“The EEOC is keenly aware that [artificial intelligence and algorithmic decision-making] tools may mask and perpetuate bias or create new discriminatory barriers to jobs. We must work to ensure that these new technologies do not become a high-tech pathway to discrimination.”

Statement from EEOC Chair Charlotte A. Burrows in late October 2021 announcing the employment agency’s launching an initiative to ensure artificial intelligence (AI) and other emerging tools used in hiring and other employment decisions comply with federal civil rights laws.

The EEOC is not alone in its concerns about the use of AI, machine learning, and related technologies in employment decision-making activities. On March 25, 2022, California’s Fair Employment and Housing Council discussed draft regulations regarding automated-decision systems. The draft regulations were informed by testimony at a hearing last year the Department of Fair Employment and Housing (DFEH) held on Algorithms & Bias.

Algorithms are increasingly making significant impacts on people’s lives, including in connection with important employment decisions, such as job applicant screening. Depending on the design of these newer technologies and the data used, AI and similar tools risk perpetrating biases that are hard to detect. Of course, the AI conundrum is not limited to employment. Research in the US and China, for example, suggests AI biases can lead to disparities in healthcare.

Under the draft regulations, the DFEH attempts to update its regulations to include newer technologies such as algorithms it refers to as an “automated decision system” (ADS). The draft regulation defines ADS as: a computational process, including one derived from machine-learning, statistics, or other data processing or artificial intelligence techniques, that screens, evaluates, categorizes, recommends, or otherwise makes a decision or facilitates human decision making that impacts employees or applicants.

Examples of ADS include:

  • Algorithms that screen resumes for particular terms or patterns
  • Algorithms that employ face and/or voice recognition to analyze facial expressions, word choices, and voices
  • Algorithms that employ gamified testing that include questions, puzzles, or other challenges are used to make predictive assessments about an employee or applicant to measure characteristics including but not limited to dexterity, reaction time, or other physical or mental abilities or characteristics
  • Algorithms that employ online tests meant to measure personality traits, aptitudes, cognitive abilities, and/or cultural fit

The draft regulations would make it unlawful for an employer or covered entity to use qualification standards, employment tests, ADS, or other selection criteria that screen out or tend to screen out an applicant or employee or a class of applicants or employees based on characteristics protected by the Fair Employment and Housing Act (FEHA), unless the standards, tests, or other selection criteria, are shown to be job-related for the position in question and are consistent with business necessity.

The draft regulations include rules for both the applicant selection and interview processes. Specifically, the use of and reliance upon ADS that limit or screen out or tend to limit or screen out applicants based on protected characteristics may constitute a violation of the FEHA.

The draft regulations would expand employers’ record-keeping requirements by requiring them to include machine-learning data as part of the record-keeping requirement, and by extending the retention period for covered records under the current regulations from two to four years. Additionally, the draft regulations would add a record retention requirement for any person “who engages in the advertisement, sale, provision, or use of a selection tool, including but not limited to an automated-decision system, to an employer or other covered entity.” These persons, who might include third-party vendors supporting employers’ use of such technologies, would be required to retain records of the assessment criteria used by the ADS for each employer or covered entity.

During the March 25th meeting, it was stressed that the regulations are intended to show how current law applies to new technology and not intended to propose new liabilities. This remains to be seen as the effect of these new regulations, if adopted, could expand exposure to liability or at least more challenges to employers leveraging these technologies.

The regulations are currently in the pre-rule-making phase and the DFEH is accepting public comment on the regulations. Comments about the regulations can be submitted to the Fair Employment and Housing Council at FEHCouncil@dfeh.ca.gov.

Jackson Lewis will continue to track regulations affecting employers. If you have questions about the use of automated decision-making in the workplace or related issues, contact the Jackson Lewis attorney with whom you regularly work.