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HR Implications of Biden’s AI Executive Order
Human resource professionals searching for guidance on managing artificial intelligence should pay attention to the Biden administration’s Oct. 30 executive order that seeks to manage the risks of AI and reap its benefits.
As HR executives ponder how they can use AI to create efficiencies, simplify work and find cost savings, the executive order establishes new standards for AI safety and security in federal agencies and requires measures that will impact companies as they apply AI capabilities to their business operations.
The executive order sets an example for the private sector by, among other things, establishing standards and best practices for detecting AI-generated content and authenticating official government communications. It also requires vendors that develop AI software to share their safety test results, which will help government agencies and private companies that use AI tools.
According to the White House, the federal government is taking action to develop principles and best practices to reduce the harm AI can have on workers by providing guidance to “prevent employers from under-compensating workers, evaluating job applications unfairly, or impinging on workers’ ability to organize.”
In an effort to lead the world in AI-driven innovation and competition, the executive order is also directing agencies to make it easier for highly skilled immigrants with expertise in critical areas to study and work in the U.S.
“The administration is addressing key issues to mitigate the fear around AI,” said Tommy Jenkins, vice president of recruiting at San Francisco-based RocketPower. “They see there is going to be a significant impact around labor, and they understand that the executive order has to establish some guidelines around AI because it’s more than just a U.S. based effort, it has to be a global initiative.”
As employers focus on their ability to hire and retain talent to implement, manage and operate AI’s large data models in a responsible way, Jackie Watrous, senior director analyst at Gartner’s HR practice, said there are three areas in the executive order that will affect HR:
- Considering how AI use within an organization may impact jobs and workforce responsibilities. The executive order directs the federal government to assess the impact of AI on the workforce, develop strategies to mitigate any negative impacts, and support programs that help workers develop the skills and knowledge they need to succeed in the AI economy.
- Ensuring that any use of AI tools has undergone the appropriate rigor to prevent discrimination, an area that many HR executives consider to be a priority. Watrous said the executive order “calls for the development of standards and guidelines for the responsible development and use of AI. These standards and guidelines should address the issue of bias and discrimination.”
- Promoting innovation, which may include upskilling existing talent and bringing in AI-skilled talent from outside the U.S.
“The executive order calls for the federal agencies to promote innovation in AI, including by supporting research and development in the field of AI. The order also emphasizes the importance of attracting AI talent from outside the U.S. and enabling accelerated hiring pathways,” Watrous said.
Improving U.S. companies’ ability to scout for AI-related talent overseas aligns with RocketPower’s multinational talent programs.
Many of the initiatives outlined in the wide-ranging executive order will help HR professionals who use AI tools for tasks such as recruiting and hiring talent, as well as analyzing employee data.
The executive order also applies to generative AI tools, which exploded onto the corporate landscape when OpenAI launched ChatGPT in November 2022. Since then, several companies have built their own generative AI tools that create content such as text, images, sound, animation and 3D models.
According to Zachary Chertok, research manager for employee experience at research firm IDC, AI has benefited from many years of technological advancements that have been used to establish use cases inside organizations. On the security front, many technology firms and their clients have already tested technical safeguards, such as encryption, firewalls, data masking and data erasure, to comply with global regulations.
Chertok added, however, that what has recently changed is the introduction of generative AI that went into the public domain without first being subjected to the controlled environment of a technology company’s research and development department.
“The rapid advancement of generative AI as an unknown element elevated public consciousness of AI tools as they now had to be retroactively trained for accuracy, trust and reliability in their use cases, output and outcomes,” he said. “The Biden administration’s executive order calls for the development of standards, tools and tests to help ensure that AI systems are safe, secure and trustworthy including their insights and output.”
Of particular concern is governance for use cases in materials, engineering, finance, health care and the public sector, Chertok said.
“In the wake of GDPR, the HR world already takes individual data anonymity seriously and goes above and beyond to protect sensitive employee information even if the information is technically noncompromising,” Chertok said. “HR data impacts employee behavior, sentiment and subsequent retention, leading HR professionals to be well-versed in raising concerns around sensitivity.”
HR professionals, Chertok predicted, will have more of a role to play in protecting against AI-enabled fraud and deception by establishing standards and best practices for detecting, authenticating and certifying AI-generated content.
“HR is going to be an internal steward of this for employment verifications, employment fraud and misused credentialing,” he said. “AI deception is a risk for persona detection and makes current badging and photo-ID verifications systems vulnerable.”
Chertok added that while many systems already use multi-factor approaches, HR is going to need to stay one step ahead of fraud detection that uses impersonated files and records by leaning into technologies that take it as seriously as they know they will need to.
The good news, according to HR analyst Josh Bersin, is that many of the security issues the executive order points to have to do with human capital management, and HR executives will have to engage other company executives to assist with developing security solutions.
“HR people are going to have to work with the IT department, the legal department and security officers to make sure what they do is acceptable to the rest of the company. These are big legal issues,” Bersin said.
“There’s much more upside than downside. You have to respect the fact that the federal government is trying to establish some ground rules for AI so that we all behave well,” he said.
Nicole Lewis is a freelance journalist based in Miami.
AI in Workplace Is Like the Wild West: Untamed
It’s the Wild West when it comes to using generative artificial intelligence (GenAI) tools—such as ChatGPT—in the workplace, with many workers using these resources without their managers’ knowledge, according to recent surveys.
As with any tool, transparency and training are key to proper usage.
However, a survey earlier this year from San Francisco-based corporate social networking platform Fishbowl found that only one-third (32 percent) of respondents who use AI at work do so with their boss’s knowledge. The 5,067 respondents included workers at Amazon, Bank of America, IBM, JP Morgan and Nike.
Snaplogic, a social career network based in San Mateo, Calif., reported similar findings from a September survey with 904 midlevel office workers in Australia, the U.K. and the U.S. It discovered 40 percent of respondents use these tools without disclosing their usage to colleagues or their employer—but are not necessarily abusing the tools.
