Tag: HR Tech

Skills Architecture is the key to SBO – and AI is the tech enabler

Posted on by Isabelle Bichler

Talent acquisition specialists are continuously challenged to match candidates with the right skills to positions that feed evolving business needs. Technology and data are the one-two punch that empowers HRs to do so effectively.

Skills Architecture: Building the SBO Foundation

A comprehensive skills architecture helps HR leaders start the process with a clear understanding of what skills are already within their talent pool and where the skills gaps lie so they can target the right candidates more quickly. 

As such, skills architecture acts as the blueprint that defines and structures the skills within an organization, unifying skills language and moving beyond traditional job descriptions to establish an agreed-upon nomenclature. It’s the foundation on which a centralized Skills-Based Organization is built, pulling together skills data across teams to form a larger, more strategically malleable view.

“We’ve always historically said that this system doesn’t talk to this system, and this system doesn’t talk to this system, and the spaghetti plate of systems is what disables us from getting data or from making informed decisions that are embedded and rooted in true data and analytics,” says Sadia Ayaz, VP of Talent Management for Veolia. “Here we are today, capable of doing that. AI can take all of that unstructured data and make sense of it.” 

AI: The SBO Tech Enabler

AI’s machine learning algorithms and expedited data analytics are the technological backbone of a sophisticated skills architecture. This is due to its ability to: 

  • Ingest vast datasets to identify both existing and emerging skills within an organization 
  • Detect patterns and trends, allowing HRs to stay ahead of critical skill gaps or areas that require upskilling
  • Reveal hidden talent within an existing workforce, uncovering internal job candidates for consideration

Beyond talent acquisition, AI-driven technologies also fortify talent management strategies in a Skills Based Organization. Through personalized learning paths and recommendations, AI enables employees to acquire new skills in alignment with organizational goals, while also benefiting from individualized professional development. 

By harnessing the power of AI to construct a detailed and dynamic skills architecture, and engaging AI as the tech enabler for a successful Skills-Based Organization, HRs can hire and retain the right talent to fuel an SBO workforce model. Synergy between skills architecture and AI is the cornerstone of shaping the future of talent acquisition and organizational success.

Learn more about the power of retrain.ai’s Talent Intelligence platform here.

Mastering Hiring Challenges: How AI Levels the Field for Companies of All Sizes

Posted on by Isabelle Bichler

Large enterprises and smaller organizations have similar challenges when it comes to hiring: Finding the right people for best-fit positions quickly, accurately and efficiently.

This at a time when 53% of in-house recruiting pros predict their recruiting budget will decrease or stay flat this year.

Fortunately, the emergence of AI-driven talent intelligence platforms has leveled the playing field, providing companies of every size with powerful tools to attract, hire, and retain top talent with the skills needed for success. 

The AI Advantage

A December 2023 report from Gartner names technology as the No. 1 investment area for HR leaders–for the third year in a row. The reason? HR increasingly relies on technology to meet a growing list of business demands, including fulfilling employee experience needs, enabling talent agility and continuing HR tech transformation.2

AI-driven talent intelligence platforms act as force multipliers, by automating time-consuming tasks, enabling the agility needed for companies to achieve more with fewer talent resources. 

Data insights generated by AI can also empower companies to target their efforts more precisely, ensuring that every investment in talent pays off in the quest for sustainable growth and competitiveness in the marketplace.

AI-driven platforms provide an advantage by: 

  • Analyzing vast amounts of data at lightning speed, providing actionable insights to help HRs make informed hiring decisions
  • Streamlining the sourcing and screening process, reducing time-to-hire and ensuring HRs can compete effectively for top talent
  • Personalizing internal mobility opportunities, using skills data to predict potential career pathing options for employees.

AI-driven automation frees up HR professionals to focus on larger strategic initiatives; and just as importantly, to spend more time on the face-to-face, human-centric aspects of the job. 

retrain.ai is a Talent Intelligence Platform designed to help enterprises hire, retain, and develop their workforce, intelligently. Leveraging Responsible AI and the industry’s largest skills taxonomy, enterprises unlock talent insights and optimize their workforce effectively to hire the right people, keep them longer and cultivate a successful skills-based organization. retrain.ai fuels Talent Acquisition, Talent Management and Skills Architecture all in one, data-driven solution. To see it in action, request a demo.

