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Research & Studies· 3 min read

What Makes a Recruitment Process Feel Fair to Candidates

A rejected candidate judges more than the outcome — they judge how the decision was made. Gilliland's (1993) organizational justice model explains why, and what it means for designing AI-assisted recruitment.

What Makes a Recruitment Process Feel Fair to Candidates
#Organizational Justice#Procedural Justice#Candidate Experience#Recruitment Bias#Transparency#Work Psychology

A rejected candidate can accept being turned down. What's harder to accept is not understanding why. Organizational psychology research settled this distinction long ago.

Two dimensions of perceived fairness

In 1993, Stephen Gilliland published a model in the *Academy of Management Review* that has become a reference point in the field, applying organizational justice theory to personnel selection systems (Gilliland, 1993, *Academy of Management Review*, 18(4), 694-734). His central contribution: a candidate's perception of fairness doesn't depend only on the outcome — whether they were hired or not — but also, and often mainly, on how the process was conducted.

Gilliland distinguishes two dimensions. Distributive justice concerns the outcome itself: does the candidate feel they received the decision they deserved. Procedural justice concerns how the process unfolded, and Gilliland breaks it down into ten rules — among them the perceived link between the questions asked and the position sought, the opportunity given to candidates to demonstrate their abilities, consistency of treatment across candidates, and the possibility of obtaining an explanation for the decision reached.

What later research confirms

This body of work, widely cited since — including in the most recent literature reviews on candidate experience — establishes that adherence to these procedural rules directly shapes candidates' overall perception of process fairness, independent of the final outcome.

A difference in experience, not just perception

This model highlights a point that's often underestimated: a rejected candidate who understands the objective, consistent criteria their application was evaluated against will come away with a very different impression than one who received no explanation, or who perceived the evaluation as arbitrary. This effect isn't only about the employer's reputation — it touches the trust the candidate keeps, or doesn't, with the process as a whole.

Two of Gilliland's rules apply directly to designing an AI-assisted recruitment process: consistency of the criteria applied from one candidate to another for the same role, and the ability to give candidates a clear, legible account of the criteria that shaped a screening decision. These are requirements of method, not messaging — a process that applies the same criteria but can't clearly explain them to the candidate only half-satisfies what research identifies as the key driver of perceived fairness.

Clarentia's position

At Clarentia, consistency in how candidates are evaluated against one another, and the recruiter's ability to trace and explain a screening decision, are design principles — not a feature bolted on to answer a transparency request after the fact.

Source cited

Gilliland, S. W. (1993). The perceived fairness of selection systems: An organizational justice perspective. *Academy of Management Review*, 18(4), 694-734.