From machine learning algorithms that predict consumer behavior to natural language processing systems that aid contract analysis, artificial intelligence (“AI”) is reshaping the way professionals approach work. Whether you're negotiating technology-centric contracts, litigating cases involving AI, or integrating AI into your business strategy, this glossary will help you understand AI terms and concepts and demystify jargon. Serve as a guide.
deep learning
meaning: A subfield of Machine Learning (defined below) that uses neural networks (defined below) with multiple layers to process complex patterns and large amounts of data.
Business applications: These include detecting fraud in financial transactions, predicting customer behavior for targeted marketing campaigns, recommendation systems for personalized product suggestions, and image recognition.
Legal considerations:
- Privacy and data protection: Deep learning models often require access to large amounts of data, raising concerns about data privacy and compliance with applicable data protection laws.and
- Bias and discrimination: Deep learning models can inadvertently perpetuate biases present in the training data, resulting in discriminatory or unfair results.
Large-Scale Language Model (“LLM”)
meaning: AI models are trained on a wide range of text data to produce text responses that resemble human writing. Commonly used for language translation, answering questions, and generating content.
Business applications: Enhance customer interactions, create marketing materials, and analyze feedback for trends and insights.
Legal considerations:
- Intellectual property: Using LLM to generate human-like text can introduce risks related to copyright infringement and plagiarism if the generated content contains protected material.and
- Misinformation and Defamation: LLM may generate false and misleading information or false information, which may lead to legal issues such as defamation and misrepresentation.
hallucination
meaning: AI hallucinations refer to the phenomenon in which the LLM produces meaningless or inaccurate output and perceives patterns or objects that are not present.
Business applications:
- Healthcare: AI models may incorrectly diagnose benign skin lesions as malignant, prompting unnecessary medical treatment.
- News bots: AI-powered news bots can spread misinformation.
- Customer Service: Chatbots can provide incorrect information.
- Finance: AI tools used for market analysis can ghost trends and patterns that don't exist, leading to poor investment decisions.and
- Recruiting: AI systems designed to review job applications can create biases based on flawed data, leading to unfair hiring processes.
Legal considerations:
- Responsibility and Accountability: If a chatbot provides incorrect information that leads to financial loss or damage, the company deploying the bot may be held liable.
- Regulatory compliance: Companies must ensure that their AI systems comply with existing laws and regulations, including data protection and privacy laws.and
- Consumer protection: Clear terms and disclaimers may be required regarding the use of AI-generated information.
Click here to learn more about the considerations for using AI chatbots.
Many companies are considering how to integrate AI chatbots with existing IT systems, databases, and business data to ensure output is based on business facts rather than artificial output from an LLM. This reduces the risk of hallucinations.
machine learning
meaning: The field of AI that includes training algorithms that learn from data to make predictions or take actions without being explicitly programmed. This allows the system to automatically improve with experience. A model is only as good as its data, so collecting the right dataset has a huge impact on model quality and performance.
Business applications: Sales forecasting, customer segmentation, fraud detection, inventory management optimization for operational efficiency, technology-enabled document review, and more.
Legal considerations:
- Fairness and discrimination: Machine learning models can produce biased results, leading to potential discrimination based on protected characteristics and potentially violating anti-discrimination laws.and
- Responsibility and Accountability: If the output or decisions made by a machine learning model result in harm or adverse effects, questions about responsibility and accountability can arise.
Natural language processing (“NLP”)
meaning: A branch of AI that focuses on enabling computers to understand, interpret, and generate human language. NLP algorithms are used in a variety of applications such as sentiment analysis (i.e., analyzing the tone of communication), language translation, and chatbots.
Business applications: Evaluate the tone of customer reviews, language translation for global communications, language localization (adapting content to meet the linguistic and cultural requirements of a specific market or region), and chatbot implementation for customer support.
Legal considerations:
- Regulatory compliance: NLP applications may need to comply with industry-specific regulations and restrictions regarding the use of certain types of linguistic data, such as medical or financial information.and
- Confidentiality and data privacy: NLP models may involve processing personal or sensitive data, raising concerns about complying with privacy laws and ensuring appropriate data security measures. Ensuring the confidentiality and security of this data is a major legal concern, especially when regulations such as GDPR are in place. For more information, we encourage you to subscribe to Fasken's Privacy and Cybersecurity Bulletin.
rapid engineering
meaning: Prompt engineering refers to the process of creating inputs or prompts that guide a generative AI system to produce optimal output. Think of it like giving directions. The clearer and more detailed the instructions, the better the AI will be able to perform the requested task. In practice, rapid engineering involves choosing the right words, using specific terminology, providing examples, setting the tone, and clarifying context.
Business applications: Improve responses to customer service inquiries, enhance data analysis, and refine marketing content output.
Legal considerations:
- Compliance and accuracy: In certain industries, such as legal and financial services, prompt engineering considers compliance obligations, the accuracy of information provided, and the potential risks associated with misleading or inaccurate guidance. is needed.
As AI continues to evolve, staying informed is key to leveraging its potential and ensuring compliance with evolving legal standards.