Gartner report: Mitigate Bias From AI in Technology

Posted on by Isabelle Bichler

The following is a summary of “Mitigate Bias From AI in Technology,” a report from Gartner. The full report is available for download here (For Gartner Subscribers). 

Summary

Organizations are rapidly adopting AI in HR, while regulations are struggling to keep up. As part of their HR strategy, HR leaders must promote responsible AI in their applications by mitigating bias that poses risks to talent management, the employee experience, DEI and more.

Key Findings from the Gartner report

  • Fifty-three percent of HR leaders are concerned about potential bias and discrimination from AI. Bias in AI is unavoidable; however, HR leaders can establish best practices that mitigate this bias.
  • Thoroughly vetting HR technologies for bias requires an understanding of business processes. Broad overarching assessments can lead to missed sources of bias, or to inaction due to fear of getting it wrong. The organization should assess each use case individually.
  • AI regulations, including HR-specific measures relevant to bias, are gradually taking effect. Since many HR functions plan to buy AI capabilities built by vendors, HR leaders face the need to monitor technology providers for their regulatory compliance and ethical considerations.
  • HR leaders are positioned to take a leading role in advancing practices that bolster openness about the potential impacts of bias from AI applications, and 35% of HR leaders recently reported they expect to lead their organization’s enterprise wide AI ethics approach.

Gartner Recommendations

HR leaders responsible for technology strategy must:

  • Map possible sources and outputs of bias for each AI use case in HR to assist in flagging areas of risk and monitoring vendors for their commitment to responsible AI practices.
  • Require and evaluate bias mitigation from HR technology providers offering AI functionality by assessing criteria related to their data, algorithms, organizational context, regulation compliance and ethical considerations.
  • Promote transparency into the potential impacts of AI’s bias by collaborating with external and internal stakeholders to take decisive steps in protecting the organization, the future of work and society at large.

Gartner, Mitigate Bias From AI in HR Technology, By Helen Poitevin, 16 October 2023

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Webinar: retrain.ai and Citi Global Insights discuss how AI empowers HRs (Part 2 of 2)

Posted on by Isabelle Bichler

retrain.ai Co-founder and COO Isabelle Bichler-Eliasaf was recently invited to speak with Rob Garlick and Wenyan Fei from Citi Global Insights for a webinar entitled “Are Talent Intelligent Platforms the Solution to Reskilling and Upskilling?” 

Below is the second of a two-part blog series highlighting excerpts from their conversation. You can also view a recording of the full session here.

Isabelle Bichler Eliasaf, Rob Garlick, Wenyan Fei

You mentioned that retrain.ai represents fifth-generation AI. Can you share with us some unique features enabled by this latest generation of AI and what differentiates your platform versus other providers?

Yes, for sure. It’s really honing in on the skills taxonomies we’re creating; retrain.ai built one of the biggest data assets that exists today. We’re bringing a holistic solution from hire to retire, but the first thing you need to do is start with the skills architecture model. It uses the robustness of skills taxonomies to see details and skills, quickly analyzing all the roles of an organization. We’re providing a complete, dynamic catalog to our customer so they can have all their roles analyzed by skills–technical skills, soft skills, hard skills, and so forth. They have an understanding of the skills that are in demand, emerging skills, those that are declining, etc. This is the first step; really building the foundation in order to add on the next floor which is going to be talent acquisition or talent management and so forth. 

What we found is that a lot of organizations don’t do all that; the problem is that they operate in silos, without a unified skills language. They don’t share the same philosophy and the same strategy across teams. But cross-functional strategy should be shared because they’re going to use the same foundation of skills, the same framework. So what we’re doing at this stage is consolidating and synchronizing all teams in the same language. We’re taking the LMS skills framework and adapting it to the HCM and all the others used in an organization. That’s the first step, and now with that accurate, granular, robust skills data, I can inform talent acquisition; I can quickly create a job description based on the skills that I’ve analyzed for that role. Then I’m able to analyze the skills of your people; I can generate a heat map to understand what skills and organizational capabilities you have, and the skills gaps that represent weaknesses in specific departments. The skills data is also going to serve for learning and development to understand what the top skills are, and what the emerging skills are to fill skills gaps.

