Navigating AI: A Beginner’s Guide for Court Professionals

In today’s tech-driven world, artificial intelligence (AI) has become a part of our everyday lives. From Siri’s voice on our smartphones to the convenience of Alexa at home, AI is all around us. Yet, despite its prevalence in our daily activities, many of us find ourselves unsure about what the integration of AI really means for our work lives, especially for court professionals.

In this blog, we’ll break down the basics of AI. You’ll get familiar with fundamental concepts and applications, review the important distinction between generative and non-generative AI, and look at the benefits AI is likely to bring to your justice agency in the coming years.

We will get into all that shortly (or if you want, skip ahead). First, let’s define some basic terms.

Common AI Terms

Artificial Intelligence (AI): Think of AI as the brainpower behind smart devices. It’s powered by algorithms which let the machine mimic human cognitive function. Because of that, AI can learn, reason, and make decisions somewhat like people do.

Algorithm: The foundation of any computer program, an algorithm is like a recipe that helps computers perform tasks. It provides a set of instructions for step-by-step procedures that aid in calculations and problem-solving. Basically, the algorithm tells the machine the steps to follow to get a job done.

Machine Learning (ML): Machine learning is the process in which machines learn from data. Machine learning algorithms let AI solutions improve their performance over time by learning from experience, identifying patterns in data, and adjusting their behavior accordingly.

Training Data: This is the information machines use to learn and get smarter. The quality and quantity of training data significantly impact the performance of AI models. Datasets that are diverse and extensive contribute to machine learning models that are more robust and accurate.

Models: In AI, models are the outcome of learning. Once a machine has learned enough, it creates a mathematical model that it can use to apply that knowledge to new situations. With a model, the machine can make predictions or decisions without being explicitly programmed for the exact scenario it’s facing.

Natural Language Processing (NLP): A broad field that revolves around the interaction between computers and human language. NLP encompasses a range of techniques, models, and algorithms that make it possible for machines to understand, interpret, and respond to human language. This includes sentiment analysis, language translation, and speech recognition.

Large Language Models (LLMs): A specific type of machine learning model that’s based on a new AI algorithm (e.g., Transformer algorithm) and large amounts of training data. LLMs fall under the umbrella of AI but are specifically designed so that AI solutions can process and generate human-like text that’s coherent and contextually relevant. They’re especially helpful in creating chatbots and doing tasks that involve language generation and text completion.

Data Processing: It’s how computers organize, analyze, and make sense of data in a way that it becomes useful for us. It includes tasks like sorting, filtering, and transforming data for analysis, even manipulating raw data to extract meaningful insights. Data processing is often used to weed through massive amounts of data to find the useful bits we need.

Robotic Process Automation (RPA): Robotics combines mechanical engineering, electronics, and computer science in order to bring us software robots. In RPA specifically, robotics is used to create software that automates routine, rule-based tasks in business processes (like sorting files into specific folders based on pre-established criteria). RPA lets us offload repetitive tasks, making the work of humans much easier.

ChatGPT: A specific AI solution — a large language model developed by an AI research organization called OpenAI. It’s designed to generate logical and situationally appropriate text in response to user inputs. Basically, you can chat with ChatGPT and ask it questions. It’ll use its training to respond to you in a human-like way.

Deepfake: Visual or audio content fabricated or manipulated with AI, often to create realistic but misleading media. This technology raises concerns about misinformation and underscores the need for robust authentication methods. It serves as a reminder that not everything we see online is as it seems.

Generative AI: “Generative” refers to AI models that can autonomously create new content, like text, images, or music. Generative AI solutions can come up with new ideas, create art, and even write stories. They are trained on data, and they use programmatic predictions, but the tools do not follow specific rules and don’t have a lot of set boundaries. This means generative AI can be unpredictable.

Non-Generative AI: In contrast to generative AI tools, non-generative AI solutions follow instructions precisely. They perform predefined operations within well-established boundaries. They don’t generate new data or content. They work well for enabling the automation of task-specific functionality based on learned patterns and explicit rules. For example, sorting an email inbox into spam and not-spam categories.

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A Closer Look at the Foundational Elements of AI

There are three essential components at the core of most major AI capabilities:

1. Machine learning (ML)
2. Natural language processing (NLP)
3. Large language models (LLMs)

These technologies share a common foundation — they're all forms of data-driven learning and use extensive datasets to facilitate language-related skills.

