Data Ethics Compliance

Audit Support
for Employers

Preparing yourself for the NYC AI Bias Mandate

Starting April 15th, 2023, employers in NYC must comply with a new law if they use “automated employment decision tools” (AEDT). Local Law 144 (LL 144) states that all companies who wish to use their artificial intelligence (AI) tools in their recruitment process or for career progression purposes must:

Preserving human dignity

Commission an independent, third-party audit of the AEDT for bias against race, ethnicity, and gender within one year of use of the tool

Mitigating unintended discrimination

Communicate the outcome of the audit in a transparent way

Promoting dialogue

Be transparent with job candidates and employees about the use of AEDTs

The law provides for enforcement by the NYC Corporation Counsel, including fines up to $1,500 per violation. Each day an unaudited tool is used constitutes a separate violation.

Why this affects you

Employers who use AI tools for hiring, recruiting or for evaluating an employee’s career progression in NYC are affected by this law. The employer – not the vendor who makes or sells the tool – is responsible for having a bias audit conducted prior to its deployment.

Trilateral Research can help you understand if you are impacted, and offers an all-encompassing, unbiased audit to aid your organization in achieving appropriate compliance. We possess years of proficiency in the fields of data ethics, data protection, and compliance support, and can cater to organizations of all sizes.

Our Independent Bias Audit Methodology


Bias can become embedded in an algorithm when algorithmic models are trained on historical data, and the historical data reflects past discriminatory decisions, policies, and procedures. For example, if from 1970-2000 a company primarily hired or disproportionately promoted men to senior positions, and an algorithm is trained on the dataset comprised of the company’s past hires and promotions to predict what makes a successful candidate, then the algorithm “learns” that being a man is a qualification for hiring or promotion.

Also, datasets can include “proxies” for protected characteristics like race, ethnicity, sex, or gender. A proxy, in this sense, is a substitute for one of these categories, and the presence of proxies can be difficult to identify. Studies reveal that ZIP code, ancestry, disease predisposition, linguistic characteristics, last name, criminal record, socioeconomic status, marital status, education, and occupation can be proxies for a person’s sex, gender, race and ethnicity. If an AI tool learns that these proxies are relevant for hiring, recruiting, or promoting, then its output can lead to discriminatory decision-making. 

Local Law 144 requires that an audit include: (1) a calculation of the selection rate for each race/ethnicity and sex category; and (2) a calculation of the “impact ratio” for each category. The proposed rules also indicate that an intersectional analysis must be conducted. This means an analysis of the impact rate for ethnicity and sex combined (e.g. how African-American women compared to Hispanic men are impacted), in addition to each protected category independently.

Yes. The date of the bias audit and a summary of the results must be made public. Additionally, employers must provide at least 10 business days’ notice to candidates and employees who reside in New York City that an AEDT will be used in connection with a given assessment or decision.

Employers can be liable for a civil penalty of $500 for a first violation and each violation occurring on the same day, and up to $1,500 for each subsequent violation (which includes failure to comply with the transparency and notice requirements).

Why Trilateral Research?

Why Trilateral Research?

Why Trilateral Research?

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