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AI is here to stay! Is the legal industry ready with its building blocks?

By Rasmeet Charya.


Artificial Intelligence (AI) is here to stay, whether we like it or not. The pace of innovation has been very slow in the legal industry. The AI-enabled technologies are geared to drive innovation at a much higher scale and speed, undoubtedly unbundling and unfolding a huge opportunity within the legal space to enhance professional capabilities coupled with the potential to streamline legal operations, enhance efficiency, and provide valuable insights.


But let us understand if the legal industry is sorted enough when it comes to managing its data and streamlining its processes. These two are the building blocks for making effective and impactful use of AI. How will we use AI if the building blocks or fundamental frameworks are missing?


Having worked on AI training assignments, I feel several building blocks still need to be developed and strengthened for successful AI implementation. Here is my take on pre-AI essentials that legal professionals need to be ready with before they think of implementing and using AI for desired results:


1. Data Structuring and Integration


The legal industry generates huge volumes of data, but much of it is unstructured, stored in different formats, and dispersed across various systems. To effectively implement AI, there is a need for standardized data formats and better integration of data sources. Developing common data standards and protocols can enable seamless data sharing and analysis across different legal systems and organizations.


2. Data Quality and Annotation


AI models require high-quality training data. In the legal domain, ensuring the accuracy, reliability, and relevance of training data is challenging. Annotating legal documents, court decisions, and case laws for machine learning purposes is a very time-consuming and resource-intensive task. Establishing guidelines and processes for data annotation, as well as using tools for efficient data labeling and verification, can contribute to improving the quality of training data. It is challenging indeed to bring data quality and annotation within the existing systems, and that is where the legal process designing can help lawyers, law offices, and law departments.


3. Explainability and Interpretability


AI algorithms often operate as black boxes, making it difficult to understand how they arrive at specific outcomes or recommendations. In the legal industry, explainability and interpretability are essential to ensure transparency, accountability, and trust in AI systems. It is therefore important to simplify the language of our documents, clearly define processes, define parameters, categorize, provide clear instructions, adequate reference text, and create cheat sheets that can define and have control over the outcome we are expecting from AI.


On the other hand, it will be pertinent to develop AI models/tools that will provide explanations for their decisions and generate outputs that can be understood and scrutinized by legal professionals.


4. Document Templatization

As a standard practice, taking the time to prepare your templates before undertaking any legal projects or at the time of implementing any legal tech tools such as CLM solutions can yield significant benefits in the long run. It establishes a strong foundation for automation, ensuring consistent document output, reducing errors and review time, and enhancing efficiency within your document/contract management process or for delivery of any legal services for that matter.


5. Data Ethics

In the existing legal tech space, mostly, there is close to no data available in the public domain that can be used to train AI from the legal industry perspective. This results in either using in-house documents or templates which is a tedious, time-consuming exercise, or resorting to unethical use of data.


The legal industry operates within a framework of laws, regulations, and ethical standards. As AI continues to advance, there is a need to develop ethical standards and guidelines for data mining and usage for AI training purposes, create standard legal data banks of contracts, compliance, case laws, etc. in collaboration with legal bodies globally that can be used by organizations and the legal tech providers to train AI ethically, ensuring compliance. Recently, 273 Ventures introduced Kelvin Legal DataPack - a dataset containing over 150B tokens of foundational legal, regulatory, and financial text that can be leveraged to support organizations across their AI journey.

6. Data Sustainability


AI can play a significant role in enhancing data sustainability. The legal industry is a huge contributor to the global data explosion leading to immense data storage requirements impacting our climate. This can be controlled by improving data management practices and reducing data duplicity and waste within our legal practice and work. In addition, we can leverage AI to facilitate, automate, monitor, and optimize legal data lifecycle management, resource consumption, ensure data sustainability and enable more informed decision-making.

7. Bias and Fairness


AI algorithms can inherit biases present in the data they are trained on, leading to potentially discriminatory outcomes. In the legal industry, fairness and impartiality are paramount. It is important to address bias in our training data, develop methodologies to mitigate bias in AI systems and ensure that AI does not perpetuate or amplify existing inequalities within the legal system.


8. Data Privacy and Security


Data privacy and security are critical concerns in the legal industry, given the sensitive and confidential nature of legal information. Implementing robust data privacy measures, encryption protocols, and access controls is crucial to protect client confidentiality and maintain the integrity of legal proceedings. It is important to ensure compliance with relevant data protection regulations, such as GDPR or CCPA, to ensure that no client, confidential data/ information is fed into AI engines, all data needs to be sanitized or consent taken from the data owner before use to train AI. Proper guidance and policy need to be created on the use of data specifically for automation or AI training and implementation.


Conclusion & Futuristic Approach

I discussed what the legal industry needs to do to adapt and adept to AI. Creating a solid framework for the implementation of AI requires a consistent, organizational collaboration between legal professionals, technologists, and business teams. While policymakers and regulatory bodies are working at their end to address the larger legal industry challenges, legal professionals and practices can employ simple methodologies at their level and actively work to develop these building blocks to harness the benefits of AI. Legal professionals can take the help of legal tech consultants, legal design, and legal operations specialists to bring about changes to their existing systems to be AI-ready.

 

About the Author

Rasmeet Charya is a strategic advisor on legal innovation and technology, an Indian and UK lawyer by qualification, with 23+ years of experience across the legal industry - litigation, law firm, ALSP, corporations, compliance, and risk advisory.

She is a legal innovator with hands-on experience in developing and implementing transformation, innovation, and technology across organizations in her various roles as Chief Innovation Officer, Strategic Advisor, Head of Product Innovation, and many more.


She is part of global legal tech and innovation forums, a speaker, and a thought leader. With a deep desire to contribute to the legal ecosystem, she actively lectures, designs, and conducts legal tech and innovation courses for law schools to equip law schools and future lawyers with legal tech and innovation expertise. #RasmeetCharya #AI #legal #innovation #digitaltransformation

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