Over the past two quarters, there’s been a marked increase in interest related to artificial intelligence. Technology vendors and industry pundits have made far-reaching claims about AI’s long-term potential and immediate benefits.
That said, many of these assertions about AI blur the line between state of the art and science fiction. That’s particularly true with respect to private markets’ legal use cases.
For internal legal teams to avail themselves of the tremendous potential AI offers, they need to understand the underpinnings of AI and its associated strengths and current weaknesses.
This and upcoming articles will provide readers with the understanding they need to:
- Ask the right AI-related questions,
- Evaluate purported AI capabilities, and
- Decide how AI can bring the greatest positive impact to their work.
AI is a broad field focused on the creation of machines capable of performing complex tasks that typically require human intelligence, such as understanding and generating language and making decisions.
When talking about AI, it’s important to understand the various technologies it encompasses. AI depends on machine learning, which gives machines the ability to learn from experience, without being explicitly programmed by humans. Machine learning uses algorithms and statistical models to analyze and learn from patterns in data.
Natural language processing is a field of machine learning in which machines can understand language as people speak and write it, which enables machines to recognize, understand, translate, and generate text.
AI developers commonly use neural networks, a specific class of machine learning algorithms inspired by the human brain. These networks contain numerous interconnected neurons. Each neuron performs a specific function, processing inputs and producing outputs that it sends to other neurons. Deep learning networks are multi-layered neural networks that can learn to approximate almost any function.
Additionally, large language models are a type of deep learning neural network that can perform natural language processing tasks. These models are referred to as large because of the number of parameters in the model (possibly in the billions) and the amount of data involved.
How AI works
AI systems work by using algorithms to analyze data and identify patterns and relationships in that data. Developers can train algorithms by using machine learning techniques, which involve providing the AI system with large amounts of data and adjusting the system’s parameters until it can accurately perform a given task on new data.
The success of an AI application depends on two components:
- Its models, the systems used to learn from data, and
- The volume and quality of the data the developers use for training.
Models: Models can be either open source or closed source (also known as proprietary). Open source code is generally available to the public and, depending on the license, parties may be able to access, modify, and distribute the model royalty free. Proprietary models may contain open source code but rely on private source data to deliver unique capabilities. Only authorized parties may be able to access these models.
Data: During the training process, models are exposed to large quantities of labeled or unlabeled data to learn how to perform specific tasks. These datasets can also be either open source or proprietary. The higher quantity and quality of the dataset used to train a model, the higher the quality of the final model.
A model’s accuracy depends on the volume and relevance of the training data used. For example, if a model is trained to recognize a standstill clause in a non-disclosure agreement using an open-source data set derived from internet searches, the model would yield inferior results compared to one trained on an extensive NDA data repository.
Ontra Synapse differentiates itself from conventional AI by using proprietary models designed to generate answers specific to private markets’ legal workflows. Most importantly, we train these models on Ontra’s unique data set — the industry’s leading repository of more than 800,000 documents. These factors allow Ontra Synapse to generate outputs that outshine any other legal AI solution in terms of relevance and accuracy.
Different types of AI models
AI models are broadly classified as predictive or generative.
Predictive models make decisions or predictions about future outcomes (for example, predicting the complexity of an upcoming contract negotiation) by identifying patterns and trends in data.
Predictive models can deliver consistent, accurate results when trained on high volumes of relevant information. They can be used to automate many manual tasks that require minimal human oversight. However, the quality of their outputs declines precipitously with poor training data.
Generative models create unique text, images, audio, and synthetic data (for example, drafting a legal clause) by mimicking content they have previously analyzed.
Generative models allow legal professionals to tackle use cases that require context-specific text-based responses. Unfortunately, these models have two considerable shortcomings. First, they’re prone to hallucinating — fabricating baseless assertions that they present as fact. And second, generating inconsistent answers to the same sets of questions. For these reasons, generative AI requires human fact-checkers — professionals familiar with the subject matter and the way in which the organization will use the AI outputs.
Ontra uses a blend of industry-specific predictive and generative models. To ensure model outputs meet the exacting standards of the private funds industry, we use the highest quality training data derived from our industry-leading routine contract repository. We also employ a global network of highly trained contract lawyers to review the information those models produce.
How to measure the quality of AI outputs
Whether the legal and private funds industries can benefit from predictive and generative models depends on the quality of the outputs. Organizations can use numerous metrics to evaluate an AI model, including recall, precision, and F1 scores.
Recall attempts to measure the proportion of actual positives a model correctly identifies. For example, of 100 contracts, if a model predicts 90 contracts contain standstill clauses when, in fact, 100 do, then recall equals 90%.
Precision refers to the accuracy of a model’s predictions and is calculated by dividing the number of true positives by the total number of predicted positives (both true and false). For example, of 100 contracts, if a model predicts 100 contracts contain standstill clauses and only 60 actually do, then the precision is 60%.
An F1 score combines precision and recall into one blended metric.
While recall, precision, and F1 scores are most helpful in determining the quality of a predictive AI model’s outputs, these metrics may be less helpful when measuring generative AI outputs. That’s because opinions may vary when it comes to assessing the quality of a legal clause drafted by AI.
To overcome the challenge of measuring the quality of generative model outputs, machine learning engineers can monitor how frequently subject matter experts (for example, the lawyers who use AI tools) accept the outputs and the degree to which these professionals modify them.
Since no single measurement can effectively represent all facets of an AI solution, Ontra Synapse uses a blend of metrics to ensure model outputs are reliable, relevant, and consistent.
Should I trust AI?
Given mounting legal demands, private markets firms will need to rely more heavily on AI to automate and optimize today’s manual legal workflows in the coming years.
Firms can do so with confidence as long as they understand the technology’s limitations and work with reputable vendors that have sufficiently addressed these issues. As discussed previously, anemic training data can undermine the quality of any AI output. Additionally, hallucinations and non-deterministic (i.e. inconsistent) outputs are two of the undesirable byproducts of generative AI models.
Fortunately, vendors that have sourced large, use case-specific data sets and employ subject matter experts to monitor quality are capable of delivering transformational outcomes with minimal risk. For that reason, Ontra has invested in building a complete AI solution comprising world-class data, industry-specific models, and a global network of lawyers.
The bottom line
- Ontra developed a proprietary blend of predictive and generative AI models tailored to private markets’ use cases.
- Ontra trains its models on anonymized and aggregated data from over 800,000 industry-related negotiations.
- Ontra’s global legal partner network addresses common AI limitations by reviewing the accuracy and relevancy of the model’s outputs and providing feedback to further train out models.
Continue to explore legal AI
Over the next several weeks, we’ll dive deeper into the topic of legal AI for the private markets. In future posts, we’ll explore the power of the human-in-the-loop model and provide insights on how private equity firms can evaluate and use AI to gain a competitive advantage.