Introduction

Artificial intelligence is no longer a futuristic concept—it’s actively reshaping how HR departments operate. From resume screening to benefits administration to performance management, AI tools are becoming standard across small and mid-sized businesses. But with this rapid adoption comes regulatory attention, particularly from the U.S. Department of Labor (DOL).

The DOL has been signaling increased focus on AI governance, emphasizing that employers bear responsibility for understanding how algorithmic decision-making affects their workforce. While formal mandates continue evolving, the framework emphasizing AI literacy represents a critical compliance consideration for any organization using automated systems in employment decisions.

This guide translates the DOL’s guidance into actionable steps for HR leaders and business owners.

Understanding the DOL’s AI Literacy Framework

What Does “AI Literacy” Actually Mean?

The DOL’s focus on AI literacy isn’t about requiring HR teams to code machine learning models. Instead, it emphasizes competency-based understanding: knowing what your AI systems do, how they work at a basic level, what data they use, and what outcomes they produce.

Think of it like this: You don’t need to be a mechanic to own a car, but you should understand basic maintenance and when something’s wrong. Similarly, you don’t need to be a data scientist, but you should understand:

What tasks your AI tools perform
How they make decisions
What bias risks exist
How to audit their outputs
When human review is necessary

The Compliance Connection

The DOL has repeatedly emphasized that employers remain liable for discriminatory outcomes—even those produced by automated systems. Whether intentional or algorithmic bias, employers cannot hide behind “the AI made the decision” as a legal defense.

This ties directly to existing compliance obligations under Title VII of the Civil Rights Act, the Age Discrimination in Employment Act (ADEA), and the Americans with Disabilities Act (ADA). The framework essentially says: If you use AI in employment decisions, you must demonstrate you understand potential discrimination risks and have safeguards in place.

Key Areas Where AI Literacy Matters Most

Hiring and Applicant Screening

Resume-screening AI has become increasingly popular among smaller employers seeking efficiency. However, the DOL and EEOC have signaled concern about tools that inadvertently filter out protected classes.

What you need to know: - Request documentation from your vendor about how their algorithm was trained - Ask specifically: What data was used? Were any protected characteristics considered, even indirectly? - Conduct periodic audits comparing AI selections against final hiring outcomes - Maintain records showing you’ve reviewed the tool’s fairness metrics

Performance Management and Ratings

AI-powered systems that track productivity, flag underperformers, or generate performance scores are under increasing scrutiny. The DOL is particularly concerned about tools that might disadvantage older workers or workers with disabilities.

Practical steps: - Don’t rely solely on algorithmic performance scores for promotion, termination, or compensation decisions - Ensure human managers review AI-generated insights with appropriate context - Document why you chose a particular tool and what validation you’ve done - Be transparent with employees about how AI factors into performance evaluation

Compensation and Pay Equity

Algorithmic pay-setting has emerged as a concern, especially regarding equal pay requirements. If your system uses historical data that reflects past discrimination, it can perpetuate wage gaps.

Key consideration: - Understand the data your compensation system uses—does it include historical wage data with inherent inequities? - Regularly conduct pay equity audits independent of your AI system’s recommendations - Ensure pay decisions have human approval, especially for outliers

Building Your AI Literacy Framework: Practical Steps

1. Inventory Your AI Systems

Start by listing every tool or system with AI components you use in HR: - Applicant tracking systems with screening features - Performance management platforms - Scheduling software - Background check services - Wellness programs with predictive features

Many business owners don’t realize how much AI they’re already using—it’s embedded in many standard HR platforms.

2. Request Vendor Transparency

Contact vendors and request information about: - How the AI model was developed and trained - What data sources were used - What variables influence outcomes - Bias testing results - Audit trails showing how decisions are made

Document these conversations. If vendors can’t or won’t provide this information, that’s a red flag.

3. Establish Oversight Mechanisms

Create a simple process for human review of AI-generated decisions in high-stakes situations. For hiring, this might mean: - Having a hiring manager review top candidates flagged by AI, not just accepting the ranking - Auditing decisions quarterly to check for demographic patterns - Maintaining documentation of why specific candidates were selected or rejected

4. Document Your Due Diligence

The DOL framework assumes you’ve been thoughtful about AI adoption. Documentation demonstrates this: - Why you selected a particular tool - What you learned about its limitations - How you’ve addressed identified risks - Training your team received - Periodic audit results

5. Train Your Team

Your HR and management team should understand: - Basic AI concepts relevant to your systems - What biases to watch for - When to override or question AI recommendations - Where to escalate concerns - How to explain AI’s role in decisions to employees

This doesn’t require technical training—focus on practical understanding of your specific tools.

Potential Liabilities You Should Consider

Disparate Impact Claims

Even unintentional AI-driven discrimination can expose you to litigation. The EEOC has enforcement authority, and plaintiffs’ attorneys are increasingly sophisticated about identifying algorithmic bias.

Reputational Risk

Word travels. If employees discover they were screened out by a biased AI tool, damage extends beyond legal liability to talent acquisition and retention.

Regulatory Scrutiny

The DOL and EEOC have expanded AI oversight in recent guidance. Proactive compliance now prevents costly investigations later.

Moving Forward: Your Action Plan

Immediate (Next 30 Days): - Inventory AI tools you’re currently using - Schedule calls with key vendors requesting documentation - Identify your highest-risk tools (those making high-stakes employment decisions)

Short-Term (60-90 Days): - Review vendor documentation; identify gaps - Assess current oversight practices - Develop a basic human-review process for high-risk decisions

Ongoing: - Conduct quarterly audits of AI-driven decisions - Train HR staff on AI literacy basics specific to your tools - Update vendor contracts to include bias-testing and transparency requirements - Stay informed about evolving DOL guidance

Conclusion

The DOL’s AI literacy framework isn’t an additional burden—it’s a practical approach to managing real compliance risks. As AI becomes standard in HR operations, employers who thoughtfully implement these principles will be better positioned legally and operationally.

Start small. Be honest about what you don’t know. Ask good questions of your vendors. Document your efforts. Maintain human judgment in high-stakes decisions. These fundamentals will serve you well as regulations continue evolving.

Nexus Benefit Solutions is an independent employee benefits advisory firm based in West Michigan. Questions? Reach out at jason@nexusbenefitsolutions.com or call 616-425-9740.

What Employers Need to Know About the DOL's New AI Literacy Framework