“Let’s start by saying there’s no evidence from our survey that people are widely and deliberately misusing GenAI in the workplace,” Snaplogic wrote in its report, Generative AI: Revealing the Wild West Inside Your Own Organization.
More than two-thirds (67 percent) think AI saves them one to five hours of work per week. And while there’s the perception GenAI is used primarily to crunch numbers, Snaplogic found the predominant use is research—sometimes because the employee is too embarrassed to ask a human for help. Nearly one-fourth (22 percent) said they would covertly use GenAI because they didn’t know how to perform a task or have the answer to something. Nearly one-fourth (22 percent) also said they would not use AI if they had to disclose their use.
But transparency works both ways.
Employees want their employer to be open about how AI could directly improve their workflow—something cited by 78 percent of respondents to a survey conducted by thought leadership and research agency Workplace Intelligence in partnership with UKG. Slightly more than half of workers (54 percent) said they have no idea how their company is using AI, according to Dan Schawbel, managing partner at Workplace Intelligence.
“AI is here, and it’s already providing some amazing benefits for the workforce—from automating tedious tasks to answering common questions to helping crunch millions of data points in mere seconds,” he said in a statement about the report.
The report, released in October, is based on a survey conducted in August and September with 4,200 employees in nine countries, including 1,800 respondents in the U.S.
Taking Action
While GenAI usage has gone mainstream, “the bad news is that everyone has hitched their wagon to the horse, without really knowing how to steer the horse,” Snaplogic noted. It recommended:
Providing training so your staff knows how to productively and safely use these tools.
A slight majority (54 percent) of respondents to the Snaplogic survey said they would avoid using AI if it meant sharing confidential information such as product sales with the AI program.
“Even when employees are correctly recognizing security risks such as sharing confidential information, it indicates that their company is failing to provide security-vetted closed systems that remove this issue—and are consequently losing out on GenAI productivity gains,” Snaplogic reported.
Adding “guardrails” to AI use by creating an environment where employees can test AI tools and services in a risk-free zone.
UKG reported that executives at organizations using AI estimated 70 percent of their total workforce will use it to automate or augment some of their tasks by 2028.
Creating clear usage rules and guidance to ensure know when they may and may not use AI tools.
Snaplogic noted there is evidence that “perhaps through lack of guidance or training, some workers are using GenAI in ways that could inadvertently create a risk for their employer.”
But with some guidance, employees and businesses could see significant gains from GenAI.
“Many businesses are finally realizing what great workplaces have known for a long time now,” said Hugo Sarrazin, UKG chief product and technology officer, in a statement about UKG’s report. “AI, when used ethically, responsibly, and transparently, has the potential to be everyone’s favorite co-worker.”
Worker Personas and AI
“Whether you have five or 5,000 people, every workplace has a unique ecosystem of personalities, skill sets and working styles,” said Christina Janzer, Slack’s senior vice president of research and analytics, in a blog post.
Slack and YouGov surveyed more than 15,000 desk workers in Australia, France, Germany, India, Japan, Singapore, South Korea, the U.K. and the U.S. to understand their unique personas and preferences for communicating and how they use AI.
They found employees fall into five distinct personality categories.
The five personas are:
- The Detective (30 percent). This is an investigative, outcome-driven person who is best at digging up information, and driven by finding the right information as quickly as possible.
- The Road Warrior (22 percent). This outgoing personality is skilled at developing connections remotely, is adaptable and flexible, likely works from a new location and values modern workplace tools. They are more likely to work on a geographically distributed team.
- The Networker (22 percent). This is someone who is highly collaborative and most likely to have friends across teams and business units. Most important to them is keeping everyone updated.A Networker in the U.S. said they use AI to help with balancing accounts, analyzing spending trends and pinpointing anomalies, according to Slack and YouGov.
- The Problem Solver (16 percent). This is someone who has a work hack for everything, is an early tech adopter and is best at streamlining work tasks. Saving time and removing repetitive tasks is most important to this personality. The Problem Solver is most likely to seek out AI training on their own from outside sources. Problem Solvers love AI and sharing shortcuts with the team, Slack and YouGov found.
- The Expressionist (10 percent). This person strongly prefers visual and less formal communication, such as emoji and gifs to express tone and personality. They use visual communication that expresses their personality and creates deeper bonds with colleagues, and making sure the tone of their communication is clear is most important to them.
While the Problem Solvers and Expressionists make up smaller groups in the workforce, they are particularly excited about AI and use it to feel more productive. Both also are most likely to plan to look for opportunities to incorporate AI into their work.
Performance Management Tools Should Go Beyond Measurement
Technology has proven indispensable to improving performance management, by providing dashboards, analytics and an automated workflow.
But as important as metrics and measurement are to inform performance, people managers need to be enabled to take actions to improve performance, provide recognition and boost engagement more effectively. 
Jeff Smith, head of product at performance management platform 15Five, talked with SHRM Online about how technology can provide the boost managers need to create more value from performance reviews.
SHRM Online: What are some of the specific ways technology has made performance management tools more meaningful and effective?
Smith: Performance management historically has been focused on measurement. Not as much on taking action and improving the outcomes HR is interested in—performance, engagement, retention. Employers may know that some people are engaged or disengaged, performing well or poorly, but what can they do about it? HR is stuck with countless options. There is a pile of recommendations out there on how to raise your percentage of top performers, how to raise your engagement score, how to raise your retention rate.
But technology can organize all that performance data sitting in the human resource information system (HRIS) and provide coaching steps for managers to expand the influence of HR in the organization. How to conduct effective one-on-one meetings, for example. One-on-ones are often weak status updates, and not actually moving things forward. Technology can provide a certain structure about what topics should be discussed and the ability to track the conversations being had, the behaviors based on those conversations and the perceptions of whether the meetings are effective.
HR leaders have the opportunity to look into what was a black box previously. Managers are often an asset that organizations do not get enough from in the performance management process because of a lack of clear expectations, a lack of enablement and a lack of the technology that will help improve managers at scale.
