Employment Law

Chapter 8

PetPals’ first warehouse manager starts a competing business using customer lists and gets fired. She sues for wrongful termination and gender discrimination. Noodle realizes they never had proper employment agreements or handbooks, leading to a costly settlement and a complete HR overhaul.

At-Will Employment

The default rule in Florida and most states that an employer may fire an employee for any reason or no reason, and an employee may quit at any time, as long as the reason is not illegal. The rule gives employers flexibility but does not shield a termination that violates a statute, public policy, or a contract.

Wrongful Termination

A firing for an illegal reason, even in an at-will state. Common grounds include termination because of a protected characteristic such as race, sex, age, or disability, retaliation for reporting illegal activity or exercising a legal right, and breach of an express or implied employment contract. Documented, nondiscriminatory reasons for every termination are the employer’s defense.

Employee or Independent Contractor

The classification that decides whether a worker is covered by minimum wage, overtime, antidiscrimination law, workers’ compensation, and unemployment insurance. Courts and agencies apply multi-factor tests focused on control and economic dependence, and misclassifying employees as contractors exposes a business to back pay, tax penalties, and agency enforcement.

Noncompete Agreement

A contract term in which an employee agrees not to work for a competitor or start a competing business for a set time and within a set area after leaving. Enforceability varies by state. Florida enforces reasonable restraints that protect a legitimate business interest under § 542.335 and, since 2025, gives covered high-wage noncompetes a presumption of enforceability under the CHOICE Act.

Title VII of the Civil Rights Act of 1964

The federal law that prohibits employment discrimination based on race, color, religion, sex, and national origin in hiring, firing, promotion, pay, and every other term of employment. It also prohibits sexual harassment as a form of sex discrimination and retaliation against employees who complain or participate in an investigation. The Equal Employment Opportunity Commission enforces it.

Fair Labor Standards Act

The federal wage and hour law that sets the minimum wage, requires overtime at one and a half times the regular rate for non-exempt employees who work more than forty hours in a week, and restricts child labor. Because wage claims are often brought collectively, one miscalculated payroll policy can become a class-wide liability.

Americans with Disabilities Act

The federal law that prohibits discrimination against qualified individuals with disabilities and requires employers to provide reasonable accommodations unless doing so would impose an undue hardship. The interactive process, an ongoing dialogue between employer and employee to identify a workable accommodation, is a practical obligation human resources staff manage constantly.

National Labor Relations Act

The federal law governing labor relations in the private sector. It protects the rights to organize, join a union, and bargain collectively, and it protects concerted activity, meaning even non-union employees who act together over wages or working conditions are shielded from retaliation. The National Labor Relations Board enforces it, and its administrative law judges write the public decisions this activity is built on.

Part 1: Build Your Benchmarks

Work through the first three prompts in order before running your analysis. Use the Benchmark Primer to build your understanding of what AI benchmark testing is and why it matters. Next, use the Benchmark Designer to generate a set of benchmarks tailored to reading legal decisions. Finally, use the Benchmark Refiner to work with the AI on tweaking and finalizing your own benchmarks for the test.

Part 2: Retrieve the Public Record

Go to the NLRB Administrative Law Judge Decisions page linked in the Resources and download one decision issued within the past twelve months. Choose a decision that involves a fact-specific dispute, such as a discharge, retaliation, or another unfair labor practice. Read enough of the decision yourself to understand the basic facts and legal issues before you begin testing. You cannot evaluate whether an AI is analyzing the decision accurately if you have not read it yourself.

Part 3: Run the Comparative Analysis

Once your benchmarks are finalized, use three different AI models, such as ChatGPT, Claude, Gemini, or NotebookLM, to analyze the decision. Ask each model to perform the same tasks so the comparison is fair, and upload the decision directly if the model allows it or paste the relevant portions. As you work through each model, note how it performs against your benchmarks, and look specifically at whether it correctly identifies the legal standard the administrative law judge applied, because that connection to employment law doctrine is what makes this a legal exercise rather than a software test. Then use the Comparative Evaluator to process and reflect on your findings.

Part 4: Post Your Deliverables

Create a single new discussion thread with two sections. The first is a case summary: a concise description of the decision you selected, including the parties, the core legal dispute, the judge’s reasoning, and the outcome, written entirely in your own words. The second covers your benchmarking results: explain your benchmarks, describe the tasks you asked the models to perform, present your ratings, and declare which model performed best with concrete examples.

Part 5: Share Chat Link

Include one AI chat link with a 1–2 sentence explanation of what the conversation shows and why you chose to share it.

Benchmark Primer

Act as an expert in AI model benchmark testing. Explain to a college student what AI benchmark testing is, why it is important, and how it works in plain terms. Describe the main steps involved in testing and comparing different AI models and highlight the most important things to remember when designing fair, clear, and useful benchmarks. Use concrete examples to show how well-designed benchmark tests help people understand which AI model performs a task better, and identify the most common mistakes to avoid when evaluating AI performance.

Method before measurement. Understanding what makes a benchmark fair, such as identical inputs and measurable criteria, keeps your comparison in Part 3 from turning into a matter of taste.

Benchmark Designer

Act as an expert in analyzing National Labor Relations Board administrative law judge decisions and as an expert in designing tests and benchmarks for AI models. Create five clear, practical benchmarks that a college student can use to evaluate how well any AI model analyzes an NLRB administrative law judge decision. Each benchmark should reflect something a real legal reader would care about, be specific and measurable, and include a brief explanation of how a student can use it to compare different AI models. At least one benchmark must test whether the model correctly identifies the legal standard the judge applied, and at least one must test whether every quotation the model attributes to the decision actually appears in it. Make the benchmarks realistic, objective, and suitable for students using tools like ChatGPT, AI Studio, or NotebookLM.

Two required benchmarks. Models fail legal reading in two characteristic ways, misstating the governing standard and inventing quotations, so the prompt guarantees your test catches both rather than only measuring fluency.

Benchmark Refiner

Act as an expert in AI model benchmark testing and in analyzing NLRB decisions. Ask me to share my proposed benchmarks for evaluating how well an AI model analyzes an NLRB decision. Once I share them, review each one carefully, provide constructive feedback and suggested improvements where needed, and explain your reasoning for any changes. Then guide me step by step on how to use my refined benchmarks to test and compare different AI models so that my evaluation is clear, fair, and meaningful.

Revision with a rationale. Asking the AI to explain every proposed change makes you decide whether to accept it, which is how the benchmarks become yours rather than the model’s.

Comparative Evaluator

Act as an expert in both AI model evaluation and employment law. I am going to share the results of my benchmark testing across three different AI models based on my analysis of a real NLRB administrative law judge decision. After I share my results, help me evaluate which model performed best overall and on each individual benchmark. Identify any patterns in where the models succeeded or struggled, and push me to think critically about whether any model correctly identified and applied the specific legal standard the judge used in the decision. Ask me follow-up questions that help me connect my AI evaluation findings back to the employment law doctrine the decision involved.

From scores to doctrine. The final step turns the benchmark results into questions about the legal standard itself, so the activity ends in employment law rather than in a software review.