Understanding Human-in-the-Loop AI

The field of Artificial Intelligence (AI) has witnessed remarkable progress in recent years, revolutionizing various industries and empowering businesses with automation and intelligent decision-making. Human-in-the-Loop AI is an innovative approach that recognizes the complementary strengths of human intelligence and machine learning algorithms. In this blog post, we will explore how Human-in-the-Loop AI is reshaping contract review, offering enhanced productivity, improved accuracy, and empowering users with a more efficient workflow.

Defined

Human-in-the-loop is a technique that combines supervised machine learning and active learning, involving humans in both training and testing an algorithm. This continuous feedback loop leads to improved results with each iteration. It can be used in various AI projects, such as natural language processing, computer vision, and transcription. In contract review, human-in-the-loop decision-making involves AI flagging potential risks, and human reviewers examine and confirm the flagged items to enhance the algorithm's decision-making. The benefits include increased accuracy, enhanced data collection, reduced bias, and improved efficiency, as humans fine-tune the model, interpret context, and correct biases in AI systems. This collaborative approach ensures better AI performance while considering human expertise and judgment.

Traditional Approach: Third-party-in-the-Loop

In the past, extracting data from contracts required labor-intensive tasks, such as manual data tagging, identifying clauses, and managing contract repositories. Early AI systems lacked the contextual understanding necessary for precise data tagging and decision-making. To overcome this limitation, some contract management vendors enlisted human reviewers to rectify errors made by AI models. Although this approach enhanced accuracy, it also gave rise to concerns regarding breaching confidentiality clauses, violating data residency laws, and compromising legal ethics. Relying on unvetted and unlicensed humans for contract review can violate privacy regulations and legal licensing requirements.

Modern Approach: User-in-the-Loop

Today, human-in-the-Loop is centered around enhancing user experience and productivity by leveraging advanced AI models like Large Language Models (LLMs). LLMs possess ability to understand context and generate human-like responses, reducing the need for explicit data tagging by users. This approach empowers users to actively participate in the contract review process, making them more confident in the outcomes and decisions.

Advantages of User-in-the-Loop:

• Streamlines contract review, reducing manual efforts and enabling users to focus on high-value tasks, such as strategic decision-making.
• Ensures higher accuracy in data tagging and clause identification, minimizing errors and potential legal risks.
• Empowers users to actively participate in the contract review process, making them more confident in the outcomes and decisions.
• AI models continuously learn from user feedback, becoming more refined and better aligned with user preferences and requirements.
• Transparency in the AI contract management process addresses privacy concerns, data residency regulations, and legal ethics, instilling trust and confidence.

Challenges and Future Considerations

While Human-in-the-Loop AI offers significant benefits, challenges such as ensuring data privacy, maintaining data quality, and managing user biases require careful consideration. At Pincites, we take each of these considerations seriously by maintaining transparency with our customers and providing continuous insights for our customers to interact with and improve their models.

If you’re curious to learn more about how Pincites uses LLMs to power contract negotiation, join the waitlist and we’ll schedule time to help you out.