The use of large language models (LLMs) in hiring promises to streamline candidate screening, but it also raises serious concerns regarding accuracy and algorithmic bias where sufficient safeguards are not in place. In this work, we benchmark several state-of-the-art foundational LLMs - including models from OpenAI, Anthropic, Google, Meta, and Deepseek, and compare them with our proprietary domain-specific hiring model (Match Score) for job candidate matching. We evaluate each model's predictive accuracy (ROC AUC, Precision-Recall AUC, F1-score) and fairness (impact ratio of cut-off analysis across declared gender, race, and intersectional subgroups). Our experiments on a dataset of roughly 10,000 real-world recent candidate-job pairs show that Match Score outperforms the general-purpose LLMs on accuracy (ROC AUC 0.85 vs 0.77) and achieves significantly more equitable outcomes across demographic groups. Notably, Match Score attains a minimum race-wise impact ratio of 0.957 (near-parity), versus 0.809 or lower for the best LLMs, (0.906 vs 0.773 for the intersectionals, respectively). We discuss why pretraining biases may cause LLMs with insufficient safeguards to propagate societal biases in hiring scenarios, whereas a bespoke supervised model can more effectively mitigate these biases. Our findings highlight the importance of domain-specific modeling and bias auditing when deploying AI in high-stakes domains such as hiring, and caution against relying on off-the-shelf LLMs for such tasks without extensive fairness safeguards. Furthermore, we show with empirical evidence that there shouldn't be a dichotomy between choosing accuracy and fairness in hiring: a well-designed algorithm can achieve both accuracy in hiring and fairness in outcomes.

Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to ... [ ] discover the innate capabilities of people and matching them to new opportunities in their own companies. Since its founding in 2016, Eightfold.ai's Talent Intelligence Platform continues to see rapid global growth, attracting customers across four continents and 25 countries, supporting 15 languages with users in 110 countries. Their Talent Intelligence Platform is built to assist enterprises with Talent Acquisition and Management holistically. Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies.

This is the list of the 10 most exciting machine learning start-ups you should be following in 2022. Artificial Intelligence has been a hot area of innovation in recent years and ML is one of the major sections of the whole AI arena. ML refers to the development of intelligent algorithms and statistical modeling that allow for further programming improvement without having to code them explicitly. Machine learning can make a predictive analysis app more precise over time, for instance. ML is not without its problems.

The pandemic has changed how folks operate, forcing human resources leaders to wager AI and other new technology and procedures that encourage a more flexible, adaptive, and fluid work force. There were "seismic shifts" in how organizations function, based on Sage's recent poll of 500 senior HR and individuals leaders. A third of those HR leaders said they're changing the way they employ by building greater candidate encounters for candidates, on-boarders, and new joiners and focusing on labor adventures. While 24 percent of organizations are using AI for recruiting, that amount is predicted to increase, with 56% reporting that they intend to embrace AI in the following calendar year. Sage's findings indicate a steady expansion through time, whilst Gartner's Artificial Intelligence Survey in March 2020 discovered that 17 percent of associations used AI-based talent control methods in 2019.

The pandemic has transformed how people work, forcing human resources leaders to bet on AI and other new technologies and processes that support a more adaptive, flexible, and fluid workforce. There have been "seismic shifts" in the way organizations operate, according to Sage's recent survey of 500 senior HR and people leaders. A third of the HR leaders said they are changing how they hire by building better candidate experiences for applicants, on-boarders, and new joiners and focusing on workforce experiences. While 24% of companies are currently using AI for recruitment, that number is expected to grow, with 56% reporting they plan to adopt AI in the next year. Sage's findings suggest a steady growth over the years, while Gartner's Artificial Intelligence Survey from March 2020 found that 17% of organizations used AI-based talent management systems in 2019.

Bottom Line: The most urgent talent management issue every business is facing today is how to improve Diversity and Inclusion (DI) by reducing the potential of bias and evaluating candidates on capabilities first. Organizations need to focus more on using AI and machine learning techniques to identify, recruit, and hire candidates based on their capabilities while removing as many potential bias triggers as possible. Businesses that are making DI an integral part of their companies are experiencing an 83% improvement in their ability to innovate, a 42% increase in team collaboration effectiveness, and a 31% improvement in customer responsiveness, according to Deloitte. A study by the American Sociological Association found that companies with the highest levels of racial diversity attain 15 times the sales revenues of those organizations with the lowest levels. McKinsey found that excelling at DI is directly related to higher profitability and value creation.

If AI is well equipped to expedite arduous onboarding processes, is it equally adept in the actual hiring process? Employers the world over have long dreamed of harnessing technology to widen their recruitment net. Well, to get the best talent in the door, to reduce reliance on human recruiter subjectivity, to achieve a more diverse, representative, and sustained employee base. The reality is that recruitment technology and AI can too readily propagate historical bias' in hiring. The humans feeding the systems with data are blind to their own prejudices working their way in.

Bottom Line: Dexcom and Micron adopting a single AI platform for talent management that adapts to their specific HR strategies and provides new insights is delivering significant results. AI-based platforms provide new insights, intelligence and guidance to CHROs and HR leaders, helping them close the growing talent gaps their organizations face. By integrating hiring, internal mobility, diversity & inclusion, contingent workforces, training & development and performance management all on a single AI platform, HR leaders gain greater insights into closing talent gaps. And it's encouraging to see how AI platforms evaluate candidates on their capabilities while anonymizing factors that might lead to hiring bias. Interested in learning more about why AI platforms are gaining adoption, I recently attended a webinar co-hosted by Talent Tech Lab (TTL) and Eightfold.ai The webinar is titled An AI-First Approach to Recruiting with Eightfold and TTL.

Talent acquisition is one of the significant challenges that companies across all the major domains face. Even if the right candidate is hired, retaining them is another challenge that companies have to deal with. Companies are now exploring new-age technologies such as AI to deal with this crisis. And, to facilitate these intelligent solutions are startups such as Eightfold, which claims to be the industry's first talent intelligence platform built for enterprises to address talent acquisition and management holistically. Founded by Ashutosh Garg and Varun Kacholia who have extensive experience in building AI programs in large technology companies, wanted to apply the experiences learnt in AI into talent acquisition.

Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to ... [ ] discover the innate capabilities of people and matching them to new opportunities in their own companies. Since its founding in 2016, Eightfold.ai's Talent Intelligence Platform continues to see rapid global growth, attracting customers across four continents and 25 countries, supporting 15 languages with users in 110 countries. Their Talent Intelligence Platform is built to assist enterprises with Talent Acquisition and Management holistically. Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies.