In this blog post, we compare New York City Local Law 144, which will require independent bias audits of automated employment decision tools from the 5 July 2023, and the New Jersey Assembly Bill 4909, which has been recently introduced and will have similar requirements if passed.
New York City’s legislation requiring bias audits of automated employment decision tools is coming into effect in just a few months (5 July 2023) and has left many people wondering – do I need an audit, or am I exempt?
The New York City Council took decisive action to mandate bias audits of automated employment decision tools (AEDTs) used to evaluate employees for promotion or candidates for employment in New York City, signaling that the risks of Artificial Intelligence (AI) are becoming an increasing regulatory concern. Local Law 144, also known as the NYC Bias Audit Law, is the first of its kind to codify independent, impartial bias audits in law.
The blog compares the NYC bias audit law with California’s proposed Workplace Technology Accountability Act and Proposed Modifications to Employment Regulations Regarding Automated-Decision Systems.
Originally due to come into effect on 1st January 2023, the enforcement date of the NYC Bias audit law (Local Law 144) has been pushed back to 5 July 2023. Here’s what you need to know.
NYC has been leading efforts to regulate Automated Employment Decision Tools (AEDTs) through the introduction of Local Law 144, now finalised and set to be enforced from 5 July 2023. This has now been followed at the state level, with New York State introducing Assembly Bill A00567 in January 2023, requiring annual disparate impact analysis (or bias audits) of AEDTs. This blog post compares NYC Local Law 144 with the New York State Assembly's Bill A00567 and highlights where they differ.
Following their proposed rules for the NYC Bias Audit legislation, the Department of Consumer and Worker Protection held a public hearing. In this blog, we summarise the key takeaways from this session.
In recent years, the fairness of automated employment decision tools (AEDTs) has received increasing attention. In November 2021, the New York City Council passed Local Law 144, which mandates bias audits of these systems.
Under Local Law 144, employers and employment agencies are required to commission independent, impartial bias audits of their tools, where, under the latest version of the Department of Consumer and Worker Protection’s (DCWP) proposed rules, bias should be determined using impact ratios based on outcomes for different subgroups. In this blog post, we outline the metrics required to conduct the bias audit, how small sample sizes can pose issues, and how they can be dealt with when carrying out audits.
Local Law 144 takes effect on 5 July 2023. It mandates independent bias audits of automated tools which are used to make or support decisions about hiring candidates or promoting employees within New York City. Learn about how RHR International, a global leadership consulting firm, are working with Holistic AI to prepare for the upcoming deadline.
The New York City (NYC) Department of Consumer and Worker Protection (DCWP) published proposed amendments to Local Law 144, which mandates independent bias audits of ‘automated employment decision tools’ used by employers or employment agencies.
Aware of the NYC Bias Audit legislation, Hired's CTO Dave Walters began vetting AI audit providers, deciding that Holistic AI was just what they were looking for. In his interview with Protocol, Dave outlines the criteria he used in his search for a partner.
Bias refers to unjustified differences in outcomes for different subgroups. To contextualise this, bias in recruitment could take the form of white candidates being hired at a greater rate than non-white when the race is not related to job requirements.
Recruitment tools driven by artificial intelligence (AI) algorithms – including game- or image-based assessments and algorithmically analysed video interviews – are becoming more mainstream, with uptake accelerated by the pandemic. The growing adoption of these tools has led to concerns about how they can be applied ethically and without discrimination.
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