Machine learning is the fundamental building block. It actively learns from labeled data and acquires the ability to recognize patterns. In language tasks, it’s machine learning that’s responsible for adeptly understanding word sequences, grammatical structures, and semantic meanings. Machine learning sets the stage for the subsequent advancements in language technology.

Natural language processing takes the reins, leveraging the insights gleaned from extensive data. It excels in understanding the intricacies of human language, including syntax, semantics, and context. NLP models can discern relationships between words, adapt to contextual shifts, and extract the meaning embedded in sentences. This refinement positions NLP as a crucial bridge that connects the presence and the meaning of a word in construction of AI models.

Large language models, exemplified by giants like GPT-4 (the technology behind ChatGPT), emerge as powerful entities. Born from vast datasets, they surpass basic pattern recognition by delving into the stylistic and contextual nuances of language. These models, with their colossal scale, can generate coherent and contextually rich text with impressive proficiency.

The strength of AI is born from the intersection of machine learning, NLP, and LLMs. Machine learning lays the groundwork, NLP enhances linguistic understanding, and LLMs leverage their vast scale to produce contextually rich responses. The interaction is not just technical but practical, leading to a unified approach to all language-related tasks.

Generative vs. Non-Generative AI

In endeavoring to understand AI and its roles in our lives, there is a critical distinction to be made between generative and non-generative AI.

Generative AI plays the role of the artist in the artificial intelligence world, autonomously creating new content — including text, images, and music.

OpenAI's GPT-4, the large language model we mentioned earlier, is a perfect example of this. It composes essays, generates creative writing, and even writes computer code.

However, it’s the creative nature of generative AI that also introduces risk, especially when it comes to fabrication.

Deepfake technology — a subset of generative AI — is cause for concern. These tools use AI to create highly realistic but entirely fabricated videos or audio files.

In a legal setting this is especially problematic. You can see how deepfake technology could be exploited to manipulate evidence and create false scenarios that could influence legal proceedings.

Imagine a deepfake video showcasing a defendant confessing to a crime. Such manipulations, if presented as evidence in court, could have severe consequences, including wrongful convictions or unjust legal outcomes.

The potential for misuse of AI in legal contexts has raised ethical and practical concerns, urging caution in embracing generative AI within certain applications.

The same risk does not exist with non-generative AI.

Non-generative AI lies on the other side of the spectrum. A reliable workhorse, non-generative AI is focused on automating tasks without even a hint of creative inventions. It excels at specific, rule-based functions and is built to carry out data recognition tasks in precisely the same way a human would, just with more accuracy and speed.

Examples of non-generative AI include document classification and data location for extraction and redaction.

Unlike generative AI, non-generative tools prioritize efficiency and accuracy in executing predefined tasks. Take, for instance, automated data entry or document processing systems. These tools streamline repetitive tasks in a way that enhances productivity, without introducing the creative ambiguities associated with their generative counterparts. This reliability makes them trustworthy allies in various industries, including the justice system, where precision and adherence to established procedures are critical.

Benefits of AI-Based Tools

When it comes to AI, efficiency gains stand out as a notable advantage, with very real benefits for humans embracing the technology.

AI handles large (even enormous) volumes of data swiftly. The tools excel at sorting through extensive datasets, extracting useful information, and facilitating quick decision-making. And they do so with meticulous accuracy. This significantly reduces the rate of errors compared to human data processing.

This is a major win for the workforce.

AI can augment existing human roles by automating the most mundane and repetitive tasks. This lets organizations add scale to their existing processes, more easily overcome gaps in worker availability, and increase their processing speeds.

Courts, for example, can implement AI technology to locate data on unstructured court documents then enter data into case management systems faster and more accurately, redact data automatically, speed up internal processes, and expedite access to justice. All while allowing human employees to focus on more strategic, creative, and intellectually stimulating aspects of their jobs.

AI transforms a tool into a partner, fostering a dynamic, efficient, and satisfying work environment.

As organizations strategically leverage the potential of AI, they go beyond efficiency gains to cultivate workplaces where human skills, creativity, and emotional intelligence are emphasized, ensuring staff find greater fulfillment and purpose in their roles.

Curious how artificial intelligence helps court professionals? Our next three blogs will explore that in depth. Stay tuned!