At first glance, artificial intelligence and job hiring seem like a match made in employment equity heaven.<\/p>\n
There\u2019s a compelling argument for AI\u2019s ability to alleviate hiring discrimination: Algorithms can focus on skills and exclude identifiers that might trigger unconscious bias, such as name, gender, age and education. AI proponents say this type of blind evaluation would promote workplace diversity.<\/p>\n
AI companies certainly make this case.<\/p>\n
HireVue, the automated interviewing platform, boasts \u201cfair and transparent hiring\u201d in its offerings of automated text recruiting and AI analysis of video interviews. The company says humans are inconsistent in assessing candidates, but \u201cmachines, however, are consistent by design,\u201d which, it says, means everyone is treated equally.<\/p>\n
Paradox offers automated chat-driven applications as well as scheduling and tracking for applicants. The company pledges to only use technology that is \u201cdesigned to exclude bias and limit scalability of existing biases in talent acquisition processes.\u201d<\/p>\n
Beamery recently launched TalentGPT, \u201cthe world\u2019s first generative AI for HR technology,\u201d and claims its AI is \u201cbias-free.\u201d<\/p>\n
All three of these companies count some of the biggest name brand corporations in the world as clients: HireVue works with General Mills Read<\/strong>: Jobs in artificial intelligence: Workers are looking to ride the wave, and employers are hiring<\/p>\n AI brands and supporters tend to emphasize how the speed and efficiency of AI technology can aid in the fairness of hiring decisions. An article from October 2019 in the Harvard Business Review asserts that AI has a greater capacity to assess more candidates than its human counterpart \u2014 the faster an AI program can move, the more diverse candidates in the pool. The author \u2014 Frida Polli, CEO and co-founder of Pymetrics, a soft-skills AI platform used for hiring that was acquired in 2022 by the hiring platform Harver \u2014 also argues that AI can eliminate unconscious human bias and that any inherent flaws in AI recruiting tools can be addressed through design specifications.<\/p>\n These claims conjure up the rosiest of images: human resource departments and their robot buddies solving discrimination in workplace hiring. It seems plausible, in theory, that AI could root out unconscious bias, but a growing body of research shows the opposite may be more likely.<\/p>\n The problem is AI could be so efficient in its abilities that it overlooks nontraditional candidates \u2014 ones with attributes that aren\u2019t reflected in past hiring data. A resume for a candidate falls by the wayside before it can be evaluated by a human who might see value in skills gained in another field. A facial expression in an interview is evaluated by AI, and the candidate is blackballed.<\/p>\n \u201cThere are two camps when it comes to AI as a selection tool,\u201d says Alexander Alonso, chief knowledge officer at the Society for Human Resource Management (SHRM). \u201cThe first is that it is going to be less biased. But knowing full well that the algorithm that\u2019s being used to make selection decisions will eventually learn and continue to learn, then the issue that will arise is eventually there will be biases based upon the decisions that you validate as an organization.\u201d<\/p>\n In other words, AI algorithms can be unbiased only if their human counterparts consistently are, too.<\/p>\n Read:<\/strong> Help wanted: No over-50s need apply<\/p>\n More than two-thirds (79%) of employers that use AI to support HR activities say they use it for recruitment and hiring, according to a February 2022 survey from SHRM.<\/p>\n Companies\u2019 use of AI didn\u2019t come out of nowhere: For example, automated applicant tracking systems have been used in hiring for decades. That means if you\u2019ve applied for a job, your resume and cover letter were likely scanned by an automated system. You probably heard from a chatbot at some point in the process. Your interview might have been automatically scheduled and later even assessed by AI.<\/p>\n Employers use a bevy of automated, algorithmic and artificial intelligence screening and decision-making tools in the hiring process. AI is a broad term, but in the context of hiring, typical AI systems include \u201cmachine learning, computer vision, natural language processing and understanding, intelligent decision support systems and autonomous systems,\u201d according to the U.S. Equal Employment Opportunity Commission. In practice, the EEOC says this is how those systems might be used:<\/p>\n Also see: <\/strong>Who\u2019s most likely to lose their job to AI?<\/p>\n AI has the potential to make workers more productive and facilitate innovation, but it also has the capacity to exacerbate inequality, according to a December 2022 study by the White House\u2019s Council of Economic Advisers.<\/p>\n The CEA writes that among the firms spoken to for the report, \u201cOne of the primary concerns raised by nearly everyone interviewed is that greater adoption of AI driven algorithms could potentially introduce bias across nearly every stage of the hiring process.\u201d<\/p>\n An October 2022 study by the University of Cambridge in the U.K. found that the AI companies that claim to offer objective, meritocratic assessments are false. It posits that anti-bias measures to remove gender and race are ineffective because the ideal employee is, historically, influenced by their gender and race. \u201cIt overlooks the fact that historically the archetypal candidate has been perceived to be white and\/or male and European,\u201d according to the report.