You say you’re a technology company built on data. Can you walk us through the kind of data that you ingest and how you train your model? It must be a huge amount to get to the point where you need to be in order to add value to people. Does this just get better and better as you are able to train it?

Exactly. It’s learning constantly. And the more data you collect, the more accurate you get so you can generate those predictions. What we’ve done throughout our first two years of inception is build a database by collecting from job boards, Indeed, all those different job boards, job descriptions, and so forth. Just on LinkedIn, you have 400 million active users that you can learn from; what career pathways they’ve pursued, and what skills they have for their position. We ingest all of this, plus pre-built taxonomies from Europe and Canada, and we continue to increase with market data. You can add even more from sites like Udemy; what are the learning trends, and what topics are in demand? All of this together feeds into one data asset. That is really integrated intelligence because we integrate with HCMs and older systems of record, not replacing them but just making them smarter with data. 

People are fearful about data privacy, algorithmic bias, bias in hiring, etc. How do we tackle this as a society? Is AI part of the solution or part of the problem? How are you guys doing that at retrain.ai in terms of responsible AI?

This is why I’m very passionate about responsible AI; because this is a problem and solution. AI is amazing. It’s like saying you’re not going to use electricity because there are risks. Of course you will, but you need to take some precautions. For us at retrain.ai, GDPR is what we’re embedding so that we’re compliant with the most stringent requirements. We’re also making sure all data we have is used with the consent of candidates; we know this because they chose to put their data online with all the information and informative disclosures involved. 

There’s also new legislation around telling you that you’re going to be assessed by a machine and allowing you to opt out: Local Law 144 here in New York City. I actually submitted public comments on the law along with others and one thing they hadn’t figured out is, if I’m a candidate opting out, am I able to be fully assessed? Am I actually discriminated against because I’m choosing that AI not be used? We had an amazing conference with Commissioner Keith Sonderling of the Equal Employment Opportunity and we were talking about that; his take on it is that the law didn’t change anything that wasn’t already in place. It didn’t do anything new. It’s still saying you shouldn’t discriminate based on gender, ethnicity, age, and all those different parameters–there’s another entity performing that assessment. I agree with him, but I think that the fear of AI makes us more aware and want to limit it. So now we need to balance between innovation and regulation.

 

Webinar: retrain.ai and Citi Global Insights discuss how AI empowers HRs (Part 1 of 2)

Posted on by Isabelle Bichler

retrain.ai Co-founder and COO Isabelle Bichler-Eliasaf was recently invited to speak with Rob Garlick and Wenyan Fei from Citi Global Insights for a webinar entitled “Are Talent Intelligent Platforms the Solution to Reskilling and Upskilling?” 

Below is the first of a two-part blog series highlighting excerpts from their conversation. You can also view a recording of the full session here.

Isabelle Bichler-Eliasaf, Rob Garlick, Wenyan Fei

What is a Talent Intelligence Platform? What is the technology that’s been transformed by AI to make upskilling possible?

Talent intelligence is really one of the biggest successes when it comes to AI for HR use cases. Talent Intelligence Platforms bring together all the relevant internal and external data sources into one useful tool to create a holistic view of a company that empowers decisions about talent or workforce planning. It’s really a merger between people analytics, market insights, market trends and workforce planning. 

If you’re looking at the system of records–HCM or ATS–AI makes them smarter. Why? Because it answers the most critical questions of recruiters: Who should they hire? What are the criteria based on skills, but not just basic skills? What is the experience required? This informs the recruiter as to who they’re looking for. With AI, they can take an omnichannel approach to sourcing candidates from two different pipelines: External sources, plus internal talent that may warrant a fresh look based on skills that are in demand now or that are emerging. HRs are able to hire much more efficiently by broadening the talent pipeline and looking at potential, not just unrelated qualifications and job titles. This is how talent intelligence is informing and empowering those HCM and ATS HR flows.

It’s my understanding that this is the same transformation technology that lies behind the large language models. If you don’t mind, maybe just contrast today’s tech with some of the legacy HR systems that create challenges for companies in terms of being able to extract data. 

What really has enabled us to bring increased efficiencies for retention and faster hiring and so forth are those models, of course, plus there’s been exponential growth–a leapfrog really–in AI This is because there’s much more data to train our models upon. In just the last two years, 90% of the current data was generated; that’s how big the computing power is now. It’s also been cheaper so we’re able to train better and more efficiently.

