fully automated (such as scheduling hearings) and which are proving to be bottlenecked (like issuing summons). If found, narrow, machine learning algorithms can be used to automate, streamline, and make simple court processes more effective.
b. To learn what works and what doesn't build feedback loops, and effect assessment structures. It is important to ensure that pilots and first-generation AI approaches are independently tested in order to improve future generations of AI for the justice system. Expert technology auditors must perform the impact assessment, which will be done under the auspices of the AI committee. Furthermore, such oversight must be ongoing rather than one-time in order to ensure that the technology implemented is of the highest quality and adheres to ethical best practices as they develop and grow within the wider debate on AI governance.
c. Through incorporating more advanced AI technologies such as case query methods, intelligent analytics, analysis augmentation, analytical tools directly aiding in judicial decisions, and legal robotics, the second generation of AI tools will target decision-making processes. As stated earlier in this paper, improving decision-making is a major motivation for AI adoption. Judges may benefit greatly from intelligent case query software or algorithms that collect and analyze case law and theoretical legal analysis. These tools would not only speed up judicial decisions, but they would also, arguably, ensure a thorough analysis of current precedent, which is more time-consuming and laborious when done manually. In addition to lawyers and judges, litigants can benefit directly from this step of AI growth by having better access to basic legal resources and an interactive archive of rudimentary legal knowledge. It's worth noting that the second wave of AI innovation for the justice system would benefit from the rapidly expanding field of AI, and could also use more sophisticated techniques than machine learning.
During this process, there will be two concurrent programs: one for judges and attorneys and the other for the general public and future litigants. Some of these "narrower" analytical methods would also help in the creation of more nuanced and reliable tools for aiding predictive justice, as previously mentioned. This type of advanced artificial judicial intelligence integration will be critical in transforming India's justice system. The easier use cases within the judiciary could concentrate on task-specific ML algorithms through pilot programs aimed at determining their effectiveness, ease of use, and possible flaws that need to be addressed before scaling or branching out into other applications.
Though achieving the twin goals is a good place to start for the Indian justice system's AI journey, AI's ability to effect change is transformative. The AI committee will need to review this roadmap in the medium to long term (3 years) and identify a new set of next steps that cater to and are sensitive to the changing needs of India's justice system. However, the production of India's AI ecosystem must remain accessible to all innovators in the Indian start-up ecosystem, based on the creation of a set of standards tailored to the Indian justice system.
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