Category: Blogs

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.

Navigating The Promise And Peril Of Generative AI In HR

Posted on by Dr. Shay David

This article originally appeared in Forbes.

Language has long been the bedrock of our human world; it’s the collective operating system that powers the way we think, feel, interact and make sense of our surroundings. But with the rapid advancements in artificial intelligence (AI), language has also become a crucial interface bridging the gap between humans and machines. Particularly in the HR sector, this evolution comes with both significant opportunities and challenges.

Generative AI, designed to create content that mirrors humanlike patterns of speech and writing, is already beginning to transform HR operations. Leveraged responsibly, it has the power to augment the employee and candidate experience significantly, specifically enabling organizations to identify, attract and retain the best talent effectively while also supporting diverse workforce growth. Conversely, misuse or misunderstanding of these tools can lead to significant pitfalls, from spreading misinformation to challenging trust, authenticity and identity altogether.

The Promise Of Generative AI In HR

Firstly, let’s consider the potential benefits. Generative AI offers unprecedented efficiency and accuracy and can enable the automation of routine HR tasks like screening résumés, answering frequently asked questions and scheduling interviews. This automation not only saves HR professionals’ valuable time but also minimizes the risk of human error, enhancing the fairness and accuracy of these processes.

Organizations are increasingly taking advantage of generative AI for these specific action items in the pre-employment phase. In a recent Littler study, among respondents whose organizations said they are deploying AI and data analytics in workforce management, nearly 70% reported using AI and analytics tools in the recruiting and hiring process.

Secondly, generative AI is a potent tool for improved decision-making. By analyzing patterns and predicting trends, it can generate actionable insights to empower more informed HR decisions. For instance, a generative AI solution could help identify which candidates are most likely to excel in specific roles or flag employees who might be on the verge of seeking new opportunities.

Lastly, generative AI can personalize the HR experience. By understanding individual preferences and needs, it can tailor communications and recommendations, offering a more customized, engaging experience for employees and candidates alike.

The Perils Of Generative AI In HR

However, the advent of generative AI in HR is not without its hazards, most notably the significant risk of misinformation. So concerning is this risk that according to Gartner, by 2027, 80% of enterprise marketers will establish a dedicated content authenticity function to combat misinformation and fake material.

In HR, for example, this could mean an AI system inadvertently disseminates incorrect or outdated information about a company’s policies or job roles, leading to a ripple effect of confusion and potentially serious legal complications.

In addition, bias remains a thorny issue. AI models learn from existing data, which may unintentionally reflect historical biases. Without careful management, these AI systems have the potential to perpetuate these biases, leading to skewed hiring or promotional decisions.

Moreover, privacy and trust are critical concerns. The use of AI in HR often involves collecting and analyzing personal data, which raises privacy questions. As has been emphasized by increasing AI regulations, organizations need to be transparent about their AI usage and take robust measures to protect employee and candidate data.

Lastly, the issue of authenticity and identity cannot be ignored. The line between human and machine interactions becomes blurry with AI. If a candidate interacts with a generative AI system during the recruitment process, they may question whether their responses are genuinely understood or valued. Again, the onus is on the organization to quell these concerns as part of transparent candidate communications.

Navigating The AI Landscape In HR

As we traverse this new landscape, it’s essential to use generative AI tools in HR responsibly. Transparency, fairness and privacy should be the cornerstones of any AI implementation strategy. It’s also crucial to recognize that AI does not make for a “set it and forget it” scenario. Organizations must continually monitor and adjust AI systems to prevent the potential spread of misinformation and the unintended perpetuation of bias.

The future of HR is undeniably intertwined with generative AI. Despite its many benefits, there is still no substitute for the human touch, especially in a field as people-centric as HR.

As we integrate these powerful tools into our HR practices, we must do so with our eyes wide open, keeping in mind that AI should augment human capabilities, not replace them. Maintaining this balance is key to harnessing the promise of AI while avoiding its perils.

 

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 retrain.ai in action, book a demo.

What are the steps to become a Skills-Based Organization?

Posted on by Isabelle Bichler

Our previous post discussed the key benefits of transformation into a skills-based organization, including agility, adaptability, talent optimization, employee engagement and DEI support. So how does an enterprise make the shift to an SBO model?