Technology can improve performance management from an operations perspective, but it can also have a humanizing effect as well, if set up properly. For example, a popular feature of 15Five is what we call our Best-Self Kickoff, a structured one-on-one that should be the first one-on-one a manager has with a direct report. It guides the conversation between managers and employees. You talk about personal things as well as things that will help improve performance, like how employees like to receive feedback. This is often something managers don’t know about their people.
SHRM Online: What kind of data insights can be gleaned from performance management platforms?
Smith: There’s a powerful concept called “thickening the data.” Quantitative data is useful in telling you where to look, but qualitative data can thicken that with context and nuance. We advocate for the combination of both. For example, we offer an engagement survey that has evidence-supported questions, but then employees are asked to elaborate on those responses, thereby providing additional insight. You could find out that the lowest driver of engagement is leader availability, so then you can ask people about that and what can be done to take action on improving that measure.
With generative artificial intelligence (GenAI) I can see that thickening happening at scale. A massive set of qualitative data could be summarized and turned into insights. Performance review ratings can be helpful operationally if designed properly, but should be supplemented with qualitative insight about the person, ideally pulled from a variety of sources and with checking bias in mind.
SHRM Online: Where do you see GenAI making an impact on performance management software?
Smith: There are several different use cases. What if you could have an executive coach in your pocket? That is compelling. GenAI can’t replace human empathy—at least not yet—but it can be helpful. AI can also summarize and prioritize a massive set of comments between managers and employees.
First drafts are another area that we’re seeing progress in. Let’s say a manager is beginning a performance review period and has nine direct reports. It will take some time to write those out. Wouldn’t it be nice to get a first draft of the reviews written based on things already documented about the individuals? It takes all the interactions the manager has had with the employee over the past 12 months and turns those into a perspective on where he’s at regarding performance.
In the more far-off future, it is exciting to think about a manager bot and a direct report bot having the first conversation about performance before the humans get together to refine the conversation based on a review of what the bots discussed. Those are the kinds of things that get me excited about the future of this technology.
AI in the Workplace: Are You Prepared?
Last month, California Gov. Gavin Newsom signed an executive order regarding artificial intelligence. While this action does not carry the weight of legislation or regulation, it should nevertheless prompt employers to recognize that AI has already grabbed and will continue to grab the attention of all levels of government.
When it comes to AI in the workplace, there are steps that employers can take now to ensure compliance with existing laws and get a head start on anticipated regulations. AI can improve workplace efficiency and lead to more consistent, merit-based outcomes in the workforce. However, if the proper safeguards are not in place, AI can perpetuate or augment workplace bias.
Newsom’s Executive Order
Newsom’s executive order directs California state agencies to study the benefits and risks of AI in numerous applications. This study must include an analysis of risks AI poses to critical infrastructure and a cost-benefit assessment regarding how AI can impact California residents’ access to government goods and services.
In the employment context, the executive order instructs the California Labor and Workforce Development Agency to study how AI will impact the state government workforce and asks the agency to ensure the use of AI in state government employment results in equitable outcomes and mitigates “potential output inaccuracies, fabricated text, hallucinations and biases” of AI.
EEOC Guidance on the Use of AI
The executive order’s contemplation of AI hallucinations and biases is a nod to the Equal Employment Opportunity Commission’s (EEOC’s) Artificial Intelligence and Algorithmic Fairness Initiative, launched in 2021. To date, the EEOC has published two technical assistance documents regarding how using AI in the workplace can result in unintentional disparate impact discrimination.
The first guidance, issued in May 2022, concerns the Americans with Disabilities Act (ADA). In this guidance, the EEOC clarified that AI refers to any “machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments.” In the workplace, this definition generally means using software that incorporates algorithmic decision-making to either recommend or make employment decisions. Some common AI tools used by employers include automated candidate sourcing, resume-screening software, chatbots and performance analysis software.
To comply with the ADA, the EEOC explained that employers using AI in the workplace should provide reasonable accommodations to applicants or employees who cannot be rated fairly or accurately by an AI tool. For example, a job applicant who has limited manual dexterity because of a disability may score poorly on a timed knowledge assessment test requiring use of a keyboard, trackpad or other manual input device. Or interview analysis software may unfairly rate an individual with a speech impediment. In both scenarios, the EEOC recommends the employer provide an alternative means of assessment.
The second EEOC guidance, issued May 18, is on the use of AI in compliance with Title VII of the Civil Rights Act of 1964. As related to AI, the EEOC’s primary concern is not with intentional discrimination, but rather with unintentional disparate impact discrimination. In such cases, an employer’s intent is irrelevant. If a neutral policy or practice, such as an AI tool, has a disparate outcome on a protected group, that policy could be unlawful.
Undisciplined use of resume-screening tools is a commonly cited example of how AI can lead to disparate impact discrimination. Used properly, resume screeners can improve efficiency and suggest the best candidates for the job. If the tool, however, is fed with input or training data that favors a particular group, it may exclude individuals who do not satisfy such biased criteria. The tool may also unintentionally favor certain proxies for protected categories—for example, zip codes and race.
Steps to Take Now
Employers using AI should consider action now to position themselves toward compliance with existing law and the likely passage of additional laws. Consider these steps.
1. Be transparent. A common theme in the EEOC’s guidance is that a lack of transparency with applicants and employees can bring about discrimination claims. For example, if an applicant with a disability does not know they are being assessed by an algorithmic tool, they may not have the awareness that allows them to request a reasonable accommodation. EEOC guidance aside, transparency on the use of AI is actually a legal requirement in some jurisdictions—including New York City. In a law that went into effect earlier this year, New York City employers are required to disclose AI use, perform bias audits of its AI tools and publish the results of those audits. Other jurisdictions, including Massachusetts, New Jersey and Vermont, have proposed similar employment-related legislation regarding AI.
