There are two main types: Narrow AI (also known as Weak Ai) and General AI (also known as Strong AI). In this article we will look first at the Narrow AI.
What is Narrow AI?
Narrow AI, also known as Weak AI, refers to AI systems designed and trained for specific tasks or narrow domains of expertise. Unlike General AI, which aims to mimic human cognitive abilities across a broad range of tasks, Narrow AI focuses on performing well-defined functions within set parameters.
How Narrow AI Works
Narrow AI operates through machine learning algorithms that are trained on specific datasets relevant to its designated task. These algorithms analyse data, identify patterns, and make decisions or predictions based on the learned information. The training process involves feeding the AI system with large amounts of relevant data to improve its accuracy and performance over time.
Two branch types
In Narrow AI, there are two primary branches: rule-based AI and example-based AI.
Rule-based AI operates on a set of predefined rules created by experts in the field. These rules dictate how the AI system should interpret data and make decisions, making it particularly useful in environments where the logic can be explicitly defined and is less prone to ambiguity. Example is Email Rules – if email arrives from john@john.com, send to “John” folder and mark as urgent.
On the other hand, example-based AI, also known as machine learning, relies on training algorithms using large datasets. Instead of following explicit rules, the system learns from examples, identifying patterns and making predictions based on the data it has processed. This approach is highly adaptable and excels in tasks requiring pattern recognition and data analysis, such as predictive analytics and natural language processing. Both branches offer unique advantages, with rule-based AI providing precision and clarity, and example-based AI offering flexibility and the ability to handle complex, unstructured data. Example is Spam Email Filtering, algorithms analyse patterns in emails to classify and filter out spam messages based on previous examples of spam and non-spam emails.
Examples of Narrow AI in Legal Practice
Legal Research and Document Review:
Narrow AI-powered tools can sift through vast legal databases to extract relevant case laws, precedents, and statutes. For instance, platforms like ROSS Intelligence use natural language processing (NLP) algorithms to streamline legal research tasks, enabling lawyers to access pertinent information swiftly.
Contract Analysis and Due Diligence:
AI applications such as Kira Systems utilise machine learning to analyse and extract key clauses and provisions from contracts. This capability aids lawyers in conducting due diligence more efficiently, identifying potential risks, and ensuring compliance with legal requirements.
Predictive Analytics for Case Outcomes:
AI algorithms can analyse historical case data to predict the likelihood of success or failure in litigation. Companies like Lex Machina offer predictive analytics tools that assist lawyers in making informed decisions about case strategies and settlement negotiations.
Legal Chatbots:
Narrow AI-powered chatbots can provide initial client intake, answer frequently asked questions, and schedule appointments. These bots improve client interaction efficiency and help manage routine inquiries, freeing up lawyers’ time for more complex tasks.
E-discovery and Data Management:
AI-driven e-discovery platforms like Relativity leverage machine learning to sift through massive volumes of electronic data. This capability facilitates quicker identification of relevant documents and reduces the time and costs associated with litigation preparation.
Benefits and Challenges of Narrow AI in Law
Benefits:
- Efficiency: Automates repetitive tasks, saving time and reducing operational costs.
- Accuracy: Enhances accuracy in document analysis, research, and predictive analytics.
- Scalability: Scales to handle large datasets and complex legal tasks effectively.
Challenges:
- Ethical Considerations: Issues related to data privacy, bias in algorithms, and accountability.
- Integration: Adoption and integration of AI tools into existing legal workflows.
- Legal Interpretation: AI’s ability to interpret nuanced legal concepts and context-specific information.
Narrow AI presents significant opportunities for lawyers to enhance productivity, improve decision-making, and deliver more efficient legal services.
