AI Glossary For Business Professionals: Defining AI Tools And Associated Legal Risks



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From machine learning algorithms that predict consumer behaviour to natural language processing systems that assist in contract analysis, artificial intelligence ("AI") is reshaping the way professionals approach their work.
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From machine learning algorithms that predict consumer behaviour to natural language processing systems that assist in contract analysis, artificial intelligence ("AI") is reshaping the way professionals approach their work. Whether you're negotiating tech-centric contracts, litigating cases involving AI, or integrating AI into your business strategy, this glossary can serve as a guide to the terminology and concepts of AI, demystifying the jargon.

Deep Learning

Definition: 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: fraud detection in financial transactions, customer behaviour prediction 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, which may result in discriminatory or unfair outcomes.

Large Language Models ("LLMs")

Definition: AI models trained on extensive text data to produce text responses resembling human writing. Commonly used for language translation, answering questions, and generating content.

Business Applications: enhancing customer interactions, creating marketing materials, and analyzing feedback for trends and insights.

Legal Considerations:

  • Intellectual property: generating human-like text using LLMs may pose risks related to copyright infringement or plagiarism if the generated content includes protected material; and
  • Misinformation and defamation: LLMs can inadvertently generate misleading or false information, potentially leading to legal issues such as defamation or misrepresentation.


Definition: an AI hallucination refers to a phenomenon where an LLM generates outputs that are nonsensical or inaccurate, perceiving patterns or objects that are nonexistent.

Business Applications:

  • Healthcare: an AI model might incorrectly diagnose a benign skin lesion as malignant, prompting unnecessary medical procedures;
  • News bots: AI-driven news bots might spread misinformation;
  • Customer service: chatbots might provide incorrect information;
  • Finance: AI tools used for market analysis might hallucinate trends or patterns that don't exist, leading to poor investment decisions; and
  • Recruitment: AI systems designed to screen job applications might develop biases based on flawed data, leading to unfair hiring processes.

Legal Considerations:

  • Liability and accountability: if a chatbot provides incorrect information that leads to financial loss or harm, the company deploying the bot could be liable;
  • Regulatory compliance: companies must ensure their AI systems comply with existing laws and regulations, which may include data protection and privacy laws; and
  • Consumer protection: there may be a need for clear terms of service and disclaimers regarding the use of AI-generated information.

Click here to see more on cautionary tales of using AI chatbots.

Many businesses are considering how to integrate AI chatbots with existing IT systems, databases, and business data to ground the outputs in business facts rather than the artificial output from the LLM, as this reduces the risk of hallucinations.

Machine Learning

Definition: a field of AI that involves training algorithms to learn from data and make predictions or take actions without being explicitly programmed. It enables systems to automatically improve with experience. Collecting good data sets has a huge impact on the quality and performance of the model because the model is only as good as its data.

Business Applications: sales forecasting, customer segmentation, fraud detection, optimizing inventory management to improve operational efficiency, and technology-assisted review of documents.

Legal Considerations:

  • Fairness and discrimination: machine learning models can produce biased outcomes, leading to potential discrimination based on protected characteristics, which may violate anti-discrimination laws; and
  • Liability and accountability: if the outputs or decisions made by machine learning models result in harm or adverse consequences, questions regarding liability and accountability may arise.

Natural Language Processing ("NLP")

Definition: a branch of AI that focuses on enabling computers to understand, interpret, and generate human language. NLP algorithms are used in various applications, including sentiment analysis (i.e., analyzing the tone of communications), language translation, and chatbots.

Business Applications: evaluating the tone of customer reviews, language translation for global communications, language localization (adapting content to meet the language and cultural requirements of a specific market or region), and chatbot implementations for customer support.

Legal Considerations:

  • Regulatory compliance: NLP applications may need to comply with industry-specific regulations or restrictions on the use of certain types of language data, such as healthcare or financial information; and
  • Confidentiality and data privacy: NLP models may involve the processing of personal or sensitive data, which raises concerns about compliance with privacy laws and ensuring appropriate data security measures. Ensuring confidentiality and security of this data is a major legal concern, especially with regulations like GDPR in place. We recommend subscribing to Fasken's Privacy & Cybersecurity Bulletins to learn more.

Prompt Engineering

Definition: prompt engineering refers to the process of crafting the input or prompt that guides a generative AI system to produce the best output. Think of it like giving directions; the clearer and more detailed the instructions, the better the AI can perform the task it is being asked to perform. In practical terms, prompt engineering can involve choosing the right words, using specific terms, providing examples, setting the tone, and clarifying the context.

Business Applications: improving responses to customer service inquiries, enhancing data analysis, and refining outputs in marketing content.

Legal Considerations:

  • Compliance and accuracy: in certain industries, such as legal or financial services, prompt engineering should consider compliance obligations, accuracy of information provided, and potential risks associated with misleading or incorrect guidance.

As AI continues to advance, staying informed is key to leveraging its potential and ensuring compliance with evolving legal standards.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

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