2. Vet AI vendors. Employers often cannot defend against discrimination claims simply by saying, “the AI did it.” So it is important that employers ask vendors whether the tool has been designed to mitigate bias and gain as much knowledge as feasible regarding the tool’s functionality. Some vendors may be reluctant to share details, deeming such information proprietary. In those scenarios, employers should either look elsewhere or demand strong indemnity rights in the contract with the vendor.
3. Audit. One way in which AI tools can cause a disparate impact is by using homogenous input data. After determining a set of inputs, such as resumes of high-performing employees, the tool should be audited to ascertain whether it results in disparate impact.
Finally, employers need to stay apprised of developments in the law. Executive orders and guidance documents are often a prelude to legislation and regulatory action. To avoid becoming a test case, it’s a good idea to partner with qualified employment counsel and data scientists when using AI tools in the workplace.
Kevin White and Daniel Butler are attorneys with Hunton Andrews Kurth in Washington, D.C., and Miami, respectively.
LinkedIn Lays Off Nearly 700 Employees
LinkedIn will be laying off 668 U.S. employees across its engineering, product, talent and finance teams, it announced Oct. 16. The move is the latest in a string of tech company layoffs during 2023.
Reduction in force in the tech sector has risen in the U.S. from 93,000 positions in 2022 to more than 153,000 in 2023, according to tech.co, a media company headquartered in London.
“The reasons for the layoffs [in the tech sector] include the current economic climate, overhiring during COVID, and the rise of AI,” tech.co reported on Oct. 13. “However, there has been some slowdown in the number of jobs lost compared to the start of the year. While some companies have blamed financial issues, others have stated that they are starting to replace jobs with AI, a trend that is set to become more and more prevalent thanks to the rise of tools such as ChatGPT and Bard.”
LinkedIn did not provide specific reasons for its staff reductions, but a LinkedIn representative told SHRM Online that “this restructuring is to support LinkedIn broadly.”
In its official announcement, LinkedIn noted that “talent changes are a difficult but necessary and regular part of managing our business.”
Various news reports indicated the cuts include positions in research and development, finance, human resources and engineering management. Most of the cuts, though, come from its engineering department, CNBC reported.
CNBC cited a memo it viewed from LinkedIn executives Mohak Shroff and Tomer Cohen that read: “As we continue to execute on our FY24 plan, we need to also evolve how we work and what we prioritize so we can deliver on the key initiatives we’ve identified that will have an outsized impact in achieving our business goals. This means adapting our organizational structures to improve agility and accountability, establishing unambiguous ownership and driving improved efficiency and transparency through reduced layering.”
The memo also said LinkedIn was “committed to providing [its] full support to all impacted employees during this transition and ensuring that they are treated with care and respect.”
The San Francisco Herald reported on Oct. 11 that LinkedIn has placed the top five floors of its 26-floor downtown skyscraper “on the sublease market,” joining “a host of major tech employers in San Francisco offering up underused space on the sublease market amid the shift to hybrid work.”
In February LinkedIn laid off an undisclosed number of staff in its recruiting department, The Information reported. In May, around the time of the company’s 20th anniversary, LinkedIn shuttered 716 positions in China as it made changes to its global business organization.
The online professional network was launched in 2003, and Microsoft purchased it in 2016. LinkedIn says it has 950 million members in more than 200 countries and territories.
SHRM Online collected the following news articles on this developing story.
LinkedIn Lays Off 668 Employees as Hiring Activity Slows
LinkedIn said on Monday it would lay off 668 employees across its engineering, talent and finance teams as demand for hiring services slows. The cuts, which affect more than 3 percent of the 20,000-strong staff, add to the tens of thousands of job losses this year in the technology sector amid an uncertain economic outlook.
(Reuters)
Read the Email LinkedIn Sent Employees Announcing More than 600 Layoffs Across Its Engineering and Product Teams
LinkedIn gave a breakdown of where the layoffs would take place and said 137 engineering management roles and 38 product roles were getting cut. It added that 368 roles would be eliminated from its engineering team “in an effort to better align resources to our FY24 plan.” Staff were told to expect to receive an email within an hour to find out whether they had been laid off.
LinkedIn Mass Layoffs: Jobs Platform Cuts Another 668 Employees in Third Layoff This Year
This is the second mass layoff at LinkedIn this year, following a decision in May to cut 716 jobs, and the third overall. An undisclosed number of workers on its talent acquisition team were let go in February, as reported then by The Information.
A Vexing Problem
Falling productivity triggers layoffs, employee monitoring and AI investment.
Leading Difficult Conversations About Layoffs
“Laying someone off is one of the hardest things to do but is often necessary to improve operational efficiencies and, as a result, create more runway for a company,” said Serena Ziskroit, fractional chief people officer at Mighty One Holding LLC. In a previous role, she led a 25 percent workforce reduction.
How HR Is Using Generative AI in Performance Management
While more HR pros are using generative artificial intelligence (GenAI) for recruiting, employee communications and learning tasks, they’ve been slower to use it for performance management. HR leaders and industry experts alike have been wary of the potential for creating problems like review bias in using ChatGPT for the sensitive and often highly charged process of providing performance feedback.
HR leaders have begun to install safeguards that govern the technology’s use and are forging ahead to use generative AI in ways that save managers time, make the administrative parts of the performance management process less tedious and seek to improve the outcomes of performance reviews.
SHRM Online spoke with three experts in performance management for insights and real-world examples of how ChatGPT is being used today in organizations and how the technology might be applied in the future to improve the efficiency and effectiveness of the oft-maligned performance review process.
Summarizing Multiple Sources of Performance Data
Performance management experts say one of the most valuable uses of ChatGPT is its ability to summarize multiple sources of both formal and informal employee performance data. The tool can collect data constantly, not just at certain points throughout a year, which helps avoid problems like recency bias.
Kenneth Matos, global director of people science for Culture Amp, a performance management, engagement and development technology platform based in Melbourne, Australia, said generative AI can save managers time and lead to more well-rounded performance evaluations by collecting things like peer- or customer-generated performance data.