Here are five key steps:

  1. Skills Assessment. An enterprise’s first step is determining which skills are already in its workforce. A comprehensive skills inventory can be built using skills assessment tools, self-assessment questionnaires, and feedback mechanisms to capture the diverse skill sets present within the organization.
  2. Skills Mapping. Next, HR leaders need to identify the critical skills required for each role and project within the organization, mapping the existing employee skills against these requirements to identify skill gaps and potential opportunities for upskilling or reskilling.
  3. Skills Development. To engage employees in the process, enterprises need to create a culture of continuous learning and skill development, offering training programs, mentoring opportunities, and access to relevant resources. HR leaders can then encourage employees to take ownership of their skills development and provide avenues for them to showcase their skills within the organization.
  4. Skills-Based Hiring and Talent Mobility. Transitioning to an SBO model needs buy-in across the board, meaning hiring practices must focus on skills rather than traditional job titles, skills-based assessments and interviews are used to identify best-fit candidates, and employees are empowered to move across teams and projects based on their skill sets and interests.
  5. Technology Enablement. Leveraging Responsible AI-driven HR technologies can facilitate skills tracking, mapping, and matching at scale. Enterprises must invest in tools that allow employees to showcase their skills, create skill-based profiles, and connect with others via an internal talent marketplace.

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 lower attrition, win the war for talent and the great resignation. retrain.ai fuels Talent Acquisition, Talent Management and Skills Architecture, all in one, data-driven solution. To see it in action, request a demo.

What is a Skills-Based Organization?

Posted on by Isabelle Bichler

One of the most significant shifts taking place within the realm of HR and talent management is the transition to a skills-based organization. Rather than focusing solely on job titles and traditional hierarchies, organizations are recognizing the importance of assessing and leveraging employees’ skills and capabilities to better drive success and foster innovation. But what exactly does it mean to transform into a skills-based organization? Why is it seemingly crucial for HR professionals to lead this paradigm shift?

Understanding the Skills-Based Organization

A skills-based organization places skill sets and capabilities at the core of its talent management strategy. Instead of relying on job titles and formal qualifications, enterprises instead shift their focus to identifying, developing, and utilizing the skills their employees possess so as to effectively match individuals to best-fit projects, initiatives, and roles.

What are the benefits of becoming an SBO?

  • Agility and Adaptability. In today’s rapidly changing business environment, enterprises need to be nimble and adaptable. By focusing on skills, companies can quickly respond to market shifts and reconfigure their teams as required. Skills-based organizations have the advantage of assembling cross-functional teams with complementary skill sets, empowering them to tackle new challenges and seize opportunities efficiently.
  • Talent Optimization. Traditional hiring practices often rely on predefined roles, limiting the potential of employees who may possess valuable skills outside their designated functions. A skills-based approach allows organizations to tap into the full potential of their workforce by unlocking hidden talents and engaging individuals to contribute in areas where they excel.
  • Employee Engagement and Growth. Engaged employees are more likely to be motivated, productive, and loyal to their organizations. In a skills-based organization, workers have opportunities to develop and showcase their skills, leading to increased job satisfaction and a sense of fulfillment. By promoting skill development and growth, organizations can foster a culture of continuous learning, which modern professionals highly value.
  • Diversity and Inclusion. Traditional job descriptions can use terminology that inadvertently creates barriers to entry. A skills-based approach promotes inclusivity by focusing on what an individual can do rather than where they come from or what their previous job title might have been. By removing biases associated with traditional hiring practices, enterprises can build diverse and dynamic teams.

In our next post, we’ll go over the key steps involved in transitioning to a Skills-Based Organization.

 

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 lower attrition, win the war for talent and the great resignation. retrain.ai fuels Talent Acquisition, Talent Management and Skills Architecture, all in one, data-driven solution. To see it in action, request a demo. 

Ready Or Not: 3 Points To Consider As Generative AI Tools Rush To Market

Posted on by Dr. Shay David

This article first appeared in Forbes.

About halfway between the day you first heard about ChatGPT and the day you started wishing you never had, the news became all about a new era of thinking machines. Faster than you can say “Generative AI,” new models are moving into the spotlight, each claiming to be better than the last.

ChatGPT is drawing big names into the generative AI race.

