New York City’s Local Law 144 (“Local Law 144”) requires employers using automated employment decision-making tools (“AEDTs”) to engage an independent entity to conduct an annual Bias Audit to calculate Selection Rates and Impact Ratios across certain Enumerated Categories. The categories required by Local Law 144 are: Race / Ethnicity (i.e., Hispanic or Latino, White, Black or African American, Native Hawaiian or Pacific Islander, Asian, Native American or Alaska Native, and Two or More Races); Sex (categorized as male or female); and the intersection of Sex and Race / Ethnicity.
The Selection Rate is defined as the rate at which individuals in each Enumerated Category are either selected to move forward in the hiring process or assigned a classification. The Impact Ratio is defined as the Selection Rate for each Enumerated Category divided by the Selection Rate of the most selected Category.
PricewaterhouseCoopers (“PwC”) reserves all rights that its use of the tool does not qualify as an AEDT under Local Law 144. Nonetheless, PwC engaged an independent entity to perform a Bias Audit and reports the results below. This Bias Audit was completed on May 21, 2026, and PwC will distribute the tool no earlier than June 17, 2026.
The Selection Rates and Impact Ratios calculated below are based on PwC historic data because PwC had not yet deployed the tool at the time of the Bias Audit. PwC application data was provided for the period between June 1, 2025, and April 6, 2026, and contained 95,715 records. The data consists of sex and race / ethnicity information and the outcome indicating whether the application passed. No data on applications for the following race categories was available: “Native Hawaiian or Pacific Islander” and “Native American or Alaska Native.” After removing applications flagged for manual review and incomplete applications, the data was analyzed. Total applications analyzed in Table 1 below vary because some applications listed enumerated categories as “unknown,” and, as a result, were not included in the analysis.
Table 1: Overall Impact Ratios and Selection Rates
| Population | All Applications Count | All Selected Applications Count | Selection Rate | Impact Ratio |
| Sex | ||||
| Male | 53,448 | 41,972 | 78.53% | 1.00 |
| Female | 33,578 | 25,627 | 76.32% | 0.97 |
| Race / Ethnicity | ||||
| Two or More Races | 3,955 | 3,410 | 86.22% | 1.00 |
| White | 24,464 | 21,081 | 86.17% | 1.00 |
| Black or African American | 6,550 | 5,450 | 83.21% | 0.97 |
| Hispanic or Latino | 6,674 | 5,357 | 80.27% | 0.93 |
| Asian | 41,715 | 29,312 | 70.27% | 0.81 |
| Sex and Race / Ethnicity | ||||
| Male, White | 16,204 | 14,038 | 86.63% | 1.00 |
| Male, Two or More Races | 2,520 | 2,178 | 86.43% | 1.00 |
| Female, White | 8,201 | 6,986 | 85.18% | 0.98 |
| Male, Black or African American | 4,108 | 3,452 | 84.03% | 0.97 |
| Female, Black or African American | 2,429 | 1,989 | 81.89% | 0.95 |
| Male, Hispanic or Latino | 4,242 | 3,468 | 81.75% | 0.94 |
| Female, Hispanic or Latino | 2,410 | 1,873 | 77.72% | 0.90 |
| Male, Asian | 24,043 | 16,931 | 70.42% | 0.81 |
| Female, Asian | 17,531 | 12,268 | 69.98% | 0.81 |
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