At the most recent HR tech conference in Las Vegas, analyst Josh Bersin recognized our work and categorized companies in AI into three types of companies. There are companies that are now adding AI into their flows, like adding a chatbot. Then there are companies such as SAP Oracle that have a lot of transactional data about candidates and employees and are now running some models to predict what would be a good fit, or what would be the next course an employee should take. Then there are the new types of companies such as ours that are built from the start on AI. We are the companies using neural networks. retrain.ai is using 1.8 billion data points and counting from all different sources to support HR flows; so it’s not just going to give an understanding of what you have in your company but also the ability to benchmark that against the market.

Webinar Recording: Unlocking the Power of AI in HR

Posted on by Isabelle Bichler

A recent webinar sponsored by Crain’s New York Business and NYU School of Professional Studies explored the advancements in AI in HR, including a discussion on why it’s critical for organizations to venture into this technological realm responsibly.

Moderated by Dr. Anna Tavis, Clinical Professor & Chair of the Human Capital Management Department of NYU’s School of Professional Studies, the conversation featured retrain.ai Co-founder and COO Isabelle Bichler-Eliasaf along with with Sadia Ayaz, VP of Talent Management for Veolia, and Heidi Ramirez Perloff, SVP of Global HR Strategic Initiatives and Delivery Solutions for the Estée Lauder Companies. The full recording is below.

 

 

 

How Generative AI Transforms Talent Intelligence

Posted on by Isabelle Bichler

For businesses competing in today’s fast-paced landscape, leading the market means having the right people with the right skills in the right roles. Generative AI has risen to the top of this conversation as a way to more efficiently and accurately reach that goal. 

But what is Generative AI, and how can it revolutionize talent intelligence? If you’re a CHRO, technical innovation leader, or workforce strategist looking to harness the power of AI for HR, here’s what you need to know.

What is Generative AI?

Generative AI is a subset of artificial intelligence that focuses on creating new, original content, rather than simply analyzing or processing existing data. Using vast datasets to learn patterns, relationships, and styles, generative AI technology is able to generate human-like content, whether it’s text, images, or even music.

Generative AI in Hiring

Finding the right talent has always been a critical challenge for HR professionals. When it comes to sourcing, screening and securing the right candidates with the skills needed for business success, generative AI can assist in multiple ways:

  • Skills Extraction and Matching: By analyzing resumes and job descriptions with unprecedented speed and accuracy, Generative AI solutions can quickly identify candidates who best match the required skills and qualifications for an open role. This means talent acquisition specialists can cut time and cost to hire by zeroing in on the right people to move through the hiring process.
  • Candidate Engagement: Chatbots powered by Generative AI can interact with candidates, answer questions, and schedule interviews, ensuring a seamless and efficient recruitment process while freeing HR leaders up to focus on more interpersonal elements of the hiring process.
  • Diversity and Inclusion: Generative AI can support a fair and inclusive hiring process by focusing solely on qualifications and skills, eliminating demographic and other information that can introduce unintended bias.

Generative AI and Internal Mobility

Fostering internal mobility is essential for employee growth and retention. In fact, it’s been shown that employees with a clear view toward future opportunities are more likely to stay with an organization, making proactive upskilling channels a business imperative. Generative AI can assist by:

  • Skills Mapping: Generative AI can help identify employees’ skills, capabilities and interests in order to more accurately suggest potential career paths within the organization.
  • Learning Recommendations: When opportunities are identified in the form of open roles, gigs or projects, Generative AI can produce relevant upskilling pathways to help them acquire the new skills required to pursue them.
  • Succession Planning: By analyzing employee data around skills, capabilities and aspirations, Generative AI can aid in identifying high-potential employees and preparing them for leadership positions.

Generative AI and Skills-Based Organizations

The traditional job-centric approach to workforce planning is quickly becoming outdated, as more companies shift to a skills-focused model. Generative AI supports this transition through:

  • Skills Gap Analysis: By powering a data-driven skills architecture, Generative AI can analyze the skills already present in a workforce, those needed in the future, and how to bridge the gap between the two with employee upskilling or reskilling recommendations.
  • Skills Tagging: Generative AI can automatically tag skills to employees based on their experiences and achievements, creating a comprehensive, continuously updated skills inventory.
  • Adaptive Training: Personalized training plans can be fueled by Generative AI, ensuring employees are continually developing the skills necessary to excel in their current and future roles.

