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Advances in big data, machine learning, and data processing are poised to fundamentally alter the job search and hiring process. Today’s labor market suffers from a critical information gap. Job seekers often step into roles with minimal insight into job expectations, team dynamics, or their manager’s leadership style. Employers, in turn, often struggle to assess whether candidates truly have the necessary skills and competencies. This gap results in mismatched hiring, where job seekers’ skills do not align with employers’ needs. These inefficiencies contribute to wage inequality, prolonged unemployment, and lower productivity, ultimately dragging down economic output.
Employers have strong incentives to fix these inefficiencies. Surveys consistently rank hiring top talent as a leading concern for CEOs, year after year.
A bad hire can be costly—not just financially, but also in terms of team morale, productivity, and company culture. Companies are increasingly leveraging technology to refine hiring. The holy grail in recruitment is advanced candidate screening: A system where companies post job descriptions, platforms attract applicants, and algorithms return a shortlist of top candidates. With strong incentives to cut hiring costs and improve outcomes, companies have unsurprisingly invested heavily in hiring algorithms.
Major job platforms like LinkedIn, Monster, and ZipRecruiter now rely on AI-powered algorithms to recommend candidates to employers. Well-resourced companies have developed in-house platforms to streamline hiring. Examples like Amazon’s 2015 recruitment automation system—since abandoned due to its persistent gender bias—highlights both the promise and the risks of AI-driven hiring.
These platforms primarily rely on candidate-submitted data from resumes, cover letters, and job platform interactions. While useful, these inputs remain incomplete and imperfect for algorithmic recommendations. Resumes and cover letters offer only a partial view of a candidate’s qualifications. Hiring managers rely on referrals, interviews, and skills tests to gain a fuller picture of a candidate. Likewise, AI algorithms are limited by incomplete data. As a result, there is significant potential—and incentive—for algorithms to build richer candidate profiles well before hiring managers and candidates ever meet.
Using unexpected data to predict job success
Leaving aside questions of fairness for a moment, it is important to recognize that unintuitive factors can significantly influence predictive algorithms. For instance, the widely used COMPAS algorithm predicts recidivism risk using questions like, “Do you live with friends?” and “How often do you feel bored? Though seemingly unrelated to criminal behavior, these factors play a key role in the algorithm’s predictions. Similarly, in hiring, factors like commuting distance—unrelated to a candidate’s skills—can strongly predict attrition rates.
The key takeaway: More data improves AI performance even when causal links remain unclear.
Unexpected factors matter partly because employers often prioritize cultural fit as much—or more—than skills and experience. Cultural fit is a vague and subjective concept, often a gateway for bias in hiring. Yet, many hiring managers still consider it crucial. With broader data—beyond resumes and cover letters—algorithms could better assess cultural fit, even if the links between candidate traits and job success remain unclear.
What hiring can learn from digital advertising
Digital advertising excels at predicting cultural fit. Most people are unaware that their digital footprint forms a detailed profile, encompassing demographics (e.g., age, gender, income, education, location), behavioral data (browsing history, search queries, purchases, device usage), psychographics (interests, hobbies, lifestyle preferences, values), and technographics (device type, operating system, browser). If these factors can be used to sell cars, it is hardly a leap to imagine they could also help predict job fit.
Your choice of iPhone or Android may not directly determine job success. The real question is whether cell phone preferences differ significantly between Lockheed Martin and Lululemon employees. Combined with other factors—like an interest in yoga or World War II memorabilia, or a preference for Blake Shelton over Beyoncé—cultural fit becomes quantifiable.
Hiring managers may define “cultural fit” in terms of traits like a growth mindset, self-efficacy, and a can-do attitude. But even these traits can be inferred from digital profiles. For example: Frequent engagement with content on motivational speakers or personal development may signal a growth mindset; involvement in professional development forums or goal-oriented activities, such as fitness tracking apps, could indicate high self-efficacy; and the language individuals use in online posts or comments could be analyzed for signs of optimism or resilience, reflecting a can-do attitude!
