Ohio's new ethics guide — Artificial Intelligence for Lawyers and Judicial Officers — leaves one big question unanswered

Ohio's new AI Ethics Guide for Lawyers is worth reading.

It tackles many of the questions lawyers have been asking since generative AI entered the mainstream: competence, confidentiality, client communications, billing, hallucinated citations, supervision, and judicial use of AI.

First, credit where it's due. Ohio deserves praise for stepping into a conversation that many jurisdictions have been reluctant to have. Lawyers are hungry for practical guidance on AI, and doing nothing is no longer an option. The Ohio Board of Professional Conduct deserves recognition for taking a serious run at a rapidly evolving issue.

Which is why one part of the Guide surprised me. On the question of whether lawyers can ethically disclose client information to AI platforms, the guidance seems to say both yes and no.

Employers can't outsource discrimination to an algorithm

AI is new and shiny. Employment law is not.

Mobley v. Workday proves the point. The court concluded that employers don't get to outsource liability just because they've outsourced the tool to an AI vendor.

The plaintiffs, a nationwide class of job applicants over the age of 40, allege that employers' use of Workday’s AI-driven screening tools discriminates on the basis of age. Whether those claims ultimately stick is a question for another day. But the legal framework governing them is old, settled, and very familiar. Discrimination is discrimination—whether it's carried out by a hiring manager, a spreadsheet, or an outsourced algorithm.

Beware the legal risks of AI meeting agents

AI meeting agents are everywhere. They join Zoom calls, transcribe conversations, summarize action items, and promise to save employees hours of note-taking. From a business perspective, the upside is obvious: better documentation, fewer "I don't remember saying that" disputes, and cleaner follow-up.

But like most shiny tech, AI meeting agents come with real employment law and litigation risk—especially if you don't think through how (and when) you use them.

The question isn't whether your employees are using AI at work (they are), but whether you're prepared for it

Employees using AI at work will be the workplace issue of 2026.

Not remote work.
Not noncompetes.
Not DEI.

AI.

Because employees are already using it — to draft emails, summarize documents, create work product, prepare presentations, and even help with performance reviews — whether employers have approved it or not.

And most companies are completely unprepared.

Your trade secrets just walked out the front door … and you might have held it open: Generative AI and trade secrets

No employee—except the rare bad actor—means to leak sensitive company data. But it happens, especially when people are using generative AI tools like ChatGPT to “polish a proposal,” “summarize a contract,” or “write code faster.” But here’s the problem: unless you’re using ChatGPT Team or Enterprise, it doesn’t treat your data as confidential.

According to OpenAIs own Terms of Use: “We do not use Content that you provide to or receive from our API to develop or improve our Services.”

But don’t forget to read the fine print: that protection does not apply unless you’re on a business plan. For regular users, ChatGPT can use your prompts, including anything you type or upload, to train its large language models.

The dos and don't of workplace AI policies

According to a recent BBC article, half of all workers use personal generative AI tools (like ChatGPT) at work—often without their employer's knowledge or permission. So the question isn't whether your employees are using AI—it's how to ensure they use it responsibly.

A well-crafted AI policy can help your business leverage AI's benefits while avoiding the legal, ethical, and operational risks that come with it. Here's a simple framework to help guide your workplace AI strategy:

Discrimination liability for "agents" extends to AI vendors, says federal court

Can an HR software vendor be held liable for the alleged discriminatory hiring decisions of its customers? According to one federal court, the answer is yes.

Derek Mobley — a Black man over the age of 40 who suffers from anxiety and depression — alleges that he applied for 80-100 positions since 2018 that use Workday as a screening tool … and has been rejected every single time despite his qualifications.

Mobley claims that Workday's artificial intelligence unlawfully favors applicants outside of protected classes through its reliance on algorithms and inputs influenced by conscious and unconscious biases.

Last week, the federal judge hearing Mobley's claim rejected Workday's efforts to dismiss the lawsuit on the basis that it was not Mobley's "employer" and thus the workplace anti-discrimination laws do not cover its actions in this context.

Prompt engineering tips for generative AI

Innovate or die. I held out long enough, but it's time for me to learn how to use ChatGPT and incorporate it into my legal practice. That was one of my biggest takeaways from the Mackrell International Annual General Meeting earlier this month.

What does one do when one wants to learn how to effectively use ChatGPT? Ask ChatGPT! Here's the prompt I used: "I'm a lawyer conducting research on employment law. Can you give me the top 6 prompt engineering tips to optimize my results on ChatGPT?"

Here's what ChatGPT recommends:

The No Robot Bosses Act

"I, for one, welcome our robot overlords." 🙃

Consider this scenario. "You're a delivery driver and your employer's tracking algorithm determines you’re not performing up to its standards — and then sends you an email to let you know you've been fired without any warning or opportunity to speak to a human being." According to Senator Bob Casey, it is this example, along with others, that caused him to draft the the "No Robot Bosses Act."

If enacted, it would add protections for job applicants and employees related to automated decision systems and would require employers to disclose when and how these systems are being used.

Why all employers should care about the SAG-AFTRA and WGA strikes

At midnight on July 14, SAG-AFTRA, the labor union representing 160,000 film and television actors, went on striking, joining their fellow members of the WGA on the Hollywood picket lines. 

One of the key issues in both negotiations in the future of AI in the entertainment industry. SAG-AFTRA claims that the studios want the ability to pay background actors for one day's work use that likeness in perpetuity for any project without consent or compensation, including through the use of generative AI to fully replace the live actor. Similarly, a key sticking point for the WGA is the use of generative AI to write scripts in their entirety, which can then be edited by lower-priced non-union members.

What does an AI-written employee handbook look like?

Last week, I spoke at our sold out Wickens Workshop. The topic — employee handbooks.

As is the case whenever I speak on that topic, I was sure to make a point about the risks and dangers of relying on internet forms to craft your company's handbook … or as I put it, "Google, J.D."

Which led me to this thought — what happens when people start asking AI bots to write their handbooks?

So, I asked Bard, Google's new AI, to write me an employee handbook. Here's the very underwhelming result.

Class action lawsuit highlights the risk of AI in hiring and other employment decisions

Yesterday, news broke of a class action lawsuit filed against HRIS provider Workday claiming that its artificial intelligence systems and screening tools disproportionately and discriminatorily disqualify Black, older, and disabled job applicants. 

The named plaintiff, Derek Mobley, is a Black man over the age of 40 who suffers from anxiety and depression. He alleges that he applied for 80-100 positions since 2018 that use Workday as a screening tool and has been denied every time despite his qualifications. 

Mobley claims that Workday's artificial intelligence unlawfully favors applicants outside of protected classes through its reliance on algorithms and inputs created by humans conscious and unconscious biases.