The Hidden Industry of Data Brokerage and Erosion of Human Dignity
In an era of big data, an individual’s worth is increasingly determined not by merit or effort, but by unseen algorithms analyzing their shadow data.
Data brokerage—the multi-billion-dollar industry of collecting, analyzing, and selling personal data—operates in the dark, feeding information to corporations, employers, and even talent agencies. These data brokers aggregate consumer behaviors, financial transactions, social media activity, and other personal details to construct predictive profiles that can make or break employment prospects.
Data Brokers: Who They Are and What They
Data brokers, often operating outside consumer awareness, gather and sell vast amounts of personal data, often without explicit consent. Studies have highlighted the murky ethical and legal frameworks within which these companies operate. According to S. Mishra (2021), data brokers offer limited transparency regarding their practices, leaving individuals powerless over how their data is used. Similarly, Kanwal & Walby (2024) report that once personal profiles are leaked or hacked, they enter a shadow marketplace where they are bought, sold, and exploited.
The commodification of personal information extends beyond financial transactions. According to Crain (2018), the collection and resale of personal data is framed as a transparency issue, but in reality, most individuals have no meaningful choice or control. This information—ranging from shopping habits to political affiliations—can then be used against individuals in ways they may never realize.
The Consequences: Employment and Economic Marginalization
One of the most insidious uses of shadow data is in employment screening and workforce participation. Talent agencies and recruitment firms increasingly rely on AI-driven hiring processes that integrate data broker profiles. This can lead to biased hiring decisions based on inaccurate, outdated, or misleading information. Sherman (2021) found that individuals often do not even know data about them is being collected and evaluated, and in most cases, they have no legal recourse to challenge erroneous conclusions.
The AI systems used to assess potential hires claim to predict job performance, reliability, and even "cultural fit.” However, these systems are often trained on biased data sets, reinforcing systemic discrimination. According to Yeh (2018), without proper regulation, AI-driven hiring systems can perpetuate existing inequalities rather than eliminate them. This means that individuals who were once gainfully employed may find themselves blacklisted from opportunities due to automated judgments based on flawed or unfair data.
AI-driven hiring processes that utilize shadow data can lead to biased employment decisions due to reliance on inaccurate or outdated information[1]. Individuals are often unaware of their data being collected, lacking legal recourse to challenge errors[2]. These systems claim to predict job performance and cultural fit but frequently rely on biased datasets, reinforcing systemic discrimination. Without proper regulation, AI-driven systems risk perpetuating inequality, potentially blacklisting previously employed individuals based on flawed data. Summary: AI-based hiring systems can reinforce discrimination and bias, necessitating regulatory measures to ensure fairness and transparency in employment practices.
Hired Score: The Algorithmic Gatekeeper
One of the most prominent players in AI-driven hiring is HiredScore, an AI-powered recruitment platform that assigns numerical scores to job applicants based on data collected from multiple sources. These scores determine an applicant’s likelihood of being considered for a position, often without their knowledge.
HiredScore claims to use “ethical AI” to make recruitment more efficient, but its reliance on shadow data poses significant ethical concerns. The algorithm factors in information from resumes, past employment records, social media, and even undisclosed third-party sources, making it difficult for individuals to challenge or correct inaccuracies in their assigned scores.
The reliance on AI-driven hiring tools like HiredScore raises questions about transparency and fairness. If an applicant is assigned a low score due to outdated or misleading data, they may be automatically disqualified before a human recruiter ever sees their application. According to Sherman (2021), individuals often do not know data about them is being evaluated in hiring decisions, and they have little legal recourse to dispute AI-generated judgments.
HiredScore’s use of “ethical AI” in recruitment, relying on shadow data, raises significant ethical concerns around transparency and fairness[3]. The platform assigns scores to applicants based on various data sources, including undisclosed third-party information, limiting individuals’ ability to challenge inaccuracies[4]. This practice can lead to automatic disqualification due to outdated or misleading data, with applicants often unaware their data is used in hiring decisions. As Sherman (2021) notes, legal options to dispute such AI-generated judgments are scarce.
Summary: HiredScore’s AI-driven hiring processes lack transparency, posing risks of unfair disqualification and limited recourse.
From the Workforce to the Shadows: The Human Cost
The impact of being reduced to a number and judged based on shadow data can be devastating. People who have been inaccurately categorized or labeled as high-risk, unreliable, or unqualified due to AI algorithms may find themselves unemployable. Once successful professionals can be plunged into poverty, their reputations are irreparably damaged.
Furthermore, Bajwa & Meem (2024) highlight cases where data brokerage missteps have directly led to identity theft and financial ruin, exacerbating the struggles of those already marginalized. When AI systems dictate who is worthy of employment, those deemed undesirable may face deteriorating mental and physical health due to prolonged joblessness and social exclusion.
The Role of Employers: A Double-Edged Sword
For employers, access to vast amounts of personal data through data brokers and AI-driven screening tools presents both advantages and ethical dilemmas. On one hand, these tools promise efficiency in hiring, reducing the time and cost of background checks. Employers can instantly assess thousands of candidates, weeding out those flagged as "high risk" based on past behavior, financial instability, or even social media activity.
However, this efficiency comes at the cost of fairness and human judgment. Many businesses unknowingly rely on flawed or biased data, making hiring decisions based on outdated or incorrect information. Employers may inadvertently exclude talented individuals based on financial difficulties, medical histories, or even misinterpreted online activity. Furthermore, these AI systems can reinforce hiring biases, disproportionately filtering out marginalized groups who already face barriers in the job market.
There is also the issue of liability—companies using such AI-driven hiring processes may face legal challenges if their hiring decisions result in discriminatory outcomes. As Roderick (2014) notes, consumer data brokers have been influencing government and corporate policies in ways that lack transparency, raising serious ethical concerns.
Employers benefit from data brokers and AI-driven tools by enhancing hiring efficiency and reducing costs[5]. However, reliance on potentially flawed data introduces ethical dilemmas, as these systems may inadvertently perpetuate biases and exclude talented candidates based on inaccurate or outdated information[6]. This raises fairness concerns and legal risks tied to discriminatory outcomes. Moreover, the lack of transparency and accountability in data usage exacerbates these issues, underscoring the necessity for ethical frameworks and regulation. Summary: Efficient AI-driven hiring tools pose ethical challenges, necessitating transparency and regulations to ensure fairness and inclusivity in recruitment.
The Psychological Toll on Job Seekers
Being subjected to data-driven hiring systems can have severe psychological effects on job seekers. The feeling of being reduced to a numerical score or AI-generated label strips individuals of their agency, making job searching an impersonal and often humiliating process. Applicants may feel powerless to correct or even access the information influencing their chances of employment, creating a cycle of frustration, anxiety, and despair.
Long-term unemployment due to algorithmic bias can also lead to a host of personal consequences, including:
The Role of Talent Agencies and AI Gatekeeping
A growing concern is the involvement of talent agencies in data brokerage networks. Some agencies collect, buy, or share applicant data with brokers, feeding AI-driven prediction models that determine the likelihood of an individual’s career success. This process often lacks transparency and oversight. Roderick (2014) describes the growing influence of data brokers in shaping government and corporate policies, effectively turning private citizens into quantifiable products.
The question then arises: Who benefits from these data-driven hiring models? Employers gain an ostensibly "efficient" hiring process, but at what ethical cost? Without clear regulatory frameworks, data-driven hiring can become an unchecked mechanism for exclusion and discrimination.
Data-driven hiring models primarily benefit employers by streamlining the hiring process, reducing costs, and ostensibly enhancing efficiency. However, the ethical costs are significant, as these systems often operate without transparency and can perpetuate discrimination, particularly against marginalized groups. The involvement of talent agencies in data brokerage networks exacerbates these issues, as applicant data is collected and shared without adequate oversight, feeding biased AI models. Without clear regulatory frameworks, such practices risk becoming mechanisms for exclusion and discrimination, turning individuals into mere data points and undermining fairness and opportunity in employment contexts.[7],[8]
Conclusion: Fighting for Data Dignity
The commodification of personal data in employment decisions represents a new form of systemic oppression—one that is invisible to most yet impacts millions. The erosion of privacy rights, coupled with AI-driven decision-making, has turned job applicants into numbers, stripping them of autonomy and dignity.
There is a dire need for comprehensive data protection laws that regulate data brokerage and AI-driven employment decisions. As Dixon (2025) emphasizes, individuals must regain control over their data to prevent manipulation and economic marginalization. The alternative is a world where algorithms—not human potential—dictate who gets to work, who remains unemployed, and who fades into the shadows of society.
If unchecked, the rise of data brokerage and AI-driven hiring systems will continue to erode privacy, fairness, and opportunity, leaving behind a society where worth is determined by invisible, unregulated calculations rather than human talent and potential. Comprehensive data protection laws are essential to safeguard privacy and fairness in hiring.[9],[10]
[1] Harris, C. (2023) Mitigating Age Biases in Resume Screening AI Models The International FLAIRS Conference Proceedings
[2] Nuka, T., F., Osedahunsi, B., O. (2024) From bias to balance: Integrating DEI in AI-driven financial systems to promote credit equity International Journal of Science and Research Archive
[3] Gazi, M., S., Gurung, N., Mitra, A., Hasan, M., R. (2024) Ethical Considerations in AI-driven Dynamic Pricing in the USA: Balancing Profit Maximization with Consumer Fairness and Transparency Journal of Economics, Finance and Accounting Studies
[4] Agu, E., E., Abhulimen, A., O., Obiki-Osafiele, A., N., Osundare, O., S., Adeniran, I., A., Efunniyi, C., P. (2024) Discussing ethical considerations and solutions for ensuring fairness in AI-driven financial services International Journal of Frontline Research in Multidisciplinary Studies
[5] C, K. (2025) AI-Powered Recruitment: Transforming Talent Acquisition in the Digital Age Journal of Informatics Education and Research
[6] Rahman, S., M., Hossain, M., A., Miah, M., S., Alom, M., Islam, M. (2025) Artificial Intelligence (AI) in Revolutionizing Sustainable Recruitment: A Framework for Inclusivity and Efficiency International Research Journal of Multidisciplinary Scope
[7] John, E., I., Bello, D., I., E. (2025) Assessing the Role of Good Governance and Policy Making in Land Use abuse Monitoring and Management in Nigeria International Journal of Research and Innovation in Social Science
[8] Bhagwat, A., M., Ferryman, K., Gibbons, J., B. (2023) Mitigating algorithmic bias in opioid risk-score modeling to ensure equitable access to pain relief Nature Medicine 29, 769-770
[9] Kumar, R., S., Lokeshwari, J., Shanmugam, S., K. (2024) AI-Powered Privacy Preservation: A Novel Framework for Adaptive Data Protection 2024 2nd International Conference on Computing and Data Analytics (ICCDA), 1-6
[10] Sebastian, G. (2023) Privacy and Data Protection in ChatGPT and Other AI Chatbots: Strategies for Securing User Information SSRN Electronic Journal