ChatGPT, the groundbreaking chatbot developed by OpenAI, became the talk of the tech world almost overnight and is the most advanced chatbot to date. Predominantly a consumer-focused tool, it was designed to interact conversationally with a user, providing answers and responding to follow-up questions. Demonstrating the extraordinary ability of artificial intelligence to use machine learning to index retrievable content and mimic writing styles, ChatGPT can even adjust tone and voice when provided with direction.

OpenAI technology is also used to power Bing, Microsoft’s less popular search engine launched in 2009 that’s now making a phoenix-rising-from-the-ashes comeback. Claiming capabilities more powerful and accurate than ChatGPT, the company says they’ve applied the AI model to the Bing search ranking engine to increase the relevance of even basic search queries. While this might be true, I think they still have a long way to go. The technology has more than a few kinks—for one thing, it recently told one researcher it was in love with him.

And Microsoft is not alone in its conundrum of determining when these technologies might be ready for market. Despite having arguably the strongest alignment with AI-charged search capabilities, Google fast-tracked its own chatbot, Bard, in order to compete directly with ChatGPT. However, a factual error churned out during a marketing demo derailed its momentum and even caused the stock of its parent company, Alphabet, to drop 9% within a day. Regardless, it’s possible that Bard may ultimately gain an edge over ChatGPT given its access to a wealth of data when integrated into Google’s search engine.

As a specialist in the AI space, my company sees the rapid uptick in generative AI products as a positive. But the promise comes with peril. As of now, these technologies lack the hallmarks of fully enterprise-level solutions. As we observe a burgeoning new tech space, here are a few points to consider:

1. AI is a tool, not a threat, but we must assign it to the right tasks.
Consumer-level chatbot technology showcases what we in the AI space already know: that machine learning and intelligent technology can greatly enhance the human experience. One could argue that when AI takes on more repetitive, mundane business tasks—and does so with a near-zero error rate—people will be freed up to generate more creative contributions. In the HR arena, AI-driven tools can map the skill sets of entire organizations, revealing hidden talent and new opportunities that may have otherwise been missed.

2. Responsible AI means more than content filtering.
The companies producing these new publicly available chatbots talk about responsibility as the importance of mitigating harmful content. Microsoft, for example, says the new Bing implements safeguards to defend against issues such as misinformation and disinformation. But for an AI product to be truly responsible, the design itself must be responsible. We are seeing this in the HR tech world, as increasing regulations are being introduced to stave off unintended bias in hiring processes. Chatbots and similar technologies must include responsible AI components even before the first piece of content is generated.

3. Better is subjective.
In the scramble to eclipse ChatGPT’s entry into the market, its competitors were launched amid bold superlatives. Microsoft introduced Bing as the tool that would “reinvent search,” providing a faster and more powerful, accurate and capable option than ChatGPT. Meanwhile, Google Bard’s access to more recent data seemed beneficial in the race with ChatGPT, as the OpenAI chatbot model was initially restricted to data collected only through 2021.

When AI is tailored to enterprise-level functionality, however, what’s considered superior in one scenario may not translate to an advantage in another. Whereas industry-specific AI tools are designed to organize, analyze and structure data precisely enough to inform critical business decisions, vertical-specific leaders must build AI models that are based on industry know-how and language to perform specific tasks. Businesses utilizing such technologies also depend upon contractual assurances like Service Level Agreements (SLAs) to outline vendor expectations and set performance metrics, something open chatbots can’t provide.

Conclusion
No doubt the consumer-facing generative AI race is just beginning. Advances and missteps are an inevitable part of growth, but I look forward to seeing how it all plays out, with the hope that it helps people view AI anew, through the lens of curiosity and potential.

HR Evolution in the Age of Talent Intelligence

Posted on by Dr. Shay David

This article originally appeared in Forbes.

In a year defined by the stark contrast of looming layoffs and a continued skilled worker shortage, it’s likely that 2023 will highlight the importance of HR’s role in leveraging data-driven insights.

At the end of last year, recruiting was a top priority for 46% of HR leaders, with enterprises revamping sourcing strategies to meet the demand. At the same time, 61% of business leaders were predicting layoffs at their companies. None of that slowed the nation’s quit rate, which landed at 4.1 million workers for the month of December 2022, the same time Robert Half’s “Job Optimism Survey” reported that 46% of professionals were looking, or planning to look for, a new job in 2023.

