Tag: Responsible AI

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. 

VIDCAST: Keep Your Best People Longer with Opportunities to Thrive

Posted on by Dr. Shay David

Of the millions of workers who quit their jobs over the last two years during the Great Resignation, many cited lack of opportunity for advancement as a major factor. Employees saw investment in their professional development as validation that their contributions were valued and rewarded; its absence sent the opposite message.

Today’s HR leaders must strategize how to hang on to their best-fit hires so they become long-term employees. Much of this comes down to providing a vision for future opportunities in the form of roles, projects and gigs that will utilize, challenge and develop a worker’s skills.

In this session, retrain.ai Co-founder and CEO Dr. Shay David and Chief Research Officer Ben Eubanks of Lighthouse Research discuss how organizations can build a mutually beneficial path forward for valued talent. Their conversation covers:

  • How HR tech can counter today’s quit rates 
  • The connection between internal opportunities and worker retention
  • What we can learn from Great Resignation data
  • The DRIP Problem: Data Rich, Information Poor
  • Implications of the new employer-employee dynamic
  • How AI enhances the human experience at work
  • The importance of Responsible AI and explainability
  • Tips for HR leaders new to using AI-driven tech
  • Talent scarcity as a business problem, not just an HR problem
  • How HRs and hiring managers can align to optimize Responsible AI solutions

 

 

 

VIDCAST: Sourcing and Screening at a Time of Talent Scarcity

Posted on by Isabelle Bichler

In the wake of the Great Resignation, the war for skilled workers rages on, with more open roles than there are job seekers to fill them. Candidates are willing to wait it out to find best-fit roles, demanding (and receiving) higher compensation, more flexibility, community, and an inclusive culture before accepting a full-time job at a traditional employer.

Meanwhile, an open role represents significant costs for an enterprise through both productivity and financial losses; numbers that only compound with each passing day. To avoid such pitfalls, a long-term strategy is needed to navigate today’s talent shortage. 


In the short term, there are immediate measures HR leaders can put in place to get the right people in the right places quickly. These include sourcing and attracting talent through creative recruitment, broadening the talent pool to include active and passive candidates, looking internally for employee mobility opportunities and focusing on skills-based hiring within all of these channels.

In this session, retrain.ai co-founder and COO Isabelle Bichler-Eliasaf and Chief Research Officer Ben Eubanks of Lighthouse Research discuss how AI can invigorate and expedite the sourcing and screening process to help HR leaders hone in on best-fit, diverse candidates faster. Their conversation covers:

  • The biggest hiring challenges today and how HRs are managing them
  • What factors have caused today’s talent shortage
  • The importance of career-pathing opportunities in attracting talent and keep employees engaged
  • How AI and skills-matching can build a talent marketplace to fuel internal mobility
  • How AI can enhance the human experience at work and strengthen DEI goals
  • What constitutes Responsible AI and how does HR tech balance automation and fairness
  • Ben’s 2023 predictions for HR

 

 

 

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

Q&A: The NYC AI Audit Law

Posted on by Isabelle Bichler

UPDATE: Local Law 144 will now go into effect on May 6, 2023.

For organizations using AI in their hiring processes, prepping for 2023 means evaluating compliance with a new law that will take effect in New York City in January, but which will impact millions of HR leaders and job candidates everywhere. 

Local Law 144, or the NYC AI Audit Law, issues updated guidelines for employers using AI in hiring. Part of a quickly growing practice, AI tools are in high demand for companies looking to speed up preliminary candidate screening and enable efficiency in the hiring process. To avoid introducing unintended bias into those actions, however, the AI must be responsible–meaning fully explainable machine learning systems structured to avoid biases that could skew results unfairly. 

With only weeks to go until the NYC AI Audit Law kicks in, there are still plenty of unanswered questions. In this vidcast, retrain.ai Co-founder and COO Isabelle Bichler-Eliasaf speaks with Rob Szyba, partner and employment attorney at Seyfarth Shaw about aspects of the law that aren’t quite clear yet, including:

  • What specifically defines an automated employment decision tool (AEDT)? How much weight is given to the AEDT as one part of a multi-level hiring process?  [Timestamp: 5:08]
  • Who is performing the mandatory AI bias audits required by the law?  [Timestamp: 10:01]
  • What accommodations are given to candidates who opt out of AEDT interview steps?  [Timestamp: 11:28]
  • How are candidates who opt out assured equal consideration?  [Timestamp: 12:38]
  • What happens to organizations in that are new to AI use in hiring and don’t necessarily have enough data to test their system by the time the law takes effect? Will they be considered in default?  [Timestamp 15:32]
  • The law applies in New York City, but what does that mean for businesses based outside of NYC who have offices or even remote workers based in the City?  [Timestamp 19:02]
  • How can those of us in the AI space convey the importance of ensuring that regulation helps the process without stifling innovation? That it protects AI’s ability to enhance the human workforce experience?  [Timestamp 25:06]

Additional Resources:

Not Headquartered in NYC? The New AI-based Hiring Regulations Will Likely Still Apply to You. Blog post

NYC AI Law Update – 4 Important Things You Need to Know Blog post

A New NYC Law Puts Pressure on Talent Intelligence: Will Your AI Solution Be Ready? Blog post

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

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