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Most faking research has examined the use of personality measures when using top-down selection. We used simulation to examine the use of personality measures in selection systems using cut scores and outlined a number of issues unique to these situations. In particular, we compared the use of 2 methods of setting cut scores on personality measures: applicant-data-derived (ADD) and nonapplicant-data-derived (NADD) cut-score strategies. We demonstrated that the ADD strategy maximized mean performance resulting from the selection system in the face of applicant faking but that this strategy also resulted in the displacement of deserving applicants by fakers (which has fairness implications). On the other hand, the NADD strategy minimized displacement of deserving applicants but at the cost of some mean performance. Therefore, the use of the ADD versus NADD strategies can be viewed as a strategic decision to be made by the organization, as there is a tradeoff between the 2 strategies in effects on performance versus fairness to applicants. We quantitatively outlined these tradeoffs at various selection ratios, levels of validity, and amounts of faking in the applicant pool.