NYC Local Law 144 and AI Hiring Tools: What Every Employer Needs to Know

February 18, 2025 · AIClarum Team

New York City's Local Law 144, which took effect on July 5, 2023, is the first law in the United States to require bias audits for artificial intelligence tools used in employment decisions. It applies to any employer or employment agency that uses an automated employment decision tool (AEDT) to screen candidates for employment in New York City or to evaluate employees who are based in New York City.

What Counts as an AEDT

The law defines an AEDT broadly: any computational process derived from machine learning, statistical modeling, data analytics, or AI that issues simplified output, including a score, classification, or recommendation, that is used to substantially assist or replace discretionary decision-making for employment decisions about candidates or employees. This covers resume screening algorithms, candidate ranking systems, interview video analysis tools, and many assessment platforms.

The Bias Audit Requirement

Covered employers must conduct an independent bias audit of their AEDT within one year before use. The audit must be conducted by an independent auditor and must calculate the selection rate and scoring rate by sex, race, and ethnicity. The audit results — including the selection rate for each category and the scoring rate for scored tools — must be published on the employer's website.

Notice Requirements

Employers must provide at least 10 business days advance notice to candidates who reside in New York City that an AEDT will be used to evaluate them. The notice must explain what the tool does and the characteristics or qualifications it assesses. Candidates must also be provided with an accommodation mechanism if they want a human review instead of AI evaluation.

Penalties and Enforcement

Violations of Local Law 144 are subject to civil penalties ranging from to ,500 per violation, with each day of non-compliance constituting a separate violation. Given that automated hiring tools may process thousands of candidates daily, the potential aggregate liability for non-compliance is significant.

AIClarum HR Compliance Template

AIClarum's HR compliance template provides automated bias audit calculations that satisfy NYC Local Law 144 requirements, pre-formatted audit reports ready for public disclosure, candidate notification workflow management, and continuous fairness monitoring between required annual audits. Our template also covers EEOC guidance on algorithmic selection tools and the emerging requirements under EU AI Act Annex III for employment-related AI systems.

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Key Takeaways

Understanding the core concepts covered in this article is essential for practitioners working in this domain.
Practical implementation requires careful consideration of your specific use case, infrastructure, and team capabilities.
The landscape continues to evolve rapidly; staying current with best practices and emerging research is critical.
Collaboration between technical teams and business stakeholders ensures solutions are both technically sound and business-aligned.
Measurement and iteration are fundamental: define success metrics upfront and continuously evaluate against them.

Implementation Checklist

Before implementing the approaches described in this article, ensure you have addressed the following:

Assess your current state: Document your existing architecture, data flows, and pain points before making changes.
Define success criteria: Establish measurable outcomes that define what success looks like for your organization.
Build cross-functional alignment: Ensure engineering, product, data science, and business teams are aligned on goals and priorities.
Plan for incremental rollout: Adopt a phased approach to reduce risk and enable course correction based on early feedback.
Monitor and iterate: Establish monitoring from day one and create feedback loops to drive continuous improvement.

Frequently Asked Questions

Where should teams start when implementing these approaches?
Begin with a clear problem statement and measurable success criteria. Start small with a pilot project that provides quick feedback, then expand based on learnings. Avoid attempting to solve everything at once.

What are the most common mistakes organizations make?
Common pitfalls include underestimating data quality requirements, neglecting organizational change management, overengineering initial implementations, and failing to establish clear ownership and accountability for outcomes.

How long does it typically take to see results?
Timeline varies significantly by organization size, complexity, and available resources. Most organizations see initial results within 3-6 months for well-scoped pilot projects, with broader impact emerging over 12-18 months as adoption scales.