This guide walks through exactly what a Local Law 144 bias audit report must include, how the required metrics are calculated, and how to format and publish the results to meet the City’s standards.

What Is the Purpose of a LL 144 Bias Audit Report?

The bias audit report serves two primary legal functions:

1. To demonstrate that an independent bias audit has been conducted within the past 12 months for any AEDT used to screen candidates or employees for hiring or promotion.

2. To make key audit results publicly accessible on the employer's website, ensuring transparency for job candidates and regulatory bodies.

The report must be published before the AEDT is used and must remain accessible for at least six months after the tool’s last use.

Step 1: Confirm Whether LL 144 Applies to You

Before creating a report, ensure that your situation falls under the scope of LL 144. The law applies if all of the following are true:

• You use an Automated Employment Decision Tool (AEDT) that employs machine learning, statistical modeling, data analytics, or AI to assist or replace human discretion in employment decisions.

• The AEDT is used to screen candidates or employees for hiring or promotion.

• The job is located in New York City, associated with an NYC-based office, or the employment agency is based in NYC.

Step 2: Conduct the Required Bias Audit

A bias audit must be performed by an independent auditor, which is defined as a party with no involvement in developing, distributing, or using the AEDT and no financial interest in the employer or vendor.

There are two types of audits depending on how the AEDT operates:

A. Selection-Based Audit

If the AEDT is used to make yes/no decisions (e.g., selecting candidates for interviews or classifying them as qualified/unqualified), the audit must include:

• No of applicants in each group

• No of selected applicants in each group

• Selection rate for each demographic group

• Impact ratio for each group compared to the group with the highest selection rate

B. Scoring-Based Audit

If the AEDT assigns scores to candidates (e.g., 0–100 fit rating), the audit must include:

• No of applicants in each group

• Scoring rate: % of individuals in each group who scored above the sample median

• Impact ratio: Scoring rate for each group divided by the highest scoring rate

For both types, the audit must disaggregate results by:

• Sex categories

• Race/Ethnicity categories

• Intersectional categories (combinations of sex and race/ethnicity)

Groups with fewer than 2% of the total audit population may be excluded from impact ratio calculations but must still be listed with raw counts and scoring/selection rates.

Step 3: Structure the Report: Required Elements

Below is a comprehensive list of the elements that must appear in the publicly available report, as required by NYC law.

1. Cover Information

Include the following identifiers:

• Employer or employment agency name

• AEDT model name and version

• Distribution date (when the AEDT was first used)

• Bias audit date (when the most recent audit was completed)

• Independent auditor’s name or firm

2. Description of the AEDT

Provide a brief summary of:

• The purpose of the tool (e.g., resume screening, interview scoring)

• Whether the audit used historical data (real-world applicants previously screened) or test data

• A plain-language explanation of the data used

If test data is used, explain why historical data was insufficient and how the test data was generated.

3. Data Summary

Disclose:

• Time range covered by the audit data (for historical data)

• Total number of individuals assessed

• Number of individuals in “unknown” demographic categories

  • These are candidates for whom race/ethnicity or sex was not provided

  • These individuals are excluded from impact-ratio calculations but must still be counted

4. Results Tables

Present results in three separate tables:

A. Sex Categories

| Sex | # Candidates | # Selected / Scoring Rate | Impact Ratio |

B. Race/Ethnicity Categories

| Race/Ethnicity | # Candidates | # Selected / Scoring Rate | Impact Ratio |

C. Intersectional Categories

| Sex | Race/Ethnicity | # Candidates | # Selected / Scoring Rate | Impact Ratio |

For each group, indicate:

• The reference group (with the highest rate, used as denominator for impact ratio)

• A dash (-) for the impact ratio if the group was excluded under the 2% rule

5. Exclusion Notes (If Applicable)

If any categories were excluded due to the 2% threshold, the report must:

• Identify the group(s) excluded

• State the number of applicants in that group

• Provide the selection or scoring rate

• Explain why the exclusion occurred

6. Auditor Independence Statement

Include a declaration that the auditor:

• Is independent and impartial

• Was not involved in developing, using, or profiting from the AEDT

• Has no financial interest in the employer or the vendor

Step 4: Publish the Report

The final report must be made publicly available in a clear and conspicuous manner:

• Post it on the careers section of your company’s or agency’s website

• You may use a direct hyperlink labeled clearly as “Bias Audit Results” or similar

• Keep the report accessible for at least 6 months after the AEDT’s last use

Step 5: Don’t Forget Candidate Notifications

In addition to the public report, LL 144 requires that candidates and employees receive advance notice if an AEDT will be used in their assessment.

This notice must:

• Be delivered at least 10 business days in advance

• Include a description of the characteristics the AEDT evaluates

• Explain how individuals may request a reasonable accommodation or alternative process (if available)

You may deliver this notice via:

• Job postings

• Email or mail

• Website notices (for job applicants)

• Internal policies (for current employees)

Make LL 144 Painless with BiasBeacon

Manually building a compliant bias audit report can be time-consuming, error-prone, and legally risky. That’s why we built BiasBeacon — a simple SaaS platform that automates the entire process.

Just upload your model-score CSV, and within seconds, BiasBeacon generates a DCWP-compliant PDF report that includes:

• Selection or scoring rates by demographic group

• Impact ratios (including sex, race/ethnicity, and intersectional categories)

• Count of unknown demographic entries

• Audit date and AEDT distribution date

• Required exclusions and explanations

• Auditor Independence Statement

The first audit is free, so you can test your data and publish a legally sound report in minutes — no spreadsheet gymnastics, no legal guesswork.