“That might include the kudos people receive in Slack channels or email conversations about their work performance as well as comments about skill areas where they need to improve that’s been captured in digitized text,” Matos said. “GenAI can scrape your internal data and put together good performance summaries for managers to review.”
Doug Dennerline, CEO of Betterworks, a performance enablement technology platform in Menlo Park, Calif., said this next-generation AI also can be used to analyze communication and collaboration patterns in companies to help boost performance and improve inclusiveness.
“ChatGPT can become a quintessential organizational network analysis tool, analyzing in real time every conversation happening in the organization from email, Slack, Teams or other communication tools,” Dennerline said. “AI has the power to connect the dots and make sure all parties within organizations are on the same page and no one is left out who shouldn’t be.”
The benefits of generative AI convinced Betterworks to greenlight use of the technology with its own workforce, Dennerline said, with the company also adding protections and implementing training to ensure the tools’ ethical and effective use.
“We have made ChatGPT and similar tools available to all of our employees and made a point of asking them to experiment with them to find ways to be more productive and compare the different tools,” Dennerline said. “We share ideas for how to leverage ChatGPT internally and we recently had an internal hackathon to brainstorm and develop ideas for our performance management solutions.”
The technology also factors heavily into Betterworks’ road map for its performance enablement platform. “We are now using it to write marketing content and for faster generation of code and for code review,” Dennerline said.
Moving the Focus of Performance Reviews from Paperwork to Conversations
Experts say generative AI, when used effectively, not only can save managers time, but also helps create more understandable and thus actionable feedback for employees. One way it does that is by taking bare-bones bulleted lists or notes compiled by managers about employee performance and—once managers use good prompts—transforms them into a more comprehensive and cohesive performance review draft.
“It makes it easier for managers to add more depth or color to components of their feedback and often articulate things in a clearer fashion,” said Cara Brennan Allamano, chief people officer for Lattice, a performance management platform in San Francisco. “Because they’re not spending as much time up front wordsmithing a first draft of a review, managers can spend more time editing or refining their perspectives or observations based on the performance data compiled.”
Matos believes one of the biggest advantages of using ChatGPT is freeing managers up to spend more time talking one-on-one with employees and less time summarizing data or writing reviews.
“If you look at all of the writing time required in a typical performance review, generative AI can significantly reduce that in producing a first draft,” he said. “Even after editing that draft, you still have more time to actually talk with employees about their behaviors or accomplishments, to get their feedback and explore things like creating development plans. It can change performance management from an impersonal exercise to one where you’re having more in-depth and meaningful conversations with employees.”
Matos said ChatGPT also can help managers or employees who only need to write infrequently for tasks like performance reviews and, as a result, may not have developed those skills.
“A manager might be tempted to say someone has an ‘attitude problem,’ for example, but with a good prompt, ChatGPT can come back with language that is more constructive and maybe provides better guidance for the employee being reviewed so he or she isn’t left thinking all they need to do is smile more on the job to address the perceived attitude issue,” Matos said.
Goal Setting and Creating Personalized Development Plans
Managers in some organizations are using ChatGPT to help create performance goals for employees based on defined sets of criteria. Some of these tools also create action plans to accompany the goals.
Betterworks uses AI and machine learning in its platform not just to help managers write higher-quality performance feedback, Dennerline said, but also to assist employees in setting better objectives and key results.
Generative AI also can create personalized learning plans for employees based on the outcome of performance reviews. ChatGPT and other forms of GenAI can suggest specific learning courses or content based on those reviews as well as on an employee’s preferred career path or development plan.
Helping Employees Prepare for Performance Reviews
Another way companies are using ChatGPT today is to help employees prepare for performance reviews by compiling and synthesizing data on their own accomplishments, behaviors or goals achieved.
Brennan Allamano said she’s become aware of this emerging use of ChatGPT through discussions with her HR executive peers and Lattice’s Resources for Humans community on Slack, which has 20,000 HR professionals as members.
“This use allows employees to go from only using bulleted lists or notes of their achievements to using ChatGPT to quickly create more comprehensive and detailed drafts of their accomplishments that they can polish to make a more compelling case for the performance they’re providing to their organization,” she said.
Addressing Risks of Generative AI
Anyone who’s followed the ChatGPT phenomenon knows the technology also comes with limitations and risks that need to be addressed by HR functions and their legal teams. Recruiting software company Textio, for example, recently conducted an experiment with ChatGPT that highlighted the dangers of the tool creating gender-biased performance feedback. Generative AI also will likely face new federal and state legislation designed to ensure it doesn’t foster bias or discrimination when used in hiring or promotion decisions.
Experts say policies and employee training to govern the use of GenAI in performance management should cover a number of key areas.
“Organizations should be aware of generative AI’s challenges and risks, including but not limited to sources being unknown, making the workplace less human and creative, lack of critical thinking, and too much similarity across content where variety and specificity is essential,” Dennerline said.
ChatGPT also has been known to produce inaccurate information or false positives, Dennerline said, and HR functions should address those risks as well as other concerns. “Leaders should coach anyone using ChatGPT to be transparent about its use and not to claim content produced by the tool as their own,” he said. “Any organization would benefit from educating itself on the liability of using generative AI tools and working with their legal teams to determine what needs to be done to protect customers and employees when using them.”
Brennan Allamano said one of the biggest concerns about using ChatGPT is the prospect of its output being biased based on gender, race, age or other protected worker categories. “The words that it generates may not always be aligned with the values of your organization if you’re using the version of the tool trained on the internet’s broad dataset,” she said.
To that end, she said, companies have the responsibility to create guardrails and employee training that ensure human review and accountability for GenAI’s output.
“Here at Lattice, we’ve created a clear policy and communicated that if you use generative AI tools, responsible use includes owning the final work output,” she said. “That means ensuring you verify sources of data and assess whether any of the terms or tone of content ChatGPT generates could be biased. We believe there will always be a need for the human component to review and contextualize the output of generative AI.”
