Category: Blogs

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. 

Disruption That’s Here to Stay: Skills Language & Talent Intelligence 

Posted on by Isabelle Bichler

Across industries, the state of workforce management has been rocked by perpetual change over the last three years. If one truth has arisen from trends like the war for talent and the great resignation, it’s this: People are an organization’s greatest asset. 

Without the right people in best-fit roles, businesses risk obsolescence in a competitive landscape driven by new and evolving in-demand skills. So real is the challenge, a majority of CEOs have reported that the ability to hire and retain skilled talent is their most critical barrier to achieving growth.

Unified Language: The Importance of Skills

For HR leaders, the new world of work demands that talent have the specific capabilities needed in order to succeed in their role. Gone are the days of impressive titles or degrees; in-demand skills are what make or break recruiting efforts. Internal mobility is forever changed as well, with the upward professional ladder climb giving way to a more agile rock wall where skills-based opportunities can come from any direction in a myriad of forms. Roles, projects, gigs, mentorships, learning pathways–all are integral parts of today’s professional development spectrum.

AI: The Rise of Talent Intelligence

To break down every open role and job description into skills needed, or to scan every CV into skills language, would take a traditional HR team more hours than are even close to possible. Yet having a clear understanding of what skills they already have in their workforce, where the skill gaps are located, and which internal or external candidates can bring those skills to the table is critical to future-proofing their organization.

To both expedite the process, and to do so with granular precision, HR innovators are increasingly implementing talent intelligence solutions. 

What is Talent Intelligence?

Talent intelligence is AI-driven technology that unifies, organizes and interprets a company’s internal data, and combines it with external data on market trends,  emerging skills and labor statistics  in a way that informs and empowers HR leaders to make better workforce planning business decisions. Similar to the groundbreaking capabilities demonstrated by ChatGPT, talent intelligence uses generative AI with similar language processing technology, but expands on the model to provide a fully explainable enterprise-level solution. Built on ethical, Responsible AI means such solutions actively mitigate the risk of unintended bias seeping into machine learning cycles, which can derail DEI hiring practices. 

AI-driven Talent Intelligence and Skills Matching

Using talent intelligence to synthesize the combination of AI capabilities and skills-focused workforce development empowers HR leaders to make faster, better business decisions.

Skills Architecture

Making the best decisions around hiring and internal mobility means HR leaders need to have a clear, granular view of what capabilities their employees have, where the skills gaps lie, and how to future-proof their workforce through developing talent.

Using AI-driven talent intelligence to skills-map an enterprise workforce, HRs can establish unified skills language and an agreed-upon skills framework. Matching it with data insights, they can then align talent decisions with organizational goals.

Talent Acquisition

It’s estimated that in the U.S., it takes more than a month to fill an open position–and that on average, an HR leader must review more than 150 CVs for a single role. Multiply that across a large hiring initiative and there’s a very real cost to an enterprise, including recruiting expenses, time invested by departmental leaders and managers in supporting the hiring process, and the productivity disruption of a prolonged vacancy.

AI-driven talent intelligence helps HRs zero in on best-fit candidates more quickly by analyzing applicants at an atomic level, breaking down their talents into individual skills. Matching applicants with open opportunities, roles or projects based solely on skills means HR leaders can link candidates to best-fit roles with room to grow; and they support DEI goals by eliminating demographic or other information that can introduce unintended bias into the equation.

Talent Management 

As millions of workers quit their jobs during the Great Resignation, one reason continually showed up in the research: Lack of opportunity for advancement. Put simply, at a time when there are more open roles than there are candidates going after them, HR leaders must strategize how to provide employees with a vision for future opportunities that will utilize, challenge and develop a worker’s skills.

AI-driven talent intelligence gives HRs a watchtower view of their workforce, including a granular understanding of employees’ strengths, skills gaps, potential capabilities and hidden talents. Fueled by these insights, HR leaders can provide their talent with personalized career pathing, internal mobility opportunities including roles, projects, gigs and mentorships–providing the kind of positive, proactive employee engagement that’s more likely to retain valuable talent. 