Considerations for CHROs

While Generative AI holds immense potential to elevate HR functions, there are some critical considerations for CHROs when evaluating AI platforms for their HR technology stack:

  • Data Privacy: Ensure that data privacy regulations are followed rigorously, including the safeguarding of sensitive employee and candidate information.
  • Ethical Use: Develop Responsible AI guidelines to comply with regulations and prevent bias, discrimination, or misuse of technology in hiring decisions.
  • Integration: Ensure the AI system can seamlessly integrate with your existing HR software to maximize its efficiency and avoid the delays of a rip-and-replace solution.
  • Training and Support: Invest in staff training to help HR teams understand and effectively use Generative AI tools as well as welcome user feedback.
  • Continuous Improvement: Keep in mind that AI is not a static, set-it-and-forget-it solution. Technology solutions require regular updates and fine-tuning to remain effective.

By automating processes, reducing biases, and focusing on skills, Generative AI enables HR professionals to make more informed decisions and create a workforce ready for the challenges of the future. As a CHRO or HR leader, embracing Generative AI can lead to a more agile, efficient, and innovative HR function, giving your organization a competitive edge in the talent market.

 

retrain.ai is a Talent Intelligence Platform designed to help enterprises hire, retain, and develop their workforce, intelligently. Leveraging Responsible AI and the industry’s largest skills taxonomy, enterprises unlock talent insights and optimize their workforce effectively to hire the right people, keep them longer and cultivate a successful skills-based organization. retrain.ai fuels Talent Acquisition, Talent Management and Skills Architecture all in one, data-driven solution. To see it in action, request a demo.

HR Tech 2023 Highlight: Generative AI Isn’t a Trend, It’s a Problem Solving Tool

Posted on by retrain.ai

At HR Tech 2023, retrain.ai Co-founder and COO Isabelle Bichler-Eliasaf sat down with Dan Riley, co-founder of RADICL, to talk about the surge in Generative AI solutions in the HR space and the importance of its ethical, responsible use. Below are excerpts from their conversation; the full recording can be viewed here.

DR: You brought up responsible and ethical AI. How are we doing? Not just retrain.ai, but the industry in general. Are we getting there?

IBE: This year, everybody is talking about AI. Specifically, everybody’s talking about generative AI, and ChatGPT was a great demonstration to show the amazing abilities of AI. But it also showed the pitfalls. It showed it to be erroneous, biased and very generic, not stable enough. So Responsible AI is all about that; putting safeguards on the AI. It’s just a tool, right? So you need to use it wisely with the right safeguards. 

Responsible AI principles span from explainability and bias reduction to consent and embedded privacy rights and so forth, and that’s what we’ve been doing at retrain.ai from the get-go. This is something that’s very important to me, it’s something that I’ve done as part of my research as well around the risks of AI. So now I’m happy to see that a lot of people are thinking about it and starting to do something about it. 

Too often, we either blindly trust AI or we blindly distrust it. But it can’t be a binary conversation. So how do we find that middle ground? How do we challenge it and use it for good? What are some of the things retrain.ai is doing to make sure that happens?

It’s about design, development and deployment. It’s about the safety that we put into the technology, first of all to understand the data that we need to be distributed. And, you have to have representation for different protected classes, for example, to prevent biases. You also have to constantly measure the output and understand if it’s having an adverse impact on certain protected classes – gender, age, ethnicity, and so forth. Those safety guards must be in place all the time.

There’s also a lot of regulation emerging now to enforce that. Local law 144 in New York City is actually mandating that companies show and prove that their output isn’t biased. Beyond biases and discrimination, it’s also about explainability. With our product, we explain why a person is a good fit for a position based on their skills. It’s not a black box; the tool has to be transparent and explainable. 

So we’re here at HR Tech, where for the most part if you talk to any vendor, they’re going to talk about what they’re doing with AI. What’s your advice for the industry in general?

