How can you ensure the transparency of AI-generated contracts?

AI-generated contracts are becoming more common in the field of contract management, especially for ERP projects that involve complex and dynamic requirements. However, AI is not a magic bullet that can create flawless and fair contracts without human oversight. In fact, AI can introduce new risks and challenges for contract transparency, such as bias, opacity, and accountability. How can you ensure the transparency of AI-generated contracts and avoid potential pitfalls? Here are some tips to help you.

1 Define your objectives

Before you use AI to generate contracts, you need to have a clear and specific idea of what you want to achieve and how you will measure it. For example, do you want to reduce contract cycle time, improve compliance, or increase customer satisfaction? How will you evaluate the quality, accuracy, and fairness of the contracts? What are the key performance indicators (KPIs) and metrics that you will use to track and report the results? By defining your objectives, you can align your AI strategy with your business goals and expectations.

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Puranjay Trehan

Legal Software Consulting at Thomson Reuters | Ex- Goldman Sachs, Sirion | CLM & Project Management LinkedIn Top Voice | Legal Automation | Legal Technology
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Clarity is key before deploying AI to generate contracts. Before deploying AI, set clear goals and expectations. What do you want to achieve? Increased efficiency? Improved accuracy? Reduced bias? Defining your objectives helps choose the right AI solution and measure success. Its always beneficial to include all the intended users (Legal, Business, Sales, procurement etc).

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2 Choose your AI solution

There are different types of AI solutions that can generate contracts, such as natural language processing (NLP), machine learning (ML), and deep learning (DL). Each of these solutions has its own strengths and limitations, and you need to choose the one that best suits your needs and context. For example, NLP can analyze and generate text from natural language, but it may not capture the nuances and subtleties of legal language. ML can learn from data and patterns, but it may not explain how it reached its decisions. DL can handle complex and large-scale data, but it may require more computing power and data sources. You need to understand the capabilities and limitations of each AI solution and compare them with your objectives and requirements.

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Puranjay Trehan

Legal Software Consulting at Thomson Reuters | Ex- Goldman Sachs, Sirion | CLM & Project Management LinkedIn Top Voice | Legal Automation | Legal Technology
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Explore options that prioritize transparency. Look for solutions with Explainable AI (XAI) capabilities and detailed audit logs to understand the "why" behind generated clauses. Also try to find solutions which is adaptable to your needs and existing processes. Its never a one size fits all when it comes to AI solutions and so the focus should not be on what's best but on what's best for you.

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3 Train and test your AI model

Once you have chosen your AI solution, you need to train and test your AI model to ensure that it can generate contracts that are transparent, accurate, and fair. To train your AI model, you need to provide it with high-quality and relevant data that reflects your contract domain and scenarios. You also need to ensure that your data is not biased, incomplete, or outdated, as this can affect the performance and outcomes of your AI model. To test your AI model, you need to evaluate its outputs against your objectives and KPIs, as well as against human standards and expectations. You also need to check for errors, inconsistencies, or anomalies in the contracts generated by your AI model, and correct them as needed.

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Ken Lam

Legal AI Prompting & Engineering Specialist @ Thomson Reuters | JD
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Depending on the scope of your needs and given the market landscape of current AI tool offerings, it might be unnecessary and potentially less helpful to actually pursue training an AI model of your own, as opposed to just leveraging a pre-existing application or tool. Training and maintaining an AI model is challenging. Even after the model is initially trained, any further training thereafter risks degradation instead of improvement. These demands can place a heavy burden on any organization seeking to leverage AI. However, many applications and tools have been created this past year which leverage pretrained foundational models. Such tools might be a good out-of-the-box solution that saves time and investment while still benefitting.

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Oliver-Andreas Leszczynski

Industrial AI Strategist | Economic Research & Maritime Industry
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Develop standards and best practices for AI-generated contracts. This includes guidelines on structure, language, and legal requirements. Ensure comprehensive documentation of the AI models, including their design, training data, and updates. This helps in understanding the AI’s "thought process". Train the users of AI-generated contracts, including legal teams and other stakeholders, to understand AI capabilities and limitations. Increased awareness and understanding among users can lead to better utilization and trust in AI-generated contracts.

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4 Monitor and audit your AI process

After you have trained and tested your AI model, you need to monitor and audit your AI process to ensure that it maintains its transparency and quality over time. You need to keep track of the inputs, outputs, and feedback of your AI process, and analyze them for any changes or trends. You also need to review the performance and results of your AI process regularly, and update or improve your AI model as needed. Moreover, you need to document and report your AI process and its outcomes, and share them with your stakeholders and customers. This way, you can demonstrate the transparency and accountability of your AI-generated contracts, and build trust and confidence with your partners.

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Oliver-Andreas Leszczynski

Industrial AI Strategist | Economic Research & Maritime Industry
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Maintain comprehensive logs of AI decisions and actions. This should include details on what data was used, how the AI processed this data, and how decisions were reached. In contract generation, an audit trail would record every step the AI took in drafting, revising, and finalizing the contract. Regularly review and update the AI systems to ensure they remain accurate and relevant, especially considering the dynamic nature of legal and industrial norms.

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5 Collaborate and communicate with your team

Finally, you need to collaborate and communicate with your team to ensure the transparency of AI-generated contracts. You need to involve your team members in the design, implementation, and evaluation of your AI process, and solicit their feedback and suggestions. You also need to educate and train your team members on how to use and interact with your AI solution, and how to handle any issues or challenges that may arise. Moreover, you need to communicate with your team members about the benefits and risks of AI-generated contracts, and how to balance them with human judgment and expertise. By collaborating and communicating with your team, you can foster a culture of transparency and innovation in your contract management practice.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you ensure the transparency of AI-generated contracts?

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1 Define your objectives

2 Choose your AI solution

3 Train and test your AI model

4 Monitor and audit your AI process

5 Collaborate and communicate with your team

6 Here’s what else to consider

Contract Management

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