Power, Control and Techno-solutionism in Algorithmic Hiring

Resume screening tools, video interviews, and game-based interviews have become common hiring practices in recent years (Law, 2026). These systems are often presented as solutions to phenomena such as inefficiency, inconsistency and bias in traditional recruitment styles (Biradar, 2024). This reflects what Evgeny Morozov (2013) describes as techno-solutionism in his book To Save Everything, Click Here: the belief that complex social issues can be solved through data, optimization and technical solutions. This essay examines how hiring practices create conditions for techno- solutionism to exist, the potential benefits it brings to society, and the harms that can be produced by transferring existing sociopolitical tensions onto technology, and what it means for the larger society.

The question of who gets hired is influenced by political, economic and sociocultural factors. Recruitment processes are also largely shaped by organizational demands for cost, efficiency, and productivity, but also by broader social values about merit and “fit” (Nichols, 2025). Firms must manage large applicant pools while attempting to identify candidates who align with organizational culture and long-term goals and values. However, biases embedded in society and workplaces frequently disadvantage candidates even before formal evaluation begins (Wynn et al., 2025). While “fit” is presented as value alignment, Nichols (2025) argues that it can even be used to mask discrimination and perpetuate exclusion sometimes.

These conditions create an environment in which techno-solutionism can thrive. As Morozov (2013) argues, techno-solutionism reframes the long and expensive process of selecting a candidate as problems that can be optimized, while embedded biases create organizational and societal pressures to reform hiring practices. Furthermore, the subjective nature of hiring presents AI as a more standardized, and objective method of evaluation, which changes this complex social process into a computable technical problem through algorithms, consistent with Morozov’s definition of techno- solutionism.

Although Morozov critiques techno-solutionism for waging ‘war on ambiguity,’ algorithmic hiring systems remain popular as they address several operational inefficiencies within recruitment. Case studies across companies show that AI-based hiring can reduce hiring time significantly. By closely studying data on past employees, candidates selected through AI-based systems have also improved retention rates in companies, which leads to long-term benefits (Kim, 2026). AI can also process thousands of applications at once, making it highly valuable for scalability. It has also made procedures standardized, as the same system is used on all candidates (Baradar et al., 2024).

“Debiased” algorithms have also been produced to tackle problems with bias in both, human, and technical decisions. This class of algorithms is aimed at improving issues with favoring a criterion over the other in recruitment (Ip, 2025). Although Ip (2025) notes that only one fairness criterion can be completely met, even the implementation or mention of debiased AI has notably increased the number of participants who otherwise feared discrimination. However, this reveals the tension between a technical optimization and human judgement. As different fairness criteria prioritize different outcomes such as merit, diversity or equal opportunity, it cannot be a pure calculation. While benefits with debiasing are present, political and social dynamics are embedded in these technical solutions and can be hard to differentiate.

As responsibility for hiring decisions are now distributed across technical systems, algorithm hiring systems shift accountability from organizations and individuals to technology. Candidates rejected through resume-screening systems or video-interview assessments are unable to meaningfully understand the reasoning behind why they were rejected (Law, 2026), which matters because hiring decisions carry consequences for employment, income and career progression. Instead of eliminating subjectivity, organizations are able to defer accountability to the data, the algorithm, or third-party vendors, which Morozov would recognize as a recursive form of techno-solutionism and its ability to shift power.

However, Henrik Skaug Sætra and Evan Selinger (2024) distinguish between narrow “techno-fixes” and broader forms of techno-solutionism that reshape social norms. What initially appears as a techno-fix for inefficient hiring, time, and cost, snowballs into a techno-solutionist framework in which technology mediates judgement. Responsibility becomes harder to locate, not because human decisions disappear, but because power is increasingly exercised through algorithms. This legitimizes hiring decisions under the guise of neutrality.

As algorithmic screening becomes standardized, applicants increasingly adapt their presentation to what automated systems can recognise and reward. Research on applicant strategies shows that applicants report optimizing CVs for keywords that rank better, and practicing video interview formats to gain familiarity rather than displaying competence (Armstrong et al., 2023). This distortion of the hiring process also creates inequality between candidates who already know how to optimize their CVs to rank higher versus the ones that don’t. This tension makes it clear that techno-solutionism not only reframes problems incorrectly, but amplifies social inequality by changing the problem itself into something unrecognizable (Morozov, 2013).

Employers often make decisions with incomplete information, since they cannot fully know in advance how well a candidate will perform, develop, or interact within a workplace (Köchling et al., 2020). Many of the qualities recruitment is meant to assess, such as judgement, potential, and “fit,” are interpretive as noted by Nichols (2025). This ambiguity is not necessarily a defect in hiring, but an inherent and necessary feature of human judgement. Often times, unstructured methods in hiring are known to provide employers with insight on personality traits and interpersonal skills, which are harder to access through standardized means (Chauhan, 2019). Algorithmic hiring, however, translates complex social evaluation into standardised and quantifiable indicators, and displaces interpersonal judgement.

Beyond structural inequalities, algorithmic hiring also raises questions about the dignity and psychological experience of candidates subjected to algorithmic hiring systems. Being assessed by a resume screening tool denies candidates the opportunity to contextualise their experiences, respond to misunderstandings, or demonstrate qualities that fall outside the system's evaluative framework (Barattucci, 2025). When candidates receive automated rejections with no explanation (Law, 2026), or perform tasks designed to extract data rather than enable genuine self-presentation, the hiring process becomes extractive rather than relational.

Overall, algorithmic hiring shows how techno-solutionism gains force not because it is ineffective, but because it offers convincing solutions to real organisational pressures of scale, speed, consistency, and bias reduction. As this essay has argued, recruitment is not simply a technical exercise, but a social and political process shaped by contested ideas of merit, fit, and fairness. By reframing these questions as matters of optimisation, standardisation, and prediction, algorithmic hiring does not remove subjectivity or power from recruitment. Instead, it redistributes them into technical systems that are harder to interpret, challenge, and hold accountable. In this sense, AI hiring does not solve the deeper problems of recruitment, but exemplifies how techno-solutionism can obscure them while appearing to fix them.

References

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