I think you first need to really understand the problem that you’re solving. AI is a tool; so it’s not just about saying hey I want to bring in AI, I want to bring efficiency. What is the pain point? What is the problem you’re solving? What’s the use case? And then, do you have the right tool for it? Once you have that, you’re okay. But just adopting AI across the board because it’s something trendy or because the notion of efficiency is there, or productivity, it’s not enough. You need to know the problem you’re solving. And depending on the use case, you need to have technology with deep AI; not just augmentation and chatbots. It’s really the data that’s used, and the algorithms, and the generation of AI. You need to use really advanced models based on LLMs, with safety guards, in place to give you the results you want. 

 

retrain.ai is a Talent Intelligence Platform designed to help enterprises hire, retain, and develop their workforce, intelligently. Leveraging Responsible AI and the industry’s largest skills taxonomy, enterprises unlock talent insights and optimize their workforce effectively to hire the right people, keep them longer and cultivate a successful skills-based organization. retrain.ai fuels Talent Acquisition, Talent Management and Skills Architecture all in one, data-driven solution. To see it in action, request a demo. 

AI’s Role in Revolutionizing Talent Acquisition and Retention

Posted on by Dr. Shay David

This article originally appeared in Forbes.

As corporations grapple with the challenging task of attracting and retaining highly skilled talent in an intensely competitive market, AI-assisted HR tools are creating a new paradigm. Of course, these innovations can create potential ethical issues during the recruitment and internal mobility processes. Here are things that HR leaders must consider when weighing the pros and cons of implementing these technologies.

AI In Talent Acquisition

Traditional recruitment methods, often laborious and time-consuming, require HR leaders to sift through hundreds of résumés for every open position. These processes can potentially cost thousands of dollars if a position remains unfilled, even reaching six figures when considering senior or technical roles. Furthermore, hastily rushing the recruitment process can lead to improper fitting, resulting in higher turnover rates. With AI-powered platforms, HR leaders can streamline their processes by ensuring a more accurate selection of candidates and accelerating the hiring timeline.

Algorithms can process vast amounts of data swiftly, eliminating the painstaking manual review of résumés. By leveraging natural language processing and machine learning, AI-powered tools analyze and use skills extraction to identify the most relevant skills for a given role. These systems go beyond simple keyword matching; they can apply context to infer skills that aren’t explicitly mentioned in résumés. Semantic skills extraction reduces missed opportunities that occur using only keyword search, creating a selection process that’s more comprehensive for recruiters and more fair to candidates.

At a time when enterprises are rapidly transitioning to skills-based models, introducing an AI-powered platform can help HR leaders quickly assess and rank internal and external candidates based on their skills and capabilities. This not only saves time by revealing best-fit candidates faster but also goes even further by enabling role matching.

Finally, recruitment professionals can use AI to enhance the candidate experience with personalized interactions. Tools like chatbots and virtual assistants provide real-time updates on application status and offer tailored job recommendations, reducing candidate effort and time.

AI In Employee Retention

High employee turnover can significantly impact a company’s bottom line. A survey showed that 63% of employees changing jobs cited lack of advancement opportunities as a main factor. In this context, AI can help HR leaders understand their employees’ needs and aspirations better, then use that knowledge to enhance their journey within the organization.

With AI platforms, talent management teams can analyze large volumes of data to gain insights into factors contributing to employee attrition, such as job satisfaction, work-life balance and career growth opportunities. This personalized insight, regardless of workforce size, allows HR professionals to identify and address at-risk employees’ concerns proactively. For example, an employee who’s remained in one position for a long time may have unrealized potential to succeed on another team in the company. A proactive HR leader will capitalize on AI-driven insights to spot that opportunity and present it to the employee, offering a new challenge and possibly keeping them from looking elsewhere.

Performance management and feedback systems receive support from AI technologies that provide objective evaluations of employee performance. This can help HR leaders and people managers provide personalized coaching and development plans, enhancing overall job satisfaction for employees.

Ethical Concerns And Potential Biases

While AI technology offers numerous advantages, it does raise ethical concerns that HR leaders should stay aware of. These systems can unintentionally perpetuate biases and stereotypes present in historical data. In 2018, Amazon came under fire when it was revealed that an AI-based recruitment system discriminated against women when hiring for technical roles. The platform sought top candidates by positions on their résumés, and considering women had held only about 24% of STEM jobs in the U.S., the majority of résumés fed into the system were from men. As a result, the algorithm developed male preference and gradually deprioritized résumés from women.