<\/p>\n One of the Cambridge study\u2019s key points is that hiring technologies are not necessarily, by nature, racist, but that doesn\u2019t make them neutral, either.<\/p>\n \u201cThese models were trained on data produced by humans, right? So like all of the things that make humans human \u2014 the good and the less good \u2014 those things are going to be in that data,\u201d says Trey Causey, head of AI ethics at the job search site Indeed. \u201cWe need to think about what happens when we let AI make those decisions independently. There\u2019s all kinds of biases coded in that the data might have.\u201d<\/p>\n There have been some instances in which AI has shown to demonstrate bias when put into practice:<\/p>\n Read more:<\/strong> Biden administration regulators warn AI, employee surveillance tools could \u2018turbocharge\u2019 fraud and discrimination<\/p>\n In one instance, a company had to make changes to its platform based on allegations of bias. In March 2020, HireVue discontinued its facial analysis screening \u2014 a feature that assessed a candidate\u2019s abilities and aptitudes based on facial expressions \u2014 after a complaint was filed in 2019 with the Federal Trade Commission (FTC) by the Electronic Privacy Information Center.<\/p>\n When HR professionals are choosing which tools to use, it\u2019s critical for them to consider what the data input is \u2014 and what potential there is for bias surfacing in those models, says Emily Dickens, chief of staff and head of government affairs at SHRM.<\/p>\n \u201cYou can\u2019t use any of the tools without the human intelligence aspect,\u201d she says. \u201cFigure out where the risks are and where humans insert their human intelligence to make sure that these [tools] are being used in a way that\u2019s nondiscriminatory and efficient while solving some of the problems we\u2019ve been facing in the workplace about bringing in an untapped talent pool.\u201d<\/p>\n Also see: <\/strong>Must be \u2018fit and active\u2019 or \u2018digital native\u2019: how ageist language keeps older workers out<\/p>\n What does the talent pool think about AI? Response is mixed. Those surveyed in an April 20 report by Pew Research Center, a nonpartisan American think tank, seem to see AI\u2019s potential for combatting discrimination, but they don\u2019t necessarily want to be put to the test themselves.<\/p>\n Among those surveyed, roughly half (47%) said they feel AI would be better than humans in treating all job applicants in the same way. Among those who see bias in hiring as a problem, a majority (53%) also said AI in the hiring process would improve outcomes.<\/p>\n But when it comes to putting AI hiring tools into practice, paradoxically, more than 40% of survey respondents said they oppose AI reviewing job applications, and 71% say they oppose AI being responsible for final hiring decisions.<\/p>\n \u201cPeople think a little differently about the way that emerging technologies will impact society versus themselves,\u201d says Colleen McClain, a research associate at Pew.<\/p>\n The study also found 62% of respondents said AI in the workplace would have a major impact on workers over the next 20 years, but only 28% said it would have a major impact on them personally. \u201cWhether you\u2019re looking at workers or not, people are far more likely to say is AI going to have a major impact, in general? \u2018Yeah, but not on me personally,\u2019\u201d McClain says.<\/p>\n That\u2019s all aside from the anxiety workers are feeling about the\u00a0impact of AI on their jobs.<\/p>\n AI\u2019s potential for perpetuating bias in the workplace has not gone unnoticed by government officials, but the next steps are hazy.<\/p>\n The first agency to officially take notice was the EEOC, which launched an initiative on AI and algorithmic fairness in employment decisions in October 2021 and held a series of listening sessions in 2022 to learn more. In May, the EEOC provided more specific guidance on the usage of algorithmic decision-making software and its potential to violate the Americans with Disabilities Act and in a separate assistance document for employers said that without safeguards, these systems \u201crun the risk of violating existing civil rights laws.\u201d<\/p>\n The White House had its own approach, releasing its \u201cBlueprint for an AI Bill of Rights,\u201d which asserts, \u201cAlgorithms used in hiring and credit decisions have been found to reflect and reproduce existing unwanted inequities or embed new harmful bias and discrimination.\u201d On May 4, the White House announced an independent commitment from some of the top leaders in AI \u2014 Anthropic, Google, Hugging Face, Microsoft, NVIDIA, OpenAI and Stability AI \u2014 to have their AI systems publicly evaluated to determine their alignment with the AI Bill of Rights.<\/p>\n Even stronger language came out of a joint statement by the FTC, Department of Justice, Consumer Financial Protection Bureau and EEOC on April 25, in which the group reasserted its commitment to enforcing existing discrimination and bias laws. The agencies outlined some potential issues with automated systems, including:<\/p>\n From the archives (Aug. 2020):<\/strong> Most white people don\u2019t believe racial discrimination exists at their workplace, but nearly half of Black employees disagree<\/p>\n Law regulating AI is sparse. There are, of course, equal opportunity and anti-discrimination laws that can be applied to AI-based hiring practices. Otherwise, there are no specific federal laws regulating the use of AI in the workplace \u2014 or requirements that employers disclose the use of the technology, either.