Such contradictory data can make an HR leader’s head spin. Figuring out how to manage it all on budgets tightened by economic instability is the icing on a very bitter cake. The answer?

In 2023, organizations need to be creative and efficient in their approach to achieving HR goals when using data-driven insights.

HR leaders need to synchronize talent acquisition, talent management and organizational skills mapping to create an agile and continuously growing workforce. By acting on AI-fueled, data-driven insights, enterprises can gain a better understanding of their organization’s job and skills architecture and plan for future skills demand—that is, if HR approaches talent intelligence properly.

HR’s Role In Talent Intelligence

Here are some tips for HR leaders on how to approach talent intelligence:

  1. Avoid the DRIP Problem. 

To compete within the volume, speed and disruption of today’s talent landscape, data is king—but actionable information and knowledge are the castle. Amassing data for data’s sake holds little value and can often lead to a DRIP problem: being data rich, information poor. Conversely, organizing, analyzing and interpreting data can provide rich knowledge and actionable insights needed to fuel better business decisions.

Given the continued talent shortage, expedited skills identification can be a great differentiator for high-performing recruiting teams. To source, screen, hire and retain the right employees with the in-demand skills needed for business success, enterprises should focus on harnessing the true value of data. By actively converting insights into strategies, HR leaders can proactively address skills gaps, plan for future skill needs and develop a more engaged workforce.

  1. Consider starting with job architecture and skills mapping.

To remain competitive and future-proofed, HR innovators should evaluate the skills their employees have, those they need and those that will likely become critical in the future.

More HR leaders are starting with job architecture, building a skills framework using unified skills language to better understand their people and spot hidden talent, diverse capabilities and skills gaps. They can then deploy talent efficiently during times of rapid change, scaling teams up or down as needed. Given the economic uncertainty, knowing where the risks lie can empower HR innovators to upskill, reskill and redeploy—rather than lose—good people.

  1. Don’t fall victim to talent scarcity.

Today’s talent shortage has negatively impacted businesses for well over a year, with more than 77% of CEOs reporting the ability to hire and retain skilled talent as an important factor in achieving growth. Traditional sourcing strategies aren’t enough; HR leaders must be more targeted and efficient than ever to navigate inevitable shifts in the market.

HR leaders can aim to expedite time and labor-intensive tasks—like scanning CVs for skills—so talent acquisition leaders can focus on more complex work. The goal is to find the right people quickly and accurately. In working alongside talent intelligence, HR leaders should focus on skills-based candidate profiling to revive past candidates as additional potential hires.

  1. Focus on internal mobility for talent retention.

Pursuit of the right talent doesn’t end once HR leaders hire people into open roles. Enterprises must proactively “recruit” their employees throughout their tenure via professional growth opportunities. Employees without a clear vision of their future within their organization are statistically more likely to leave.

Career development for today’s workers means learning and evolving within a multidirectional framework, with space to explore open roles, projects, gigs or mentorships, and with access to learning opportunities to reach them. As such, HR innovators can identify hidden talent within their workforce and visualize internal mobility pathways through skills development, increasing the likelihood that their best people will look for their next opportunity within the organization rather than leave to seek out other options.

Learning Curves And Challenges With Talent Intelligence

As HR leaders continue to define their role in the age of talent intelligence, it’s important to note the current limitations, challenges and learning curves associated with the technology.

Regulations And Responsible AI

As AI becomes more embedded in HR processes, so does the responsibility placed on enterprise leaders to manage the potential implications of AI adoption and to implement responsible AI principles. Responsible AI sources and screens applicants based on capabilities, masking demographics or other factors that can introduce unintended bias.

Regulations mandating bias audits, fairness algorithms and explainability to ensure responsible AI compliance are already on the rise. Regulations take shape from municipalities to states and globally, with compliance guidelines being defined in real time. Enterprises must continue tracking these developments to ensure their responsible AI parameters are in place.

HR Tech: What’s Working, What’s Not

Finally, the explosion of new technologies and AI-driven solutions in the HR space has put more data and automation in the hands of enterprises than ever before. HR leaders under pressure to make business decisions that fuel success, and to do so under tightening budget constraints, are taking a closer look at their HR tech stack to determine what’s adding value and what isn’t.

Overlap between HR tech solutions, or inefficiencies within them, represent an unnecessary expense that’s no longer considered the cost of business. Evaluate your tech stack to ensure there are no redundancies.

