AI ethics training in 2026: What L&D leaders need to know

Your marketing team just used AI to draft a customer email. It looked promising, until someone spotted information that came from a competitor’s confidential pitch deck embedded in the training data.

Now there’s a compliance concern, a trust challenge, and a fast-moving Slack conversation where some employees are asking, “Are we even allowed to use AI?” while others have stopped using it altogether.

Employees want to use AI, but they’re unsure how to do it safely. The risk of unintentionally breaking policy or creating legal exposure means some avoid it entirely, while others experiment quietly without guidance. 

Ethical AI use means employees can recognise when AI outputs might be biased or misaligned, and address these concerns before they cause harm.

• The hiring manager who catches that AI resume screening is filtering out candidates with employment gaps (which disproportionately affects caregivers and people with disabilities)
• The customer service manager who notices AI responses use overly formal language with customers who have certain surnames
• The marketing lead who questions why AI-generated ad copy only shows women in caregiving roles and men in leadership positions

Compliant AI use means understanding and following the laws, regulations, and policies that govern AI, and applying them in daily work:

• The HR coordinator who knows she can't feed employee performance reviews into ChatGPT because it violates General Data Protection Regulation (GDPR)
• The healthcare admin who never puts patient names into AI tools, even for "just summarizing notes"
• The finance team that checks whether their AI-powered expense tool meets SOC 2 requirements before rolling it out

When planning AI training for employeesThe AI governance and compliance frameworks that matter include the EU AI Act (if you do business in Europe), state-level laws like Colorado's AI Act and California's regulations (if you're in the US), and industry-specific rules like HIPAA for healthcare and SOC 2 for finance. Plus, a shifting federal landscape that might invalidate some state laws by mid-2026.

Here's what your employees need to understand about the frameworks that apply to your organization.

The EU AI Act

This is the world's first comprehensive AI regulation, and if you do business in Europe, it's mandatory.

What your employees need to know:

• AI systems are categorized by risk level: unacceptable (banned entirely), high-risk (strict requirements), limited risk (transparency obligations), and minimal risk (mostly unregulated)
• High-risk applications include hiring tools, credit scoring, and anything involving biometric data
• Violations can result in fines up to €35 million or 7% of global revenue, whichever is higher

ISO/IEC Standards

These are global benchmarks for AI governance and risk management.

What your employees need to know:

• ISO/IEC 42001 sets requirements for AI management systems (how you govern AI use across your organization)
• ISO/IEC 23894 focuses on AI risk management (identifying and mitigating potential harms)
• If your organization claims ISO compliance, employees need to know what that actually means for their day-to-day work

Industry-Specific Regulations

Depending on your sector, additional rules apply.

What your employees need to know the responsible AI principles and rules for each:

• Healthcare (HIPAA): Never input protected health information into public AI tools. Even "anonymized" data can sometimes be re-identified.
• Finance (SOC 2, GLBA): AI tools that handle customer financial data must meet strict security and privacy standards.
• Education (FERPA): Student records can't be processed through AI tools without proper safeguards.

State-Level AI Laws (US):

38 US states passed AI-related measures in 2025 alone. But the landscape is about to shift: a December 2025 executive order directed the DOJ to challenge "onerous" state AI laws and push for a unified national framework. What's enforceable now might not be by mid-2026.

For now, here are some of the major state laws your employees need to know:

• Colorado AI Act (effective June 30, 2026 after delay): First comprehensive state AI law requiring risk assessments, bias audits, and consumer disclosures for high-risk AI systems
• New York RAISE Act: Targets AI developers with high training costs, requiring safety policies and risk-mitigation frameworks
• Texas TRAIGA (effective Jan 1, 2026): Focuses on preventing specific harmful uses of AI, such as inciting self-harm or creating child exploitation, and discrimination 

The compliance frameworks are one thing. The risks are what keep security teams up at night: AI agents with excessive access, deepfake attacks on executives, uncontrolled employee AI usage that causes legal action.

Here are the ones to watch out for:

• AI agents: By 2026, 40% of enterprise applications will include AI agents. Agents have access to everything: your APIs, your data, your systems. A single poorly designed or misused prompt can result in data loss or approval of unauthorised transactions, making clear controls essential.
  
  
• Deepfakes:  In 2025, attackers used deepfake video calls to trick IT into granting network access. Deepfake technology can convincingly mimic voices and video, creating risks for fraud or misinformation.
  
  
• Shadow AI: Your employees are already using AI tools you don't know about. They're feeding customer data into ChatGPT, drafting contracts with unvetted models, and using unapproved coding assistants. It may only become visible when the organisation faces a compliance review or data incident.

Policies tell employees what not to do. Training teaches them how to make judgment calls in real situations, spot bias before it causes harm, and handle edge cases that no policy document can predict.

Here's why AI ethics training matters more than policy alone:

• Policies explain the rules. Training teaches how to apply them: Your policy says "don't use customer data inappropriately in AI tools." Training teaches employees that company name, revenue figures, and project details are off-limits, but publicly available info like industry is generally fine.
  
• Policies can't predict every scenario: Your AI policy for employees covers the rules. Training builds the judgment employees need to navigate gray areas policy documents can't predict.
  
