The Artificial Intelligence Video Interview Act requires employers in Illinois to inform job applicants if they will use AI to evaluate video interviews and disclose which characteristics will be used for the evaluation. The law also requires candidates to consent to AI use before the interview. Video interviews must only be shared with relevant parties, and applicants have the right to request their interview be deleted. Employers must report the race and ethnicity of applicants not selected for in-person interviews or hired after AI analysis. This law only applies to Illinois employers and is not intended to provide legal advice.

The AI Disclosure Act of 2023 is a federal bill introduced by U.S. Representative Ritchie Torres of New York's 15th Congressional District that seeks to create greater transparency around the use of generative AI. The bill requires any outputs generated by artificial intelligence to be accompanied by a disclaimer indicating that it was generated by AI. Violating this requirement will result in penalties, privileges, and immunities under the Federal Trade Commission Act. The AI Disclosure Act is an important step towards algorithmic transparency, but it is not the first initiative to increase algorithmic transparency. Other initiatives include the Illinois Artificial Intelligence Video Interview Act, New York City Local Law 144, Maryland’s HB1202, and the EU AI Act. Organizations using AI should prepare for compliance with transparency requirements in advance to ensure compliance.

Artificial Intelligence (AI) has transformed numerous industries, but it also poses risks that require strong regulations to mitigate. Governments across the world are ramping up efforts to ensure AI's responsible development and deployment. This blog provides an in-depth overview of AI regulations in Europe, the United States, and Canada, focusing primarily on high-risk AI applications. The EU AI Act establishes a risk-based framework that prohibits AI systems with unacceptable risks and imposes stringent obligations on high-risk AI systems. The Algorithmic Accountability Act mandates companies to identify and resolve AI biases in the US, focusing on Automated Decision Systems (ADS) used for Critical Decisions (ACDPs). The Stop Discrimination by Algorithms Act prohibits the use of algorithms that make decisions based on protected characteristics in Washington DC. Assembly Bill 331 in California seeks to regulate automated decision tools that contribute to algorithmic discrimination. The Artificial Intelligence and Data Act in Canada aims to establish a risk-based regulatory approach for AI systems that may adversely affect human rights or pose risks of harm. The article concludes with the importance of early compliance for businesses and the role of Holistic AI in ensuring compliance with upcoming AI regulations.

New York City's Local Law 144 requires employers and employment agencies that use automated employment decision tools (AEDTs) to obtain an independent, impartial bias audit annually. The law applies to any computational process derived from machine learning, statistical modelling, data analytics, or artificial intelligence that results in a simplified output used to substantially assist or replace discretionary decision making in employment decisions. Employers must identify if their tools fall under the regulations, identify an independent auditor to conduct the audit, prepare the required data for the audit, and establish notification procedures. Failure to comply could result in limited access to a talent pool in New York City.

California policymakers are proposing modifications to employment regulations to ensure that automated decision systems (ADSs), which are being increasingly used for talent management, do not result in unlawful discrimination based on protected characteristics. ADSs include algorithms used for screening resumes and online tests to measure skills, dexterity, reaction time, and cultural fit. The proposed modifications define ADSs, algorithms, artificial intelligence, machine learning, adverse impact, and proxy. They require evidence of bias testing and retention of records, including ADS data for at least four years. Employers must ensure ADSs measure job-related characteristics and are appropriately validated to avoid unintentional discrimination.