As 2023 continues to unfold, HR leaders can solidify their role in the age of talent intelligence.

 

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 lower attrition, win the war for talent and the great resignation in one, data-driven solution.

To see it in action, request a demo.

 

Sources:

What Will HR Focus on in 2023? | Gartner | October 2022

When Does Your Salary Become a Threat to Your Job Security? | NASDAQ News | 2023

80% of workers who quit in the ‘great resignation’ have regrets, according to a new survey | CNBC make it | February 2023

Nearly Half of U.S. Workers Plan to Look for a New Position in the New Year | Robert Half | December 2022

 

Event Recap: Responsible HR Forum 2023 presented by retrain.ai

Posted on by Isabelle Bichler

There’s something incredible that happens when thought leaders and knowledge seekers gather to explore a critical topic. Such was the vibe at the first-ever Responsible HR Forum presented by retrain.ai. Below, find a brief overview of the day’s sessions, which you can now access as podcast or vidcast recordings.


Keynote: EEOC Commissioner Keith Sonderling

Starting off the day, keynote speaker Commissioner Keith Sonderling of the EEOC shared insights on the expansion of Responsible AI governance across the U.S., emphasizing that current regulations put the onus on businesses using AI systems to ensure they generate fair end results–not on the makers of AI systems.

Watch the vidcast | Listen to the podcast

 

Ready or Not, Regulations Are Coming 

Talk of Responsible AI continued into the first panel discussion, where Commissioner Sonderling was joined by Scott Loughlin of Hogan Lovells, Rob Szyba of Seyfarth Shaw and Niloy Ray of Littler to discuss the new AI Audit Law in New York City, the far-reaching implications of seemingly local regulations, and how the European Union’s approach to AI governance differs from the U.S.

Watch the vidcast | Listen to the podcast


The Paradox of the HR Mission: Creating a Multidimensional View of Talent

In conversation with retrain.ai’s Amy DeCicco, Dr. Anna Tavis of the Human Capital Management Department at New York University and Dr. Yustina Saleh from The Burning Glass Institute posed provocative questions, encouraging attendees to think about questions like whether empathy is truly a skill or a trait, or how HR leaders can tell from a skills profile whether or not a candidate will be able to do the job needed.

Watch the vidcast | Listen to the podcast


Becoming a Skills-Based Organization: More Than a Trend?

With more enterprises talking about transforming to an SBO model, Dr. Sandra Loughlin of EPAM Systems shared lessons learned from her company’s transformation, while Heidi Ramirez-Perloff discussed The Estee Lauder Company’s exploration into SBO strategy. Urmi Majithia of Atlassian delved into executing technology to help overcome the challenges of becoming an SBO, and Ben Eubanks of Lighthouse Research & Advisory broke down the larger SBO concept to a tangible level regarding individual employees and hiring managers.

Watch the vidcast | Listen to the podcast


The Hidden Architecture of a Skills-Based Organization

Following the panel discussion, Dr. Loughlin sat down for a one-on-one with retrain.ai CEO Dr. Shay David to go more in depth into EPAM’s experience developing a thriving SBO strategy, sharing benefits, pitfalls and lessons learned along the way.

Watch the vidcast | Listen to the podcast

 

Can Innovation and Regulation Co-Exist? How ChatGPT Sparked the Conversation

No discussion around Responsible HR would be complete without an exploration of the huge impact ChatGPT and other generative AI solutions are having on the tech space.  Leading a fascinating discussion on the topic were Yuying Chen-Wynn of Wittingly Ventures and Art Kleiner of Kleiner Powell International, who examined the potential of generative AI to greatly improve business systems, as well as the ethical AI use questions that remain in the midst of growing regulation.

Watch the vidcast | Listen to the podcast


Continuing the Conversation: The Responsible HR Council

To conclude the Responsible HR Forum, retrain.ai announced the formation of our Responsible HR Council. Like the Forum, our Council will involve experts from academia, law, enterprise, government and nonprofit sectors. We’ll meet quarterly to get up to speed on new AI legislation, new AI technologies, and the melding of the two within Responsible HR practices. Check back for details soon! 

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 lower attrition, and win the war for talent and the great resignation. retrain.ai fuels Talent Acquisition, Talent Management and Skills Architecture, all in one, data-driven solution. To see it in action, request a demo. 