Dave Zielinski is principal of Skiwood Communications, a business writing and editing company in Minneapolis.
DE&I Tech Leaders Mull Affirmative Action Decision
Technology providers that support their customers’ diversity, equity and inclusion (DE&I) efforts are thinking out loud about the best ways they can use their software and strategies to strengthen a diverse workforce in the face of the U.S. Supreme Court’s recent decision to strike down the use of affirmative action when considering college and university admissions.
At Mathison Technologies Inc., a Los Angeles-based company that provides a data platform to measure and benchmark DE&I goals, CEO John Peterson is worried that the national ban on affirmative action at higher education institutions will have the same result as the 1996 passage of California’s Proposition 209, which banned race and gender as factors in admission at the state’s public universities.
Proposition 209 resulted in a reduction of minority admissions at high-ranking public universities in California. As a result of the U.S. Supreme Court’s latest affirmative action decision, that pattern could be repeated across the nation, Peterson believes.
“If we see what played out in California play out in the United States as a whole, we are going to be immediately shrinking the pool of underrepresented minority candidates from top universities,” Peterson said.
[Related article: What Happened When States Banned Affirmative Action?, SHRM Online]
Research conducted after Proposition 209 took effect three decades ago shows the impact of the law on minority admissions at the University of California (UC).
In 2020, the UC’s own data—such as college enrollment, course performance, degree attainment and wages—found that “ending affirmative action caused UC’s 10,000 annual underrepresented minority (URM) freshman applicants to cascade into lower-quality public and private universities. URM applicants’ undergraduate and graduate degree attainment declined overall and in STEM fields, especially among lower-testing applicants.”
The report continues: “The average URM UC applicant’s wages declined by 5 percent annually between ages 24 and 34, almost wholly driven by declines among Hispanic applicants. By the mid-2010s, Prop 209 had caused a cumulative decline in the number of early-career URM Californians earning over $100,000 by at least 3 percent.”
Another academic research report that examined the effects of affirmative action bans in four states—California, Florida, Texas and Washington—showed that the greatest reductions in enrollment in graduate programs occurred in the fields of engineering, natural sciences and social sciences.
Minority students who aren’t accepted to top-tier universities but are admitted to lower-ranked educational institutions, as happened in California, lose out on the career advantages of networking at top-ranked schools, Peterson said.
Research from employment website Indeed supports that point. The study showed that 37 percent of managers who self-identified as coming from a top school said they prefer to hire candidates from top institutions only.
“You really start to see this trickle effect of less and less underrepresented folks making it through, especially to those top and key positions, as well as those positions that do the hiring. It really can snowball the problem,” Peterson said.
John T. Saunders, chief diversity officer at management consulting company Korn Ferry, predicted that in the short term, the Supreme Court’s decision will lower the number of underrepresented minority workers coming out of top-tier universities. Companies should begin looking for qualified candidates at educational institutions that serve large numbers of minority candidates.
Such a move could challenge company executives’ long-held perceptions that “highly selective institutions tend to have students with the most potential. In today’s environment, we are learning that this may not necessarily be true,” Saunders said.
Using Technology to Find, Hire
If there are fewer people from minority groups in the job candidate pipeline, employers will have to think about who their recruiting partners are, how and where they will appeal to the next crop of minority graduates entering the workforce, and when the company does hire these graduates, how they can use DE&I strategies backed by technology to keep them at the company. Organizational and cultural practices are often riddled with human biases, so it is critical that practitioners understand how to leverage technology to ensure they are achieving the best possible outcomes.
“If HR executives are only thinking about measuring success based on the number of slots we fill with Black and Hispanic people, that completely ignores the experience employees may have at the company. If underrepresented employees leave, HR might see it as a recruiting challenge when it actually might be a retention issue,” Saunders said.
Peterson said HR executives should rely on DE&I data to make informed decisions and noted that “in some of the surveys we do, even a one-point increase in an individual’s inclusion and belonging score makes them six times more likely to stay at that company.”
Resolving to Hire Differently
The Supreme Court’s decision comes at an important inflection point in race relations across corporate America.
In the aftermath of George Floyd’s murder by a police officer in Minneapolis in May 2020, companies such as Apple, Estee Lauder, Google, Intel, Johnson & Johnson, Nike and Pfizer committed themselves to hiring more minorities, and businesses created deeper relationships with universities and organizations that serve minority groups.
For example, in 2020, Google announced its commitment to improve leadership representation of underrepresented groups by 30 percent by 2025.
The year 2020 also saw IBM announce the IBM Mentorship Marathon to help Hispanic people advance their career opportunities. The marathon focused on U.S. Pathways in Technology (P-TECH) schools and committed to match 1,000 IBM mentors with students.
In 2021, Amazon Web Services and Howard University announced an initiative to upskill students and build pathways to technical careers with cloud computing courses and training resources for educators.
Laura Close, co-founder and chief business development officer at Included.AI, a Seattle–based company with a platform designed to help businesses connect with a diverse candidate pool, said the use of artificial intelligence has sharpened recruiters’ ability to find qualified minority candidates.
For example, artificial intelligence can be used to examine thousands of data points to widen the scope of the search for minority talent in ZIP codes or universities that have a disproportionately high percentage of racial diversity, Close said.
“The list is very long of how we are able to look through large datasets and understand the probability of reaching candidates that would diversify the pipeline,” Close said.
She added that data can help recruiters market themselves in locations where there is a high population of potential candidates who are Black, Hispanic, Asian or from other underrepresented groups.
“The Supreme Court’s decision does not say you can’t advertise in certain markets, you can’t read your own diversity data or you can’t develop strategies based on that data,” Close said.
Saunders said he believes this latest disruption will create some challenges for organizations but will also afford them the opportunity to think about new strategies that can help them achieve their DE&I commitments.
“The reality is today’s employee—and today’s consumer—still values a more diverse workforce. They recognize the importance of creating more innovation, better products and services, more creativity, and a greater sense of belonging. That’s not going to go away,” Saunders said.
