An AI and ML primer for in-house counsel

This is the first of our series of deep dives into AI for in-house lawyers. Learn about Generative AI and LLMs in part 2.

Artificial Intelligence (AI) and Machine Learning (ML) can fundamentally change in-house legal departments, providing innovative tools that significantly enhance efficiency and decision-making for lawyers and legal operations professionals. These technologies offer a strategic advantage for legal teams aiming to scale their service delivery and operational efficiency without increasing headcount or compromising quality — a big win for legal teams and organizations under budget constraints.

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What is Artificial Intelligence (AI)?

Artificial Intelligence, commonly known as AI, involves systems simulating human intelligence processes. This technology encompasses a range of functionalities, including problem-solving, pattern recognition, and understanding natural language, enabling machines to perform tasks that typically require human intelligence. 

AI operates primarily by ingesting and processing vast amounts of labeled training data, identifying correlations and patterns within this data, and using these insights to predict future states.

One of the key drivers of the advancement of AI capabilities has been the improvement in computational power and data storage capabilities, which enables more efficient processing of ever-larger datasets. This progress has enabled AI to analyze existing data and learn and adapt over time, enhancing its accuracy and applicability in various fields. Now, AI can sift through case law and legal precedents at speeds and volumes unattainable by human researchers, providing invaluable insights for case strategy and legal research.

What is Machine Learning (ML)?

Machine learning is a subset of AI involving algorithms that enable machines to improve their performance over time as they are exposed to more data. Unlike traditional programming, where the machine follows a predefined set of instructions, ML allows the system to learn from past experiences and adjust its responses accordingly. This aspect of learning and adaptation is what makes ML a powerful tool.

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What’s an example application of Machine Learning for in-house counsel?

In-house lawyers often deal with a high volume of contracts, including vendor agreements, sales contracts, and other legal contracts. Reviewing these contracts can be time-consuming and requires high attention to detail. Leveraging ML tools for contract review can boost efficiency for in-house legal counsel by drastically cutting down the time needed for contract analysis. It can ensure consistency across contracts by checking if contracts adhere to company policies and legal playbooks and mitigate risks by identifying legal and financial risks in contracts. By putting initial contract review on autopilot, this process optimizes valuable resources and allows in-house attorneys to concentrate on more strategic and complex tasks.

Here’s an overview of the process:

Training the ML Model: First, the ML algorithm is trained on a large set of contracts and legal documents. During this training phase, the model learns to recognize and understand various legal terms, clauses, and contractual obligations.

Pattern Recognition and Learning: As the ML model processes more documents, it becomes adept at identifying key contract elements such as indemnification clauses, termination conditions, compliance requirements, and more. It can also learn to flag potential risks or deviations from standard contract terms.

Application in Contract Review: Once trained, the ML tool can quickly review new contracts. It can highlight areas of concern, suggest edits, and even compare new contracts against a database of existing contracts to ensure consistency and compliance with corporate standards.

AI and ML technologies can improve the efficiency of legal request intake for in-house counsel. Legal teams can automate the initial stages of request processing, where AI algorithms swiftly categorize, prioritize, and route legal requests based on their intake form field responses, such as monetary thresholds, request type, urgency levels, and more. This smart triaging reduces response times and legal review duration and ensures that critical matters receive immediate attention. This technology integration transforms the traditional, labor-intensive intake process into a more dynamic, data-driven workflow, aligning with the modern demands of in-house legal counsel.

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What’s the difference between Artificial Intelligence and Machine Learning?

Artificial Intelligence (AI) is the broader concept of machines capable of performing tasks that typically require human intelligence. Machine Learning (ML), on the other hand, refers specifically to algorithms that learn from data and make predictions or decisions. In the legal context, while legal teams can use AI for automating administrative tasks, ML could be employed for predictive analytics.

For example, in-house commercial legal teams focus on legal issues related to business operations. They manage a vast array of contracts with suppliers, partners, and customers. These contracts vary in complexity and are subject to various laws and regulations.

Potential AI applications include:

• Contract Generation: The AI tool uses natural language processing (NLP) to assist in drafting contracts. Based on inputs about the contract’s purpose, parties involved, and key terms, the AI tool generates a draft contract aligning with legal standards and company policies.
• Contract Review and Analysis: AI software reviews existing contracts, identifying the contract term, key clauses, obligations, and liabilities. It can also highlight deviations from standard playbook clauses or potential compliance issues with current laws.

Potential ML applications include:

• Predictive Risk Analysis: The ML algorithm, trained on a dataset of past contracts and their outcomes, identifies patterns that might indicate risk. For example, it could flag contracts that are likely to lead to disputes based on certain clause combinations or past incidents.
• Compliance Monitoring: ML tools continuously analyze changes in regulations and compare them against existing contracts. They alert the legal team if any contract becomes non-compliant due to new laws and regulations, enabling proactive legal risk management.

