Data annotation or labeling plays a vital part in transforming content into recognizable formats for machines. In other words, tagging or labeling facilitates a system to transform data into more intelligent outputs/insights. It means raw data becomes intelligence using data annotation. And, this is what enables machine learning (ML) models to decipher and distinguish between various content inputs and convert them into meaningful output. Case in point, many of the promising advancements in AI that we witness today, such as robot surgeons, self-driving cars, virtual assistants, virtual tutors, and chatbots, are a reality today, thanks to the integration of data annotation in the fundamental workflow.

What are the most common data annotation types that AI leverages

  • Semantic annotation: This is a process by which people, places, or company names are labeled within a text. It helps machine learning models categorize new concepts in future texts.
  • Image annotation: This is a process by which the machine identifies the annotated areas as distinct objects. It uses bounding boxes to do this.
  • Video annotation: This also uses the bounding boxes concept to factor in movement, similar to image annotation. It is done frame by frame.
  • Text categorization: Assigns categories to sentences or paragraphs by topic within documents.
  • Entity annotation: This allows the machine to comprehend unstructured sentences.

Bounding boxes, lines & splines, semantic segmentation, 3D cuboids, polygonal segmentation, landmark & key-point, content, and text categorization are some of the other related data annotation types that are often used to help a machine recognize text, images, and videos (objects) via computer vision. This process of labeling texts, images, and other objects helps ML-based algorithms improve output accuracy and provide a superior user experience.

How the AI and Machine Learning are redefining legal operations?

The legal industry known for its conventional approach has been hesitant to adopt AI. The price of adoption, lack of proper systems and laws governing data have all come in the way. However, the winds of change have blown in the direction of legal, steering the clerical and routine tasks towards AI. Now, AI-based software is tapped to review documents to establish its relevance to a given case.

According to a survey of nearly 100 law firms by real estate giant CBRE, 48% of firms already use AI software in their businesses, and 41% have immediate plans to implement the same. The survey also found that 61% of the companies use AI to generate and review legal documents, 47% for due diligence purposes, and 42% for research.

The below are some of the prominent ways in which AI-powered legal is redefining the current legal landscape.

  1. Effective Legal Strategy: Enormous in-depth analysis and research of past case histories guide how lawyers navigate the cases. E.g., how often similar cases have been rejected, which types of cases have won in what courts and related critical case information are now easily accessible.
  2. Client Relationship Management: Again, with intelligent data as ammunition, organizations can advise clients on the multiple dimensions of their case. E.g., how long it will take for the case to be completed, how much it would cost, chances of winning, which judges are likely to chair the bench, and so on.
  3. Proven Branding Strategy: The stakeholders are attracted to the AI-powered legal intelligence that firms offer. It is a significant branding mechanism to attract and retain existing and prospective clients and employees. The innovation and holistic information draw them that legal tech offers.
  4. Swift Due Diligence: Lawyers can reduce the time and unbillable hours reviewing memos and briefs and confidently deliver 100% validated documents. AI-based document analyzing tools enable organizations to provide accurate contract documentation.

That being said, several new-age legal tech startups have pioneered to offer simple but effective services such as:

  • Connecting clients with the best lawyers available in their preferred location
  • Providing clients real-time updates, information sharing, and assistance
  • Helping customers draft, review, manage, and sign contracts
  • Analyzing legal documents and suggesting clauses that can be negotiated
  • Offering holistic research findings and in-depth analysis
  • Understanding search queries and presenting appropriate recommendations

AI/ML-enabled legal solutions are indeed a great boon to law professionals. With time, their existence in the legal space will be ubiquitous, playing a vital role in steering every process and effort in the right direction. However, contrary to the prevalent belief that AI will replace lawyers leaving them jobless, it will actually work in close collaboration with legal experts to reach its true potential.

Human expertise is fundamental in building an AI platform’s capability, by converting raw and unstructured data into valuable training datasets. Alternative Legal Service Providers (ALSPs), such as Cenza, have been enabling legal AI solutions to accelerate their learning curve through human-in-the-loop annotation services. Not just that, the role of human-in-the-loop services in legal space expands to include abstraction, annotation, and classification as well as identification of concepts & boundaries, obligations, and enforcement across legal and financial documents.

To know more about the AI/ML training services using humans in the loop, or talk to our experts.


About the Author: Raja

Raja leads the Sales and Marketing at Cenza and his job involves creating strategy for the day to day sales and marketing activities, and focused on optimizing the sales for Contract Migration, AI and ML training, Lease Management, and other managed legal services. Also help CLM providers with cost-effective solutions for contract extraction and migration needs.