Nicole Lewis is a freelance writer based in Miami.
Generative AI Will Disrupt Career Paths, Recruiting
As generative artificial intelligence tools replace some entry-level positions, this will disrupt the job pipeline that moves junior employees to midcareer roles, ultimately upsetting recruiters’ ability to fill midlevel management positions, predicted Alexandra Samuel, digital-workplace speaker and co-author of Remote, Inc.: How To Thrive at Work . . . Wherever You Are (Harper Business, 2021). “Recruiters are going to face a bottleneck when it comes to midcareer hiring because a lot of companies are going to replace big portions of their junior workforce with generative AI tools,” Samuel told SHRM Online.
In previous digital transformations, Samuel said, flattening the organization has meant a compression of midlevel positions, but the introduction of generative AI is different because “at least as far as your white-collar work is concerned, it’s not really a flattening out of the middle as much as it is a cutting off of the bottom.”
A reduction of junior, entry-level workers means that in the years ahead there won’t be as many lower-level employees to train and promote to midlevel career jobs, and that will also affect the transition from midlevel to senior jobs within a company.
With the expected decline in entry-level jobs, Samuel said, recruiters and other HR stakeholders should support organizations’ efforts to transform their organizational model into one in which entry-level workers are fast-tracked into midlevel positions.
“Instead of going from data entry to inside sales to junior account assistant you now are going on a much steeper curve and getting your ‘entry-level’ talent into midlevel roles within a year or two of joining the organization,” Samuel said. “Most organizations don’t have the expertise yet on how to use AI to accelerate their junior talent, and that’s where there’s a huge opportunity.”
Changing How Work Is Done
Even if generative AI doesn’t entirely replace certain jobs, it will change how the job is performed, forcing employees to go beyond repetitive and mundane tasks as they adopt other job functions, said Hiten Sheth, director, research and advisory at the HR tech and transformation division of Gartner’s HR practice.
Sheth gave the example of an intern who is hired to write research notes, which now are automated with generative AI.
“They will now be more focused on generating more notes, so either the quantity goes up or their role will be more targeted towards driving some strategic goals of the organization itself at that level too,” Sheth said. “The result is many of the key performance indicators will be redefined to further bolster their underlying goals and so those roles may shape up differently.” As recruiters look for opportunities to match job applicants with the right job openings, they should observe how companies intend to radically rethink roles as they adopt newer versions of AI technologies such as ChatGPT, Midjourney, and Dall-E.
Goldman Sachs estimates that shifts in workflow resulting from the use of generative AI could put 300 million full-time jobs at risk of either being completely automated or may result in parts of an employee’s job being outsourced to creative AI tools.
Researchers at global professional services company Accenture note that generative AI can impact more than half of all hours worked in several job categories such as office and administrative support; sales; computer and mathematical roles; business and financial operations; and arts, design, entertainment, sports and media.
Workers Eager for AI Help
For employees, the thought of using generative AI to cut the time it takes to complete tasks at work is appealing.
Recent research from Microsoft found that across the Microsoft 365 apps, “the average employee spends 57 percent of their time communicating (in meetings, email and chat) and 43 percent creating (in documents, spreadsheets and presentations). The heaviest email users (top 25 percent) spend 8.8 hours a week on email, and the heaviest meeting users (top 25 percent) spend 7.5 hours a week in meetings.”
Microsoft’s research was recently published in its report, Will AI Fix Work? The report also states that, “Employees are more eager for AI to lift the weight of work than they are afraid of job loss to AI. While 49 percent of people say they’re worried AI will replace their jobs, even more—70 percent—would delegate as much work as possible to AI to lessen their workloads.”
While addressing their clients’ fluctuating talent needs, recruiting companies are also expanding their own capabilities as they transition from using AI machine learning capabilities to scan millions of resumes to help their clients make hiring decisions toward using generative AI to produce, for example, first drafts of written documents, and automating routine administrative tasks such as scheduling interviews and sending follow-up emails.
SeekOut, a Bellevue, Wash., company that provides a talent intelligence platform, recently introduced SeekOut Assist, a tool that uses ChatGPT to help recruiters parse a job description into search criteria including job title, required skills and preferred skills. The tool then analyzes over 800 million profiles in SeekOut’s talent database to find the best match for a job in minutes instead of hours.
SeekOut Assist also creates personalized messages to candidates that incorporate sentences speaking to a candidate’s unique qualifications for the role.
“We leverage ChatGPT to automatically generate a message, and the length of the message can be controlled, the tone can be controlled and what is emphasized can be controlled,” said Anoop Gupta, CEO of SeekOut.
The technology also helps recruiters quickly learn about job candidates prior to a first phone interview, Gupta said.
Hari Srinivasan, vice president of product at LinkedIn, said the business and employment-focused social media platform is exploring new ways to integrate generative AI throughout their products as they improve every step of the recruiter and job seeker journey. A global study by LinkedIn highlights some of the areas where generative AI can be useful.
“We know the No. 1 goal of hirers is to find the right candidate fast,” Srinivasan said. “We found that 75 percent of hirers hope that generative AI can free up time for more strategic work and that two-thirds (67 percent) hope the technology can help them uncover new candidates.”
Like Gupta, Srinivasan noted that tasks such as writing job descriptions or candidate messages can take a lot of time.
“We’re testing things like AI-powered job descriptions and AI-assisted messages to help hirers streamline parts of the hiring process so they can focus on the most strategic aspects of their job, like speaking to and building relationships with candidates,” Srinivasan said.
As generative AI replaces, recalibrates and redefines work, Sheth said the introduction of bias in the data should be top of mind when recruiters use these tools, especially since the data consists of large language models that are more likely to reflect biases present in society.
“Using such tools puts a responsibility on the recruiters and the hiring teams to ensure that bias and the wrong data are not perpetuated,” Sheth said.
He added that concerns about data privacy are also being discussed among recruiters because using generative AI involves handling and processing large volumes of mostly confidential data.
