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UPDATE 1-Biased bots? US lawmakers take on 'Wild West' of AI recruitment

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Summary

The article discusses the increasing use of AI in hiring processes and the potential for discrimination based on historical data. It describes a class action lawsuit against Workday Inc. for alleged discrimination against Black, disabled, and older applicants and discusses new guidelines from the Equal Employment Opportunity Commission (EEOC) and legislation by state and federal authorities to reduce the risk of bias in AI recruitment. It also looks at the risk of ‘algorithmic blackballing’ and potential solutions such as data literacy, audits, and bans on certain types of data collection. Finally, the article examines the failure of a California bill that would have made it easier for applicants to sue and encourages states to take the lead in regulating AI.

Q&As

What regulations are being put in place to prevent algorithmic bias in the US recruitment market?
Regulations being put in place to prevent algorithmic bias in the US recruitment market include the Equal Employment Opportunity Commission (EEOC) releasing new guidelines to help employers prevent discrimination when using automated hiring processes, a novel law to regulate AI in hiring going into force in New York City, and lawmakers from California to Vermont to New Jersey pushing through new legislation.

What are the potential risks of using AI in hiring processes?
Potential risks of using AI in hiring processes include technology-enabled bias, where AI uses algorithms, data and computational models to mimic human intelligence and relies on “training data” that could replicate bias in an AI program, and algorithmic blackballing, where hiring systems repeatedly reject an applicant based on hidden criteria.

How are state and federal authorities in the US responding to the use of AI in automated hiring?
State and federal authorities in the US are responding to the use of AI in automated hiring by introducing new laws and regulations, issuing fines, and issuing statements.

What is the significance of the EEOC's first automation-based case?
The significance of the EEOC's first automation-based case is that it fined iTutorGroup $365,000 for using software to automatically reject applicants over the age of 40, and set a precedent for future cases.

How can AI be used to counteract human bias?
AI can be used to counteract human bias by investing in AI and data literacy to mitigate risks such as discrimination in automated hiring, tweaking the variables that are considered by an automated system, and auditing hiring tools to ban certain kinds of data collection.

AI Comments

👍 This article does an excellent job of exploring the changing landscape of automated hiring processes, and how lawmakers are taking steps to reduce the risk of discrimination.

👎 Despite the efforts of lawmakers, this article does not address the potential for algorithmic bias against applicants who are disabled or over the age of 40.

AI Discussion

Me: It's about a lawsuit that was filed against Workday Inc. for allegedly using AI algorithms that discriminated against Black, disabled, and older applicants. Lawmakers in the U.S. are taking steps to regulate the use of AI in labor hiring and guard against algorithmic bias.

Friend: Wow, that's really concerning. What are the implications of this article?

Me: The implications of this article are that AI is becoming more and more prevalent in the recruitment process, and that there is a risk of discrimination if the algorithms are trained with biased data. As such, there is a need for greater regulation of AI in the recruitment process and for employers to be more aware of the potential for algorithmic bias. Additionally, companies need to invest in AI and data literacy to ensure that potential risks are minimized.

Action items

Research the current laws and regulations in your state or country regarding the use of AI in recruitment.
Reach out to your local representatives to voice your concerns about algorithmic bias in hiring.
Educate yourself on the potential risks of automated hiring processes and how to mitigate them.

Technical terms

AI (Artificial Intelligence): AI is a type of computer technology that is designed to mimic human intelligence and behavior. It is used in a variety of applications, including robotics, natural language processing, and machine learning.
Algorithm: An algorithm is a set of instructions or rules that are used to solve a problem or complete a task. Algorithms are used in computer programming to automate processes and make them more efficient.
Data: Data is information that is collected, stored, and analyzed. It can be used to make decisions, solve problems, and create new products and services.
Automation: Automation is the use of technology to automate processes and tasks that would otherwise be done manually. Automation can be used to reduce costs, increase efficiency, and improve accuracy.
Discrimination: Discrimination is the unfair or unequal treatment of people based on their race, gender, age, religion, or other characteristics. Discrimination can be intentional or unintentional.
Bias: Bias is a prejudice or preference for one thing over another. Bias can be based on a person's beliefs, values, or experiences. It can lead to unfair decisions or actions.