• Training creates shared understanding: When everyone goes through the same foundational AI ethics training, they understand the same things: Engineering knows why Legal blocks certain tools. Marketing understands why Finance needs extra approval for AI expense software. Shared training means fewer crossed wires and faster decisions.
  
• Policies get outdated. Training evolves: Your AI policy may have been created months ago, but tools, regulations, and risks can change quickly. Training can adapt with these changes, keeping employees informed and confident.

Research from Go1's AI Research Report backs this up: Over the past year, AI awareness and safety courses had the highest enrollments of any AI topic. Employees don't just want rules to follow. They want to understand how to apply them in real situations.

Responsible AI training for employees is about giving them the skills to use it safely. That means covering four core areas: bias recognition, data responsibility, human oversight, and transparency.

1. Teach employees to spot and stop bias before it scales

AI learns from historical data. If that data reflects past discrimination, the AI will perpetuate it. Unless someone catches it.

Here's what employees need to learn:

• How to spot bias in real time: Does the AI consistently rank candidates from certain universities higher, even when qualifications are identical?
  
• Why your input data matters: The hiring team wants to use AI to screen resumes, but their historical hiring data shows 90% male hires in engineering. If they feed that data to AI without adjustment, it'll recommend more male candidates.
  
• Why every AI output needs a human sanity check:  Does this AI-generated job description use language that discourages certain groups from applying? Is this performance evaluation fair, or is the AI penalizing someone for taking medical leave? 

2. Train employees on what data is safe to share (and what isn't)

Not all AI tools handle data the same way. And not all data belongs in AI tools. Employees need to know the difference before they accidentally paste a client's financials into ChatGPT, including learning: 

• If it's not public, don't put it in a public AI tool: The rule is simple: If you wouldn't post it on LinkedIn, don't feed it to free AI.
  
• Free tools and enterprise tools aren't the same thing: ChatGPT's free version saves your prompts and uses them to train future models. Your company's enterprise AI with a signed BAA (Business Associate Agreement) doesn't.
  
• Anonymize before you analyze: Strip names, email addresses, and account numbers before feeding customer feedback into AI. Remove identifying details from employee survey responses before summarizing them. 

3. Reinforce that AI is a tool (and humans remain accountable)

Even when AI generates seemingly perfect outputs, humans remain accountable. 

Employees need to verify every factual claim AI makes because AI hallucinates, misinterprets context, and makes up citations. And certain decisions (hiring, firing, credit approvals, medical recommendations, performance reviews) require documented human oversight, not just a rubber stamp on whatever the AI suggests. 

Above all, AI compliance training needs to teach employees that AI is only a tool. The human is always accountable for the outcome.

4. Train employees when and how to disclose AI usage

Transparency doesn't mean announcing "ChatGPT wrote this!" on every email. It means knowing when disclosure matters and creating an audit trail so your company can prove AI was used responsibly if things go sideways. 

For example, the customer service rep using AI to draft responses doesn't need to tell customers. But they should log it internally, because if that AI hallucinates a fake return policy, someone needs to trace where it came from. Or, the marketer using AI for campaign briefs should flag which sections are AI-generated so reviewers know what needs a human fact-check. 

Understanding what ethical and compliant AI use looks like is one thing, but building it into your organization's everyday operations is another. 

The shift from awareness to action requires a structured approach that meets employees where they are, addresses real gaps, and scales across your entire workforce. That’s what helps your organization overcome AI adoption challenges that meet employees where they are, close skill gaps, and integrate into everyday workflows.

Here's how to do it:

Step 1: See what's actually working (and what's not): Use Go1's AI Research Report to understand how other organizations are tackling AI right now. What skill gaps they're facing. What employees are worried about. How L&D leaders are rolling out training that sticks.

Step 2: Figure out where your organization stands: Go1's AI Maturity Matrix benchmarks your organization across four critical areas of AI readiness, including compliance. You'll see exactly where you're ahead, where you're behind, and what to fix first.

Step 3: Build a plan that won't fall apart in three months: Go1's AI Upskilling Playbook gives you the step-by-step process, from creating an L&D-driven strategy to rolling it out with templates, tools, and success metrics. It's how you move employees from "what is AI?" to "I know how to use this safely" without reinventing the wheel.

Step 4: Give employees training they'll actually use: Go1 is a trusted AI learning solution. It provides 2,500+ continuously updated AI courses organized into personalized learning pathways. Foundational AI literacy, ethical use, compliance frameworks, role-specific applications. 

Helping employees move from basic awareness to confident, compliant AI use requires structure, skills, and a culture where responsible AI is standard practice. Go1’s AI Maturity Matrix, Upskilling Playbook, and continuously updated course library give teams the insights and training they need, from spotting bias to making informed decisions about data use.

Go1 gives you everything you need to make that happen. The AI Maturity Matrix shows you where your organization stands right now. The AI Upskilling Playbook gives you the step-by-step rollout plan. And 2,500+ continuously updated courses give employees the training they actually need, from spotting bias to making real-time judgment calls about what's safe to share.

When employees understand responsible use of AI in the workplace, they'll actually use it. And your organization stays compliant while everyone else is scrambling to catch up.