The Responsible HR Forum Presented by retrain.ai

Posted on by Isabelle Bichler

The conversation around responsible HR innovation and best practices has been growing steadily as enterprises look to ethically pursue equitable, diverse workforce growth. Its importance has further increased given the NYC AI Audit law taking effect in July 2023. 

Up to this point, much of the conversation has taken place disparately, with HR leaders, technologists and regulators operating in silos.

We are thrilled to announce that this May, we are hosting a first-of-its-kind event focused entirely on Responsible HR. We’re bringing together key stakeholders to form a community of HR leaders, technologists, educators, advocates and regulators to collaborate on our collective journey toward designing and adopting Responsible HR practices. 

The Responsible HR Forum

presented by retrain.ai

May 17, 2023

New York City

This full day of exploration and discussion will be kicked off by our esteemed keynote speaker, Commissioner Keith Sonderling of the Equal Employment Opportunity Commission (EEOC).

Keith Sonderling, Vice Chair and Commissioner, EEOC

Commissioner Sonderling will discuss increasing Responsible AI regulation as well as what’s on the legislative horizon for enterprises and HR leaders implementing AI-based tech solutions. 

You’re invited to join us as we bring together CHROs, regulators, legal experts, analysts, academics, nonprofits and more for a day of invigorating discussions, shared ideas and key strategies to prepare for this next wave in the future of work.

 

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 lower attrition, win the war for talent and the great resignation in one, data-driven solution. To see it in action, request a demo. 

White Box vs. Black Box HR Solutions: What’s the Difference

Posted on by Isabelle Bichler

As AI becomes increasingly embedded in HR systems, enterprise leaders face growing accountability from regulators, their C-suite, applicants, and more to ensure their solutions use ethical, responsible systems to mitigate unintended bias. As a result, Responsible AI is becoming a business mandate, with increasing momentum around laws requiring audits to ensure all benchmarks of Responsible AI are in place.  

One key component of Responsible AI is explainability. Users of an AI-based system should understand how their AI gathers, organizes and interprets data, as well as how the platform produces outcomes.

White box = Explainability

The level of transparency needed to fully explain an AI solution can only be found in what is referred to as a white box solution. With this approach, a full end-to-end view of an AI system’s functionality enables system users to see the what of the system–its data output–while also being able to ask the why–the methodology behind the results.

Such interpretability also allows data scientists and analysts to test the design and internal structure of an AI system in order to authenticate the input and outflow, gauge for errors or inconsistencies, and optimize functionalities accordingly.

What White box Means for HR Leaders

A white box AI solution empowers users to question processes and challenge results, which is especially critical when using such technology within HR functions. Armed with a thorough understanding of their AI solution, an HR leader can be sure their system is performing critical functions, such as mitigating bias risk within its machine learning models. Assured of such mitigation, the organization can stand behind hiring practices that fully support their diversity and inclusion goals.

Black box = Blind Trust

Conversely, there are AI systems for which explanations are too difficult to understand–or aren’t available at all. These are often referred to as black box solutions. In certain settings, black box AI can be useful. The algorithmic complexities necessary in fraud prevention systems, for example, are not explainable in simple terms. 

But within HR functions, a black box system doesn’t allow users to understand how the AI arrives at its conclusions around hiring decision support. As such, there is no visibility to detect errors within the processes, including the presence of possible bias permeating the algorithms.

What Black box Means for HR Leaders

For these reasons, black box solutions represent a significant risk to HR innovators. In the larger sense, they demand a significant level of blind trust. More specifically, by masking information that can derail DEI hiring practices, they render an AI  solution non-compliant in the face of increasing Responsible AI regulation.

retrain.ai and Responsible AI

In providing end-to-end transparency for platform users, retrain.ai is a white box solution. In choosing this methodology, retrain.ai supports the rights of enterprises to know and understand how their HR platforms deliver critical information.

As part of our larger commitment to leading the forefront of Responsible AI innovation in the HR Tech space, retrain.ai works with the Responsible Artificial Intelligence Institute (RAII), a leading nonprofit organization building tangible governance tools for trustworthy, safe, and fair artificial intelligence. To see the retrain.ai difference book a demo. 

 

 

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 lower attrition, win the war for talent and the great resignation in one, data-driven solution. To see it in action, request a demo.