 

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. 

The 5 Pillars of Responsible AI

Posted on by Isabelle Bichler

Beginning in April 2023, NYC employers—and all organizations hiring and doing business in NYC—will be subject to one of the most stringent regulations governing AI to date. We’ve written extensively about the evolution of Local Law #144, which prohibits employers from using Automated Employment Decision Tools (AEDT) in hiring and promotion decisions unless they’ve taken affirmative measures. Specifically, employers using AEDTs in hiring must have them independently audited and must notify candidates in advance of their use. 

Why is this important?

As AI becomes more embedded in HR systems, enterprise leaders face increased responsibility to ensure their solutions use Responsible AI to mitigate unintended bias risk. 

What exactly makes AI responsible?

Responsible AI uses specific methodologies that continuously test for bias against personal characteristics and eliminate information that can introduce unintended bias. 

In all, there are 5 pillars of Responsible AI:

 

  • Explainability and Interpretability – AI machine learning outcomes, as well as the methodology which produces them, are explainable in easily understandable business-speak. Platform users have visibility into the external and internal data being utilized and the platform’s data structurization and outcomes delivery.
  • Fairness algorithms – AI machine learning models mitigate unwanted bias by focusing on role requirements, skills maps and dynamic employee profiles while masking demographic and other information that can potentially introduce bias.
  • Robustness – Data used to test bias is expansive enough to accurately represent a large data pool while being granular enough to provide accurate, detailed results.
  • Data Quality and Rights – AI system complies with data privacy regulations, offering transparency to the user around proper sourcing and usage of data, and avoiding using data beyond its intended and stated use.
  • Accountability – AI systems meet rigorous accountability standards for proper functioning, responsible methodology and outcomes, and regular compliance testing. 

In addition to building our Talent Intelligence Platform on Responsible AI from the ground up, retrain.ai exemplifies a larger overall commitment to innovation built on Responsible AI. As such, we work with the Responsible Artificial Intelligence Institute (RAII), a leading nonprofit organization building tangible governance tools for trustworthy, safe, and fair artificial intelligence. To learn more, visit our Responsible AI Hub.

 

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.

ChatGPT Is Changing the AI Game, But Enterprises Need More

Posted on by Dr. Shay David

Chances are you’re one of the millions of people who have played with ChatGPT, the game-changing generative AI assistive technology released by OpenAI. Designed to interact conversationally, the advanced chatbot can engage in dialogue with a user to provide answers, respond to follow-up questions, correct mistakes, and adjust tone and voice when provided with direction. 

A consumer-focused tool, ChatGPT aptly showcases the groundbreaking ability of generative AI to use machine learning to index retrievable content and mimic writing styles. As such, it has prompted a conversation around its possible business uses, garnering opinions from those who see great potential–and those who fear for their jobs. Some even suggest that we are nearing the singularity, or at least seeing for the first time machines that can pass the (in)famous Turing test.

>> Book a demo to see retrain.ai’s generative AI in action

As leaders in the AI space, we see ChatGPT as an example of a set of tools with the potential to transform business processes. Yet it has notable limitations when viewed through the lens of an enterprise-level solution. There are four main areas in which this differentiation is most apparent:

  1. AI-driven technology designed for business incorporates features optimized for a particular industry. retrain.ai, for example, was built from the ground up as a specialized solution for the HR space. As such, our technology expands beyond a ChatGPT-level machine learning model to one which can organize, analyze and structure data precisely enough to inform critical business decisions. We anticipate that in each industry, vertical-specific leaders will emerge who build AI models that are based on industry know-how and language, and are tailored toward specific tasks.
  2. Explainability is another critical feature of specialized AI technologies that you won’t find in a general-purpose chatbot platform. Explainable solutions are referred to as white-box technology, meaning machine learning outcomes, and the methodology which produces them, can be explained using general business-speak. For enterprises trusting generative AI systems with critical decision assistance, this means they have a clear enough understanding to question or challenge the platform’s output. 
  3. Without white-box explainability, an AI system is lacking a key component of Responsible AI, a non-negotiable design element, when it comes to bias prevention in hiring processes. Only by using Responsible AI can an enterprise ensure candidates are being screened solely on skills, eliminating information that can introduce unintended bias. Increasing regulations will also hold enterprises accountable for making sure they are using Responsible AI in hiring practices.
  4. Enterprise-level solutions are implemented to directly impact business performance. They come with contractual assurances like Service Level Agreements (SLAs) to outline vendor expectations and set metrics by which the technology’s effectiveness will be measured. Open platforms like ChatGPT don’t offer performance metrics or customized services, leaving adopters with no recourse should something go wrong. The same is true about data sovereignty, and compliance with privacy standards like GDPR. We anticipate that the big vendors like Microsoft and Google will soon offer enterprise grade service assurances around consumer tools like ChatGPT (or Google’s Lambda), but until that time, the use of consumer tools cannot be relied upon.

