Intelligent Settlement System in Law

The applications of Artificial Intelligence in law will transform legal practice and the field of law. As of May 2022, over 4.7 crore cases are pending in courts across different levels of the judiciary. Of them, 87.4% are pending in subordinate courts, 12.4% in High Courts and nearly 1,82,000 cases have been pending for over 30 years. The lack of transparency makes accountability even more difficult. From facilitating legal research to adjudicating disputes, AI in the legal system will resolve most of the challenges that the judicial bodies are facing today. 

In every civil proceeding lies a background of settlement negotiation. Both the parties to the suit bargain for damages or injunctive relief in case of any dispute. There are settlement tactics including negotiating with the plaintiff’s counsel, releasing evidence, counter offers etc. The outline of Analytics of settlement is divided into intuitive dispute settlement, predicting settlement value and analytics of performance. Mathematically; settlement point = total settlement offer – defendant first offer/plaintiff first offer – defendant first offer. Usually, the settlement point is closer to the defendant’s offer because of the defendant’s Bank balance and higher-unrealistic expectations of the plaintiff. 

Intuitive dispute settlement faces problems of decision-making errors, bias, and noise. Organisations process a significant number of claims, disputes, complaints and customer reviews in which managers/lawyers resolve disputes intuitively. Without data, decision-making is just personal opinions or biases or noise leading to decision-making errors. There have been several types of research where judges are more lenient after taking a break and when a judge's football team loses, the juvenile sentences go up, comparatively. Oliver Wendell Holmes, Jr says that the law’s long-held aspirations are the prophecies of what the courts will do in fact and nothing more pretentious are what I mean by the law. 

A study reveals that 44 courts and the national justice council use artificial intelligence in Brazil. Estonia is building a robot judge to help clear legal backlog and Beijing is bringing AI judges to the court. Beijing Internet Court has launched an online litigation service centre featuring an artificially intelligent female judge with a body, facial expression, voice and actions all modelled off a living, breathing human (one of the court’s actual female judges to be precise). Machine Learning is already used in Tax Law, to predict the decision of the European court of human rights. In China’s Smart Court Initiative, the Judges' e-assistant platform has text or image recognition and analysis to automatically index scanned litigation materials, generate legal docs and summaries, and use similar case push systems to improve judge efficiency. Also, they are using analytics to hold judges accountable and promote standardization of the judicial system. 

BlueJ Legal’s AI-powered software is saving time on research and building stronger analytics by accurately predicting legal outcomes in challenging areas of law. AI-based research benefits lawyers and judges. MyOpenCourt is an AI-based tool which determines whether a party shall pursue legal action, determine typical case outcomes and gets its users in touch with a lawyer. There are several things possible with Artificial Intelligence in the field of law such as: 

  • Add intelligence to your contract drafting, vetting and due diligence. 
  • Modernise document processing, storing and reviewing. 
  • Personalise your customer experiences by creating a database accessible to all the parties to the dispute and contract. 
  • Find accurate information faster. Lawyers spend countless hours and days preparing for cases and finding legal precedents. Several legal search engines around the world have addressed this problem but Artificial Intelligence will solve this problem. 
  • AI will make data processing methods accessible and understandable as well as authorise external audits. 
  • Speed up document review, digest e-databases, and examine agreements. 
  • Provide predictions, options, assistance in cases and bargaining strategy.

The future of alternative dispute resolution is online dispute resolution and dispute resolution based on data. Data is the fuel of digital transformation, and this is incredibly true in times of economic uncertainty and downturn. Data Analytics eliminates guesswork and manual tasks. According to McKinsey & Co., almost two-thirds of leading organizations believe that creating data-rich platforms is one of the best ways they can future-proof their business. The AI-based legal system will be based on data. Such data will be an amalgamation of anchor effect, plaintiff demographic, nature of the injury/injuries, the severity of the injury/injuries, important dates, estimated litigation risk, co-defendants & contributory negligence, plaintiff’s or defendant’s first offers, total settlement amounts, age of plaintiff or defendant etc. 

The AI-based system will commence with the question of law, determine critical factors, impartial statement of facts, processing legal materials, apply tests, refine algorithms, monitor and process new rulings. This will constrain legal costs, limit needless litigation, provide optimal settlement, entertain authentic-substantive disputes, encourage confident social and business planning, and support laymen with a real-time sense of their legal rights and obligations. 

Online Dispute Resolution is much more than the ‘use of information and communications technology to help disputants find a resolution to their disputes.’ Today, it is ‘the practice of using technology to prevent, manage and resolve conflict by facilitating communication and support decision making.’ Decision-making plays a vital role here which is supported by data analysis. Such data analysis is possible when we build data warehouses or store data related to ADR. This Data Science can be divided into automation, insights, reports and correlations. Automation is leveraging the power of algorithmic analysis to streamline organisational efficiency and enable the automation of workflows. Insights shall deepen the understanding of one’s business by using data to identify strengths and weaknesses across organisations. Reports will empower management with real-time reports on key performance indicators including filing, closure, efficiency and profit. Correlation discovers relationships across caseloads that were previously hidden, including top neutrals and most valuable case types. There are several opportunities for AI in online dispute resolution such as: 

  • Translations, voice-based assistance for improving accessibility, chatbots, categorise their legal issues, navigate and connect with providers. 
  • Guided interviews that assist self-represented litigants (SRL), assemble documents and fill out forms.
  • AI could use the bed to structure and clean data, extract, reduction etc. 
  • Providing case research tools to SRL’s probability of outcomes, success rate etc. 

AI will be the fourth party to a dispute. A portable, cheaper and more productive technology that will run diagnoses, process payments, administrate and manage cases, provide notifications, manage documents, schedule and assist in negotiations. In the future, this fourth party will research, evaluate cases, coach clients, draft and submit documents, automate negotiations and reframe and enforce cases. Although, these will be segregated by the third party meaning the lawyers, ODR service provider etc. Lex Machina provides legal analytics where raw data is collected and processed to extract structures, and consistent data about all facets of litigation and their legal experts further refine high-value data and algorithms to mine and moralise basic data into higher-level concepts. These concepts can be accessed by judges and courts, law firms and attorneys and parties.

Therefore, humans will be in the loop of these stages of AI. A loop is a system or process by which invaluable data is generated, managed and leveraged throughout an organisation. In the loop is when human involvement is required for the process to occur. On the loop is where machines do the bulk of the work and human involvement becomes a check to ensure processes are running normally and to verify accuracy. Out of the loop is no human involvement. Machines have become accurate and self-sufficient to continue operating independently. 

There must be a balance between legal reasoning and technology. Overestimation of technology because of its benefits in providing a better system may not be the right approach. The way in which AI can be used by legal decision-makers is still very unclear. The better is the dataset structured, the better will be the prediction of settlement. AI will work best with specific legal issues that have high volume and relatively homogeneous fact problems. In today’s world, after searching for something on Google, we make absolutely no effort to cross-check it. We use the information available on Google as the ultimate truth. What if, the judges adopt a similar behaviour while adjudicating cases where AI-based predictions become their decision’s reasoning? AI-based legal systems cannot be second-class justice, inaccurate or discriminatory. Certain principles of accountable algorithms have to be formulated which include an obligation to report, explain or justify algorithmic decision-making as well as mitigate any negative social impacts or potential harms. The principles of accountable algorithms shall be responsible towards its users and have explainable terms, accurate standards, auditability services and fairness.