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Final Thoughts

Creating a compliant LL 144 bias audit report is not just about checking legal boxes—it’s a public statement of your commitment to transparency and fairness in the hiring process. While the law does not require you to act on the audit results, ignoring clear disparities can lead to scrutiny under broader anti-discrimination laws.

A well-prepared report is:

• Clear

• Complete

• Legally defensible

• Easy for candidates and regulators to understand

Invest in the process, document everything, and treat the audit as an opportunity to improve—not just comply.

If your organization, in New York City, uses AI-driven systems or automated tools to screen candidates, you’re now required to audit those tools for bias, be transparent about how they’re used, and notify applicants in advance.

This law represents more than a compliance requirement — it’s a sign of how technology, fairness, and employment law are beginning to intersect.

What Are Automated Employment Decision Tools (AEDTs)?

Under Local Law 144, an Automated Employment Decision Tool (AEDT) is any software or algorithm that uses machine learning, AI, or statistical models to help make hiring or promotion decisions.

In plain terms, if your system gives a score, ranking, or recommendation that influences who gets hired, interviewed, or promoted, it’s likely considered an AEDT.

Examples of AEDTs:

• Resume-scanning software that ranks candidates

• AI video interview tools that assess tone or facial expressions

• Predictive analytics systems that score candidates based on performance data

What’s not considered an AEDT:
A tool is generally not an AEDT if it doesn’t use machine-learning or statistical models to substantially assist or replace discretionary decision-making about candidates.
Examples: a plain spam filter, a firewall rule, or a database that stores résumés but never “scores” them.

Does the law even apply to my role?
LL 144 triggers only when an AEDT is used “in the city.” That means, either:
(a) the job is tied to a New York City office (even part-time)
(b) the role is fully remote but associated with an NYC office
(c) the employment agency itself operates in NYC

What Is a Bias Audit?

A bias audit is an independent evaluation designed to check whether an AEDT disproportionately disadvantages any group of people based on race, gender, or other protected characteristics.

Employers must ensure:

• The audit is conducted by an independent, impartial auditor.

• The audit examines how the tool impacts different demographic groups.

• The audit is completed before the AEDT is used

The goal: to make sure your AI tools aren’t introducing unintended discrimination into your hiring process.

At minimum, the audit must publish selection or scoring rates and impact ratios for each sex, race/ethnicity, and intersectional group, plus the number of individuals whose demographic is “unknown.”

Legal Requirements Under Local Law 144

To legally use an AEDT for hiring or promotion in New York City, employers and employment agencies must meet three main conditions:

1. Annual Bias Audit
   The AEDT must have undergone a valid, independent bias audit within the last year.

2. Public Disclosure
   A summary of the most recent audit results (and the tool’s distribution date) must be publicly available on your company’s or agency’s website.

3. Advance Notice to Candidates and Employees

   • Give notice at least 10 business days in advance to NYC-resident candidates or employees that an AEDT will be used.

   • State the specific job qualifications or characteristics the AEDT will evaluate.

   • Include instructions on how to request a reasonable accommodation or alternative selection process.

Penalties for Non-Compliance

Violating Local Law 144 can be costly. Employers or agencies that fail to comply may face:

• Up to $500 for the first violation (and each additional violation on the same day)

• $500–$1,500 for each subsequent violation

Each day a non-compliant AEDT is used—and each missed notice to a candidate or employee—counts as a separate violation.

Remember: The employer or employment agency—not the software vendor—is ultimately liable for completing a valid bias audit before using the tool.

Enforcement and Legal Rights

The NYC Department of Consumer and Worker Protection (DCWP) enforces LL 144; discrimination claims still go to the NYC Commission on Human Rights.

Importantly, Local Law 144 does not replace or limit an employee’s or candidate’s right to:

• File a lawsuit under other employment laws, or

• Bring a complaint under the NYC Human Rights Law for discriminatory practices.

When the Law Took Effect

Local Law 144 officially went into effect on January 1, 2023. Employers and employment agencies operating in New York City should now have compliant policies and documentation in place — including bias audit records, public disclosures, and clear notification procedures. Enforcement began on July 5, 2023.

Quick Compliance Checklist

Here’s a simple checklist to help ensure your organization meets the law’s requirements:

• Conduct an independent bias audit of every AEDT used in hiring or promotions every 12 months.

• Publish the audit summary and tool distribution date on your website.

• Provide 10 days’ advance notice to NYC-resident candidates and employees before using the tool.

• Disclose evaluation criteria and allow alternative processes or accommodations.

• Keep documentation of all compliance activities.

• If any demographic group makes up < 2 % of your data, document why it was excluded from audit calculations.

Make LL 144 painless

BiasBeacon acts as your independent auditor: drop in your model-score CSV and, minutes later, download a DCWP-ready PDF bias-audit report—complete with selection rates, impact ratios, unknown counts, audit date, distribution date, and an Auditor Independence Statement. Publish the file, prove compliance, move on.

Final Thoughts

Local Law 144 marks a turning point in how technology and employment law intersect. As AI-driven hiring tools become more common, cities and states are beginning to demand transparency and accountability from employers.

Organizations that take the lead on compliance — by auditing their tools, updating notices, and prioritizing fairness — won’t just avoid penalties. They’ll build trust with candidates and demonstrate a genuine commitment to ethical hiring practices in the age of AI.