What should in-house lawyers look out for when leveraging AI tools?

When leveraging AI tools, in-house lawyers should consider several crucial factors for effective and ethical use. First, understanding the capabilities and limitations of AI is paramount. While AI can significantly enhance efficiency, particularly in tasks like contract analysis, legal research, and document management, it is not a panacea for all legal work. AI operates based on the data it has been trained on, which means its outputs are only as good as the data input. This limitation necessitates a critical approach where AI-generated recommendations or conclusions should be reviewed and contextualized by legal counsel.

In-house lawyers should be aware that AI systems often reflect biases present in their training data. AI can amplify and perpetuate these biases if these sources contain inherent prejudices and skewed data. This is especially concerning as AI biases can lead to illegal discriminatory practices under civil rights laws, impacting critical areas such as hiring, employment decisions, lending, housing, and medical care. This underscores the importance of human oversight and the need for ethical, transparent AI development and application across all sectors. Legal professionals need to understand the sources of training data for AI tools, assess potential biases, and implement robust checks and audits to ensure that AI systems function fairly and objectively. Collaborating with AI developers and data scientists to ensure more balanced and diverse datasets can mitigate these risks.

Integrating AI into legal workflows can bring significant data privacy and security considerations. In-house legal teams should ensure that AI tools comply with relevant data protection laws, such as the General Data Protection Regulation (GDPR) in Europe or the California Consumer Privacy Act (CCPA) in California. This includes understanding how these systems store, process, and protect data.

Continuous education and training in AI technology should be a priority for in-house legal teams. Staying informed about the latest developments in AI, the evolving landscape of AI tools, and bias mitigation strategies is critical to maintaining ethical and reliable AI-driven legal processes. Embracing AI in legal operations is a step towards a more efficient future, but it must be navigated with a clear understanding of its potential and pitfalls.

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Why is having a human in the loop important when in-house legal leverages AI and ML tools? 

Having a human involved in the process is a critical step for in-house legal teams that use AI/ML tools. This approach significantly minimizes business and legal risks. The AI/ML tools are ideal for initial screenings of contracts or other legal documents, as they can highlight potential issues or important clauses. Subsequently, an in-house attorney can conduct a more thorough review, ensuring a comprehensive and accurate legal analysis.

While AI and ML can efficiently process large volumes of documents and identify key terms, human oversight ensures that the nuances and context, often missed by algorithms, are accurately captured and interpreted. This collaboration is essential in mitigating risks such as non-compliance with laws and regulations and oversights that could lead to costly legal disputes. Furthermore, human intervention in AI-driven processes ensures that contractual obligations and risks are understood and managed in alignment with the organization's legal and business strategies.

When lawyers are involved in the human review of contracts or other documents initially screened by AI/ML tools, several best practices can ensure effectiveness and accuracy:

• Understand the AI/ML Tool’s Capabilities: Lawyers should have a basic understanding of the AI/ML tool’s capabilities and limitations. This knowledge helps determine where to focus their attention during the review, especially in areas where AI may not be as reliable, such as complex legal arguments or nuanced negotiations.
• Regularly Update and Train the AI: AI tools learn from data, so it’s crucial for lawyers to regularly update and train these systems with new contracts and legal information. This ongoing training helps the AI to adapt to updates and changes in law, contract language, risk playbooks, and company policies.
• Focus on High-Risk Areas: Lawyers should prioritize their review on high-risk areas that AI might not fully comprehend, such as clauses with significant legal or financial implications. This targeted approach ensures that critical aspects of the contract receive the necessary human attention.
• Cross-Verification of AI Output: Lawyers should cross-verify the AI’s findings with their legal training and experience. This includes checking for missed or incorrectly interpreted clauses and ensuring the contract aligns with current legal playbook standards and organizational requirements.
• Feedback Loop: Establishing a feedback loop where lawyers provide input back into the AI system is crucial. This feedback helps improve the accuracy and efficiency of the AI tool over time.
• Collaborate with Technology Providers: Lawyers should maintain open communication with their tool’s developers and providers to understand any updates in the AI tool, share insights on legal trends, and discuss any discrepancies found during reviews.
• Stay Informed About Legal Tech Developments: Legaltech tools are rapidly evolving. Staying informed about the latest developments in AI and ML can help lawyers leverage these tools more effectively and understand their evolving role in the legal review process.
• Maintain Ethical and Compliance Standards: Finally, it’s essential to ensure that the use of AI/ML in legal reviews adheres to ethical guidelines and compliance standards. Lawyers should be vigilant about how data is used and protected and ensure transparency in AI-driven decisions.

By combining the strengths of AI/ML tools with the critical thinking and expertise of legal professionals, legal professionals can enhance the accuracy, efficiency, and reliability of their key legal review processes.