The New York City Department of Consumer and Worker Protection will enforce its final rules on the Bias Audit Law beginning on July 5, 2023. These rules clarify definitions, modify the calculation of scores, and establish new regulations for independent auditors. The definition for "machine learning, statistical modelling, data analytics, or artificial intelligence" has been expanded, and the requirement for inputs and parameters to be refined through cross-validation or training and testing data has been removed. The adopted rules also require auditors to indicate missing data and exclude categories that comprise less than 2% of the data while justifying the exclusion. The summary of results must also include the number of applicants in each category. Historical data may only be utilized if the employer provides it to the auditor or if the AEDT has never been used before, while test data may only be used if no historical data is available.

The UK Government has published a White Paper outlining a regulatory framework for AI, based on five key principles of safety, transparency, fairness, accountability and contestability. The approach seeks to promote responsible innovation and maintain public trust. The White Paper establishes a multi-regulator sandbox and recommends practical guidance to help businesses put these principles into practice.

In critical areas such as healthcare and self-driving cars where AI is being increasingly used, the efficacy of algorithms is crucial. The measurements of efficacy depend on the type of system and its output. Classification systems rely on metrics such as true and false positives and negatives, accuracy, precision, recall, F1 scores, and area under the operating curve. For regression systems, correlations and root mean square error are used to compare outputs with ground truth scores. The choice of metric depends on the context and type of model being used. Holistic AI's open-source library provides built-in metrics for measuring model performance.

The Society for Human Resource Management (SHRM) and the Society for Industrial and Organizational Psychology (SIOP) held an event discussing the legal and practical implications of using AI-based assessments in hiring. The panel discussed guidelines on how to evaluate and implement AI-based tools for recruitment and legal and ethical implications of using AI-based assessments in hiring practices. Key themes that emerged were compliance with Federal EEO laws, practical challenges in using AI-based assessments, and challenges in complying with the Uniform Guidelines on Employee Selection Procedures. The use of AI and other automated and algorithmic tools in recruitment will soon be even more strictly regulated than traditional hiring practices, with policymakers across the US and EU introducing legislation that will have important implications for employers across the world using these tools.

Spain is actively regulating AI through various initiatives, including launching the first regulatory sandbox for the EU AI Act to create a controlled environment for experimenting with AI obligations, publishing a National AI Strategy, establishing Europe's first AI Supervisory Agency, and passing a Rider Law to give delivery riders employment rights. The Spanish government is investing in these regulatory efforts and has set specific objectives to reduce social inequality and promote innovation while protecting individual and collective rights. These regulations aim to increase transparency and accountability for algorithmic systems and ensure compliance with upcoming AI legislation.