“It’s crucial for recruiters to have an ethical and responsible use of these tools to safeguard data privacy and comply with their organization’s data policies,” Sheth said.
Nicole Lewis is a freelance journalist based in Miami.
Crafting Policies to Address the Proliferation of Generative AI
A growing number of employers have moved to restrict employee use of ChatGPT, while other companies are embracing generative artificial intelligence and recognizing opportunities to streamline work processes and augment workflows.
There’s certainly value in generative AI tools. But there are risks as well. Whether your organization chooses to ban or embrace tools like ChatGPT, there are new policy considerations to consider to ensure that employees are using these tools in alignment with company concerns and opportunities.
The Need for New Policies
The advent of generative AI isn’t the first time that organizations have had to quickly revise or recreate policies, said Lisa Sterling, chief people officer at Perceptyx, a Temecula, Calif.-based employee listening company that has been closely following GenAI development. “When social media first became prevalent, organizations scrambled to build guidelines and policies regarding employee use to ensure a clear delineation between personal and professional use,” Sterling said. “Now GenAI has introduced some new twists on those guidelines, and most people are still working to define the new parameters.”
And, as with any new technology, generative AI offers both benefits and some potential challenges and risks for organizations.
“Generative artificial intelligence involves the use of algorithms to create new content and interfaces from existing data,” explained attorney Paul E. Starkman with Clark Hill in Chicago. “When used in the workplace, if vetted and implemented properly, AI can have positive effects, such as improving productivity, streamlining operations and developing content that is remarkably human-like.” However, he added, “there are legal, business and reputational risks and other implications involved in the utilization and creation of a policy addressing generative AI.”
The data used by AI and the output of generative AI systems “must often be constantly or at least periodically monitored, reviewed and audited,” Starkman advised. “Therefore, many organizations have instituted workplace policies on when and how generative AI systems may be used, with some organizations banning the use of certain AI systems altogether.”
Policy Considerations
Organizations must decide whether to allow employees to use generative AI. And, if so, to what extent and for what purposes?
It’s also important, Sterling said, to provide a clear definition of generative AI. “This is a very new technology for many, so policies need to be precise on what it is and isn’t.” In addition, she said, “how, when and why it should be used should be clear for employees.” It can also be helpful to “provide use cases where GenAI can and should be leveraged.”
When considering the use of generative AI—both currently and in the future—Sterling said, “organizations need to be clear about what is acceptable and what is not.” This will involve a number of organizational functions. “When organizations are ready to craft their guidelines/policy, they will need the collective input and support of their legal, HR, operations, technology, compliance, data privacy and security teams,” Sterling said. “Each is critical in protecting the organization and its employees from legal, commercial and ethical risks.”
John Bremen, a managing director at WTW in the Chicago area, has been having a number of conversations with clients about generative AI and its implications. Generative AI policies, Bremen said, are going to evolve but, for now, there are three key areas where most policies are focusing: avoiding any risk to protected information, ensuring that users don’t inadvertently violate copyrights, and ensuring that the use of the information is honest and accurate.
Data privacy is a key consideration for any organization. Generative AI is continually “learning” through the information that it gathers—information that is provided by users. Depending on how individual apps are using, storing or sharing the data received, your organization’s data may become part of the public domain and accessible by AI and, consequently, users far and wide.
As the New York City-based business membership and research organization The Conference Board has warned: “Uploading proprietary data into the AI apps for processing could pose organizational risks that include forfeiting ownership of information, intellectual property, patents, and copyrights.”
“One thing for employers to watch out for as they utilize GenAI prompts is data privacy—both company level and employee level,” agreed Emily Kilham, director of research and insights with Perceptyx. “Having a clear policy in place about trade secrets, personal information and GenAI will be important so that no one inadvertently causes a data privacy issue. As we see countries and areas of countries develop laws around the use of GenAI, those policies will need to be adjusted.”
It’s also important for organizations to ensure that their employees’ use of generative AI tools doesn’t infringe on others’ copyrighted or protected information or intellectual property.
Another worry: the potential for misinformation and manipulation in the form of hallucinations. As The Conference Board explains: “Based on inaccurate information and dependent on the sources used to train it, AI sometimes ‘hallucinates,’ meaning it confidently generates inaccurate information without flagging that shift for the user of the app.”
When relying on AI-generated outputs, it’s important to be transparent about using it—including being open and honest about the limitations, potential biases and uncertainties that may exist.
Additional Considerations
Starkman shared some key questions for teams tasked with creating an AI workplace policy:
- What is the focus of the policy? Will it address all AI or just generative AI? Will the policy be part of a computer and technology use policy?
- Are all relevant stakeholders’ interests incorporated in the policy?
- How is AI currently used in the organization, and how might it be used in the future?
- How will the policy be rolled out, and how will the policy become a living document that remains relevant despite changes in technology and usage?
Legal counsel is critical when developing and implementing generative AI policies. As Starkman noted: “Among the legal risks that can arise from the use of generative AI to perform human resources functions and participate in employment decisions is the potential that the AI algorithms may reinforce biased or discriminatory employment practices against legally protected groups, such as older workers, persons with disabilities, women and others.”
There are other troubling considerations, Starkman said. “Using AI to monitor employee activities can raise privacy issues, given that some AI tools purport to measure employee engagement and emotional states.” There are concerns among both employees and regulators, he said, “that using AI technology for increased monitoring can reveal protected disabilities and other confidential personal information. As a result, companies may want to address these concerns in their messaging, training and content of their AI policies.”
Training, agreed Sterling, is a critical part of policy implementation. Policies are important but must be augmented by training, she stressed. “It all starts with training. A policy is only effective if people are educated.”
As with other policies like data privacy and sexual harassment, for instance, Sterling said companies should have annual education and certification processes to ensure employees know what’s expected of them. “Additionally,” she noted, “organizations can implement technology that monitors the usage and can detect inconsistencies and potential risks.”
Lin Grensing-Pophal is a freelance writer in Chippewa Falls, Wis.