Beyond resumes: Reading your skills from your digital footprint
AI algorithms could leverage digital profiles for more than predicting cultural fit: Browsing and search history could help predict how well a candidate’s skills align with job requirements. A software engineer who frequently searches for advanced programming concepts may be a stronger fit for a role requiring that expertise than someone who does not; a candidate who frequently engages with puzzle-solving apps or coding challenges may be better suited for a problem-solving role.
Digital profiles could effectively indicate proficiency in both skills, such as programming languages and broader cognitive abilities like analytical thinking. As a result, recruiters might no longer need to verify proficiencies that are merely listed on resumes—many of which are increasingly AI-proctored.
However, this reasoning overlooks a key point. Hiring managers are not just looking for specific skills or experiences; their priority is identifying the best overall match for the role. Rather than evaluating isolated skills or experiences, matching algorithms assess how well a candidate’s overall profile aligns with job requirements.
A simplified example illustrates how this matching process works. To develop these algorithms, engineers train them on vast datasets of successful and unsuccessful matches—analyzing thousands, if not hundreds of thousands of data points on candidates, job roles, and companies. A successful match may be one where the candidate is quickly promoted or receives positive employer feedback, while an unsuccessful match may involve early departure from the company.
Using this training data, algorithms learn how to weigh each parameter and interpret their interactions to predict successful matches. Certain skills or experiences may be insignificant—or crucial—depending on how a candidate’s profile aligns with past successful hires.
Bias and privacy: The twin dilemmas of AI hiring
The potential for bias and unfairness in AI-driven job matching is clear. We previously mentioned Amazon’s failed 2015 recruitment automation software, which was abandoned after showing gender bias—favoring male candidates simply because most of the company’s past successful hires were men. With the current backlash against DEI initiatives reaching boardrooms and C-suites, one might question whether such biases will be perceived the same way in the future.
AI-driven job matching could introduce an entirely new form of bias. Algorithms that heavily rely on digital footprints may systematically exclude candidates with minimal online presence.
Yet, will fairness concerns outweigh the powerful cost-cutting incentives driving hiring platforms? One can easily envision a future where candidates are expected to enhance their digital footprints to remain competitive—just as they are expected to continuously upskill.
Regulatory efforts, such as the European Union’s proposed AI Act—emphasizing transparency, human oversight, and accountability—and the U.S. Equal Employment Opportunity Commission’s (EEOC) guidance on AI fairness, represent early steps in managing the risks associated with AI-driven hiring. While these measures aim to prevent unintended bias and protect candidate privacy, they remain broad in scope and challenging to implement. Virginia’s HB 2094, the High-Risk Artificial Intelligence Developer and Deployer Act, is an example of a more specific regulatory effort—and it flags a critical distinction between AI-based black-box systems that autonomously make employment decisions and AI-driven tools that assist human decisionmaking.
Things could get worse—or better—depending on how these technologies develop. Future job-matching algorithms could extend beyond resumes, integrating broader data sources to refine predictions. Health and biometric data, or proxies such as engagement with fitness apps, might one day be used to assess candidates’ work habits, stamina, or resilience. Financial data may also come into play. Digital advertisers already estimate income and spending power—hence the absence of ads for caviar, diamonds, and champagne on certain users’ screens. What is to stop hiring platforms from not only ranking candidates but also predicting their salary expectations? Just as banks assess creditworthiness based on spending patterns, hiring algorithms could one day estimate the lowest salary a candidate is likely to accept.
Only stricter—and not necessarily desirable—privacy laws could limit the use of such data. Most people share their personal information willingly—if unwittingly—every time they accept a “user agreement.” One can easily imagine a near future where users readily trade their personal data for access to a “free” job search platform that promises hyper-personalized recommendations.
As companies and regulators navigate these challenges, proactive efforts to define transparent standards and regularly audit algorithmic outcomes will be crucial. Key questions must be addressed: How can transparency in AI-driven hiring processes be assured? What standards should govern the balance between predictive accuracy, candidate privacy, and fairness?
The answer to these questions may determine whether AI-driven hiring enhances fairness and expands opportunities—or merely shifts biases from traditional networks to digital footprints.