The retrain.ai Talent Intelligence Platform uses generative AI with similar language processing technology to ChatGPT’s, but expands on the model to provide a fully explainable enterprise-level solution designed specifically for talent intelligence, while complying with SOC2, GDRP, and offering enterprise grade SLA. We’re excited to see how the market continues to develop and how enterprises transform years old practices with new tools. 

>> Book a demo to see retrain.ai’s generative AI in action

See how the retrain.ai Talent Intelligence Platform fuels your talent acquisition, talent management, job architecture and DEI goals, contact us today. 

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 architecture, 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.

Learn more: book a demo. 

Update: Responsible AI and the NYC Audit Law Pushed to Q2

Posted on by Isabelle Bichler

UPDATE: The Automated Employment Decision Tool (AEDT) Law (Local Law 144) slated to take effect in New York City, on April 15th will be delayed until May 6, 2023.

On Monday, December 12, 2022, the New York City’s Department of Consumer & Worker Protection (“DCWP”) announced the Automated Employment Decision Tool (AEDT) Law (Local Law 144) slated to take effect in New York City, on January 1st will be delayed until April 15, 2023.

Created to ensure organizations using automated / AI-based hiring tools proactively protect against potential or unintended bias in the processing of candidate information or hiring decisions, the law requires organizations using such tools to comply with mandatory independent audits of AI systems and transparency about their use with candidates. With only months to go, this means the time for enterprises to evaluate their systems for ethical, Responsible AI is now. 

Learn how this law impacts HR Leaders everywhere, not just in NYC >>

Despite its designation as a local law, HR leaders everywhere must remain engaged in tracking its evolution. New York City is the epicenter of the business world, if an enterprise operates and has employees or is hiring employees in NYC this regulation applies to them.

So why the delay? 

The New York City Department of Consumer and Worker Protection (DCWP) is overseeing the rollout of the law. They say the delay is due to the high volume of public comments generated by a public hearing held in November. A quick review of the department’s website shows well over 100 pages of feedback and inquiries stemming from that hearing, including comments submitted by retrain.ai. The DCWP aims to review all input before planning a second hearing.

What sort of questions came up? 

Numerous points were raised, ranging from what specifically defines an AEDT to how regulation can remain effective without stifling innovation. A few specifics included:

  • What sort of qualifications and certifications will be required to select and authorize an independent auditor? 
  • How will data size be figured into the equation, given that some businesses won’t possess the robust data set necessary to accurately determine bias?
  • What options are available to candidates who opt out of the AI-based systems, as is their choice? How will they be assured equal consideration in the hiring process?

A second public hearing will be planned for the first quarter of 2023. In the meantime, we’ll keep you updated in our Responsible AI Hub, where you can also learn what constitutes unbiased, Responsible AI, what to look for in an HR Tech vendor to ensure compliance, and how retrain.ai uses the five pillars of Responsible AI to support the growth of a skilled, diverse workforce.  

To experience a personalized walkthrough of how retrain.ai can help you reach your HR goals, visit us here.

Additional resources: 

  • Responsible AI and the NYC Audit Law: What You Need to Know Before 2023 – On-demand webinar
  • Responsible AI: Why It Matters and What HR Leaders Need to Know – On-demand webinar