<\/p>\n For now, that leaves municipalities and states to shape the new regulatory landscape. Two states have passed laws related to consent in video interviews: Illinois has had a law in place since January 2020 that requires employers to inform and get consent from applicants about use of AI to analyze video interviews. Since 2020, Maryland has banned employers from using facial recognition service technology for prospective hires unless the applicant signs a waiver.<\/p>\n Thus far, there\u2019s only one place in the U.S. that has passed a law specifically addressing bias in AI hiring tools: New York City. The law requires a bias audit of any automated employment decision tools. How this law will be executed remains unclear because companies don\u2019t have guidance on how to choose reliable third-party auditors. The city\u2019s Department of Consumer and Worker Protection will start enforcing the law July 5.<\/p>\n Additional laws are likely to come. Washington, D.C., is considering a law that would hold employers accountable for preventing bias in automated decision-making algorithms. In California, two bills that aim to regulate AI in hiring were introduced this year. And in late December, a bill was introduced in New Jersey that would regulate the use of AI in hiring decisions to minimize discrimination.<\/p>\n At the state and local level, SHRM\u2019s Dickens says, \u201cThey\u2019re trying to figure out as well whether this is something that they need to regulate. And I think the most important thing is not to jump out with overregulation at the cost of innovation.\u201d<\/p>\n Because AI innovation is moving so quickly, Dickens says, future legislation is likely to include \u201cflexible and agile\u201d language that would account for unknowns.<\/p>\n Plus<\/strong>: What skill sets are needed for workers in the AI era<\/p>\n Saira Jesani, deputy executive director of the Data & Trust Alliance, a nonprofit consortium that guides responsible applications of AI, describes human resources as a \u201chigh-risk application of AI,\u201d especially because more companies that are using AI in hiring aren\u2019t building the tools themselves \u2014 they\u2019re buying them.<\/p>\n \u201cAnyone that tells you that AI can be bias-free \u2014 at this moment in time, I don\u2019t think that is right,\u201d Jesani says. \u201cI say that because I think we\u2019re not bias-free. And we can\u2019t expect AI to be bias-free.\u201d<\/p>\n But what companies can do is try to mitigate bias and properly vet the AI companies they use, says Jesani, who leads the nonprofit\u2019s initiative work, including the development of Algorithmic Bias Safeguards for Workforce. These safeguards are used to guide companies on how to evaluate AI vendors.<\/p>\n She emphasizes that vendors must show their systems can \u201cdetect, mitigate and monitor\u201d bias in the likely event that the employer\u2019s data isn\u2019t entirely bias-free.<\/p>\n \u201cThat [employer] data is essentially going to help train the model on what the outputs are going to be,\u201d says Jesani, who stresses that companies must look for vendors that take bias seriously in their design. \u201cBringing in a model that has not been using the employer\u2019s data is not going to give you any clue as to what its biases are.\u201d<\/p>\n More:<\/strong> Will AI cause mass unemployment? What history says about technology and jobs<\/p>\n AI is evolving quickly \u2014 too fast for this article to keep up with. But it\u2019s clear that despite all the trepidation about AI\u2019s potential for bias and discrimination in the workplace, businesses that can afford it aren\u2019t going to stop using it.<\/p>\n Public alarm about AI is what\u2019s top of mind for Alonso at SHRM. On the fears dominating the discourse about AI\u2019s place in hiring and beyond, he says:<\/p>\n \u201cThere\u2019s fear-mongering around \u2018We shouldn\u2019t have AI,\u2019 and then there\u2019s fear-mongering around \u2018AI is eventually going to learn biases that exist amongst their developers and then we\u2019ll start to institute those things.\u2019 Which is it? That we\u2019re fear-mongering because it\u2019s just going to amplify [bias] and make things more effective in terms of carrying on what we humans have developed and believe? Or is the fear that eventually AI is just going to take over the whole world?\u201d<\/p>\n Alonso adds, \u201cBy the time you\u2019ve finished answering or deciding which of those fear-mongering things or fears you fear the most, AI will have passed us long by.\u201d<\/p>\n
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\n Unilever
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\n Mercedes-Benz and St. Jude Children\u2019s Research Hospital; Paradox has Amazon
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\n CVS
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\n McDonald\u2019s
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\n Nestle
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\n and Unilever on its roster; while Beamery partners with Johnson & Johnson
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\n McKinsey & Co., PNC
\n PNC,
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\n Verizon
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\n How AI is used in hiring<\/strong><\/h2>\n
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How AI could perpetuate workplace bias<\/strong><\/h2>\n
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\n MSFT,
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\n Face API \u2014 interpreted Black applicants as having more negative emotions than their white counterparts.<\/p>\n<\/li>\nPublic opinion is generally mixed<\/strong><\/h2>\n
Government officials raise red flags<\/strong><\/h2>\n
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AI in hiring is under-regulated<\/strong><\/h2>\n
How businesses will respond<\/strong><\/h2>\n
So will the HR robots take over or not?<\/strong><\/h2>\n