AI For Attorneys: Everything You Wanted to Know But Were Too Afraid to Ask—Part 2
- patentpendingmades
- Mar 24
- 5 min read
Updated: Mar 27
AI in Legal Practice & Ethical Considerations: 10 More Terms for Attorneys
Now that we’ve covered the basics, let’s dive into how AI is applied in the legal field and the challenges it presents. From eDiscovery to AI ethics and regulatory frameworks, these next ten terms will help attorneys navigate AI’s growing role in law.
11. Prompt Engineering
What It Is
The practice of crafting precise inputs (prompts) to guide AI models—especially large language models—to produce desired outputs.
How It Works
With models like ChatGPT, the way you phrase your prompt can drastically alter the answer.
Attorneys increasingly find that specifying context, tone, or format can produce more reliable or relevant legal content.
Why It Matters to Attorneys
If you’re using AI to draft a motion or contract clause, you need to know how to “ask” for exactly what you want.
Prompt engineering becomes a valuable skill—like writing a thorough memo to junior associates so they understand what you need.
12. eDiscovery
What It Is
The digital version of discovery: identifying, collecting, reviewing, and producing electronically stored information (ESI) in litigation.
How It Works
Modern eDiscovery tools deploy machine learning (ML) algorithms to categorize documents and flag relevance or privilege.
These systems rely on pattern recognition: an attorney labels a small set of documents, and the AI learns to extrapolate.
Why It Matters to Attorneys
eDiscovery can be a budgetary black hole if done inefficiently. AI speeds up the review process and reduces human drudgery—but it also introduces the question: Did the AI miss anything?
Proper validation and quality checks become essential.
13. AI Ethics
What It Is
The moral framework guiding how AI should be developed, deployed, and regulated—covering fairness, transparency, privacy, and accountability.
How It Works
Ethical guidelines often intersect with legal requirements. For instance, ensuring algorithms don’t illegally discriminate or invade privacy.
Many organizations adopt internal AI ethics boards to review and approve new AI initiatives.
Why It Matters to Attorneys
As the guardians of legal and ethical standards, attorneys are uniquely positioned to shape these frameworks.
Advising on AI ethics can protect clients from lawsuits and reputational damage, and help them align with emerging regulations.
14. Algorithmic Bias
What It Is
When an AI system systematically disadvantages or discriminates against certain groups—often because of biased or unrepresentative training data.
How It Works
Bias creeps in when the data used to train the model excludes or disproportionately represents specific demographics.
The model picks up on these skewed patterns and perpetuates them at scale.
Why It Matters to Attorneys
From hiring algorithms to loan approval systems, bias can lead to claims under civil rights, consumer protection, or employment laws.
Plaintiffs’ attorneys can use discovered bias as a powerful basis for class-action suits. Defense attorneys need strategies to show robust data handling.
15. Explainable AI (XAI)
What It Is
Efforts to make AI decisions interpretable so humans can understand why a model reached a certain outcome.
How It Works
XAI tools often provide “feature importance” metrics or simplified decision trees that approximate how the model thinks.
It’s an ongoing research area: how do we simplify inherently complex neural networks for human comprehension?
Why It Matters to Attorneys
The legal system demands reasoning and evidence. If a “black box” algorithm denies someone insurance coverage, can we explain the rationale to a judge?
Transparency is key to building trust with clients, regulators, and courts.
16. Hallucination
What It Is
The phenomenon where an AI model confidently generates information that’s factually incorrect—essentially making it up.
How It Works
Because large language models rely on statistical patterns, they sometimes “fill in gaps” with plausible-sounding nonsense or references.
They aren’t “lying” in a moral sense; they’re just trying to predict the next likely word sequence, with no grounding in actual facts.
Why It Matters to Attorneys
A seemingly authoritative citation might reference a non-existent case, or misquote a statue. That’s a serious risk if you rely on AI for legal research without verification.
Always do a reality check on any AI-generated text before finalizing.
17. Supervised vs. Unsupervised Learning
What It Is
Supervised learning uses labeled data (e.g., “This contract is relevant,” “This clause is about IP”), while unsupervised learning has no labels and discovers patterns on its own.
How It Works
Supervised models learn “X → Y” from training examples, akin to a teacher grading homework.
Unsupervised models cluster data based on similarities, often uncovering hidden structures or anomalies without human guidance.
Why It Matters to Attorneys
In eDiscovery (supervised), you label a small set of docs, and the model extrapolates. In investigating potential fraud (unsupervised), you let the model unearth suspicious clusters you might not see otherwise.
Different tasks call for different learning modes; attorneys should know which approach is in play to anticipate strengths and weaknesses.
18. Transfer Learning
What It Is
Leveraging a model trained on one task (e.g., general language comprehension) and adapting it to another (e.g., legal drafting).
How It Works
Instead of training from scratch, you fine-tune an existing model with some domain-specific data.
The model retains broad language understanding, then picks up specialized legal context quickly.
Why It Matters to Attorneys
Transfer learning makes advanced AI cheaper and faster to deploy. Firms don’t need to invest in training giant models from zero.
This leads to more accessible, customized solutions—like a small firm leveraging a fine-tuned LLM for specialized case law research.
19. Data Privacy
What It Is
The policies, laws, and practices governing how personal data is collected, used, shared, and protected.
How It Works
AI systems rely on data as “fuel.” But they must comply with data protection rules like GDPR (EU), CCPA (California), or HIPAA (for health data).
Compliance often means anonymizing data, limiting retention, ensuring purpose limitation, and obtaining valid user consent.
Why It Matters to Attorneys
Navigating these regulations is a top concern in AI deployment.
Lawyers need to draft data-sharing agreements, advise on compliance, and ensure that AI models don’t illegally process or store personal info.
20. Artificial General Intelligence (AGI)
What It Is
The hypothetical AI that can perform any intellectual task a human can, possibly surpassing human intelligence.
How It Works
AGI is still largely speculative. Building a machine that “understands” and can reason about a broad range of topics, learn abstract concepts, and apply them flexibly remains an open challenge in AI research.
Why It Matters to Attorneys
While AGI isn’t here yet, it frames the extreme potential of AI.
It underscores the importance of regulatory frameworks. If we ever do reach AGI-level systems, we’ll face radically new legal questions about personhood, liability, and rights for intelligent machines.
The question isn’t whether to engage with AI, but how to do so strategically. Armed with a deeper understanding of these fundamental terms and mechanisms, you can better:
Advise your clients on compliance and risk,
Adopt AI tools that streamline your practice, and
Anticipate the next wave of change—whether that’s new regulations, market expectations, or even the elusive AGI.
Remember: AI is not a magic wand; it’s a set of tools that still requires your legal expertise to deploy wisely and ethically. The more you know about how these systems actually work, the better equipped you’ll be to harness them—and avoid their pitfalls.
Comments