Clearly, unintended bias like this can have devastating consequences for an enterprise on several fronts beyond skewed workforce growth. The ripples can be felt throughout brand reputation, customer backlash, candidate trust and more.

Such dangers have prompted an increase in regulation around responsible AI, including Local Law 144 in New York City. The new law requires independent audits of what it categorizes as automated employment decision tools (AEDT) used in hiring within, or from within, New York City—an expansive reach, given the city is a global business hub. While it can be argued that AI-driven platforms don’t automate decisions but rather inform humans’ decision-making, the systems present within an organization’s HR tech stack are included in the regulations.

To remain compliant, HR leaders must ensure diverse and representative training data for any AI systems they implement. Additionally, systems should comply with the five pillars of responsible AI: explainability and interpretability; bias mitigation and fairness algorithms; data robustness and granularity; data quality and rights; and accountability through regular audits and monitoring of the AI’s decision-making process.

HR innovators looking to employ responsible AI-based systems will benefit from first researching available platforms and asking potential vendors the important questions: Is your solution transparent? Can you easily explain how its algorithms work? What bias mitigation is in place? What client onboarding experience can we expect and what training is included?

AI Is Here To Stay

Artificial intelligence is undeniably transforming the world of HR, especially in talent acquisition and retention. The benefits of AI, like streamlined recruitment processes and improved employee engagement and satisfaction, are significant for organizations. By employing ethical, responsible AI-driven systems, enterprises can future-proof their workforce and reap immense benefits.

Update: NYC AI Audit Law (Local Law #144)

Posted on by Isabelle Bichler

As of July 2023, Local Law #144 in New York City is in full effect for enterprises using Automated Employment Decision Tools (AEDT) to assist in hiring decisions. The complete rollout of the new regulation includes punitive enforcement for those found in disagreement with the law.

Given the prospect of a civil penalty for non-compliance, HR leaders are well-advised to keep up to speed on specific requirements of the law, starting with AEDT audits. The law mandates that:

  • AEDT system audits be conducted by an independent entity
  • Summarized audit results be posted on the employer’s website
  • Alternative selection processes or accommodations be provided to job candidates who opt out of AEDT-assisted selection

Despite being known as a NYC-based law, Local Law 144 has generated questions about its actual domain. The geographical guidelines now clarify: 

  • If the role is located in New York City: A bias audit and notice must be sent to NYC residents
  • If the role is located outside of New York City: A bias audit and notice are not required
  • If the role is fully remote but the company has only a New York City office: A bias audit and notice must be sent to NYC residents
  • If the hiring company does not have an office in New York City: A bias audit and notice are not required
  • If the hiring company has offices both in and outside of New York City: Specifics of the role will dictate the need for a bias audit and notice

To break the law down further, let’s start with a few, tangible moves HR leaders and Talent Acquisition specialists can make now to plan for Local Law 144 compliance:

  1. Conduct internal assessments to find out what tools are currently being used that may qualify as an AEDT
  2. Build an inventory and tracking plan for identified AEDTs, including where and how they’re used
  3. Monitor and track AEDT performance, including data input and outcomes, to test accuracy
  4. Develop policies and procedures for storing demographic and selection data needed for AEDT audits
  5. Create a compliance team of HR leaders, legal advisors and tech representatives to prepare or adapt compliance disclosures 

As stewards of Responsible AI, we at retrain.ai are keenly aware that the issues raised by Local Law #144 directly impact the talent intelligence space. Automated, accelerated, bias-free hiring processes powered by innovative technology act as the key to growing and supporting a diverse and inclusive workforce; Responsible AI is the key to reaching DEI goals and remaining compliant.

If your organization is considering integrating an AI-driven solution into your HR tech stack, be sure to check out our Buyer’s Guide to Talent Intelligence to learn more about what to look for, what questions to ask, and how to engage your teams in the process. 

 

retrain.ai is a Talent Intelligence Platform designed to help enterprises hire, retain, and develop their workforce, intelligently. Leveraging Responsible AI and the industry’s largest skills taxonomy, enterprises unlock talent insights and optimize their workforce effectively to hire the right people, keep them longer and cultivate a successful skills-based organization. retrain.ai fuels Talent Acquisition, Talent Management and Skills Architecture all in one, data-driven solution. To see it in action, request a demo.