facial recognition act<\/a> in employment, which basically said if you\u2019re going to use facial recognition technology during an interview to assess if they\u2019re smiling or blinking, then you need to get consent. They made it more difficult to do [so] for that purpose.<\/p>\n“You can see how fragmented the laws are, where Illinois is saying we\u2019re going to worry about this one aspect of an application for facial recognition in an interview setting. New York is saying our laws are designed for hiring and promotion in these categories. So, OK, I\u2019m not going to use facial recognition technology in Illinois, and I\u2019ll audit for hiring and promotion in New York. But, look, the federal government says you still have to be compliant with all the civil rights laws.<\/p>\n
“You could have been doing this since the 1960s, because all these tools are doing is scaling employment decisions. Whether the AI technology is making all the employment decisions or one of many factors in an employment decision; whether it\u2019s simply assisting you with information about a candidate or employer that otherwise you wouldn\u2019t have been able to ascertain without advanced machine learning looking for patterns that a human couldn\u2019t have fast enough. At the end of the day, it\u2019s an employment decision and at the end of the day, only an employer can make an employment decision.”<\/p>\n
So, where does the liability for ensuring AI-infused or machine learning tools lie?\u00a0<\/strong>“All the liability rests with the employer in the same way it rested with HR using a pencil and paper back in the 1960s. You cannot lose sight that these are just employment decisions being made faster, more efficiently, with more data, and potentially with more transparency. But [hiring] has been regulated for a long time.<\/p>\n“With the uncertain future of federal AI legislation and where it may go, where the EU\u2019s legislation may go, and as more states take this on \u2014 California, New Jersey, and New York State wants to get involved \u2014 you can\u2019t just sit back and say well, there\u2019s not certainty yet in AI law. You can\u2019t think there\u2019s no AI regulatory body that a senator wants to create; there\u2019s no EU law that will require me to do one, two, three before using it, and think, ‘We can just wait and implement this software like we do other software.’ That\u2019s just not true.<\/p>\n
“When you’re dealing with HR, you\u2019re dealing with civil rights in the workplace. You\u2019re dealing with a person\u2019s ability to enter and thrive in the workforce and provide for their family, which is different from other uses. I\u2019m telling you that are laws in existence and will continue to be in existence that employers are familiar with, we just need to apply them to these HR tools in the same way we would with any other employment decision.”<\/p>\n
Do you believe New York\u2019s Local Law 144 is a good baseline or foundation for other laws to mimic?\u00a0<\/strong>“I think Local Law 144 is raising the awareness of the ability for employers to do employment audits. I think it\u2019s a good thing, in the sense that now employers in New York who are hiring are being forced to do an audit. It raises awareness that whether or not you\u2019re being forced to do it, it\u2019s good compliance.<\/p>\n“Just because a local government is forcing you to do an audit, doesn\u2019t mean you cannot do it yourself. In the sense that employers are now recognizing and investing in how to get AI compliant before it makes a decision involving someone\u2019s livelihood, it\u2019s developing this framework of how to audit AI pre-deployment, post-deployment and how [to] test it. How do we create the framework for AI broadly, whether it\u2019s being used in employment, housing, or credit? It gets companies more familiar with not only spending the resources needed to build these systems or buy them, but the implementation side of it has a compliance aspect to it.<\/p>\n
“I think it\u2019s raising awareness in a positive way of performing audits to prevent discrimination. If you find the job candidate recommendation algorithm has a factor in there that\u2019s not necessary for the job, but instead is eliminating a certain class of workers who are qualified but excluded because of age, or race, or national origin, or whatever the algorithm is picking up \u2014 if you can see that and prevent it and tweak it, whether by changing the job description or doing more recruiting for certain areas to ensure you have the inclusive job applicant pool or just ensuring the job parameters are necessary \u2014 that\u2019s preventing discrimination.<\/p>\n
“A big part of our mission here at the EEOC, even though people look at us as an enforcement agency \u2014 which we are \u2014 is to prevent discrimination and promote equal opportunity in the workplace. Doing these audits in the first place can prevent that.”<\/p>\n
What makes an AI applicant tracking system problematic in the first place?\u00a0<\/strong>“A true ATS systems is just going to be a repository of applications and how you look at them. It\u2019s what you\u2019re doing with that data set that can lead to problems, and how you\u2019re implementing the AI on that data set, and what characteristics you\u2019re looking for within that pool and how does it get you the flow of candidates. In that funnel from the ATS to who you\u2019re going to select for the job is where AI can be helpful. Many times when we\u2019re looking at a job description or a job recommendation, or the requirements for that job, those in some cases haven\u2019t been updated in years or even decades. Or a lot of times they\u2019ve just been copied and pasted from a competitor. That has the potential to discriminate because you don\u2019t know if you\u2019re copying a job description that may have historical biases.<\/p>\n“The EEOC is going to look at that and simply say, what were the results? If the results were discrimination, you have the burden of going through every aspect of the characteristics you put into that ATS and can you prove that\u2019s necessary for the job in that location based upon the applicant pool?<\/p>\n
“So, it\u2019s not as much the ATS systems that can be problematic, but what machine learning tools are scanning the ATS systems and if it wasn\u2019t a diverse pool of applicants in the first place. That\u2019s a long-winded way of asking: how are you getting into that ATS system and then once the applicant is in that system, what are they being rated on? You can see how historically biases can prevent some [people] from getting into those systems in the first place, and then once you\u2019re in the ATS system, and the next level is what skills or recommendations are not necessary but are discriminatory?”<\/p>\n
http:\/\/www.computerworld.com\/category\/security\/index.rss<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"![](\"https:\/\/images.idgesg.net\/images\/article\/2023\/07\/shutterstock_2125941194-100944017-small.jpg\"\/)
<\/p>\n\n\nKeith Sonderling, commissioner of the US Equal Employment Opportunity Commission (EEOC), has for years been sounding the alarm about the potential for artificial intelligence (AI) to run afoul of federal anti-discrimination laws such as the Civil Rights Act of 1964.<\/p>\n
It was not until the advent of ChatGPT, Bard, and other popular generative AI tools, however, that local, state and national lawmakers began taking notice \u2014 and companies became aware of the pitfalls posed by a technology that can automate efficiencies in the business process.<\/p>\n
Instead of speeches he’d typically make to groups of chief human resource officers or labor employment lawyers, Sonderling has found himself in recent months talking more and more about AI. His focus has been on how companies can stay compliant as they hand over more of the responsibility for hiring and other aspects of corporate HR to algorithms that are vastly faster and capable of parsing thousands of resumes in seconds.<\/p>\n
To read this article in full, please click here<\/a><\/p>\n<\/section>\n<\/article>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"colormag_page_container_layout":"default_layout","colormag_page_sidebar_layout":"default_layout","footnotes":""},"categories":[11062,10643],"tags":[11113,11063,11070,29835,11067,18384],"class_list":["post-22577","post","type-post","status-publish","format-standard","hentry","category-computerworld","category-independent","tag-artificial-intelligence","tag-data-privacy","tag-emerging-technology","tag-generative-ai","tag-government-it","tag-it-leadership"],"_links":{"self":[{"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/posts\/22577","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/comments?post=22577"}],"version-history":[{"count":0,"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/posts\/22577\/revisions"}],"wp:attachment":[{"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/media?parent=22577"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/categories?post=22577"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/tags?post=22577"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}