Candidate-Relevancy-FAQ.pdf

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Last Updated: December 15, 2023
Candidate Relevancy FAQ’s
Employers who post jobs on ADP’s recruiting platforms may refer to an applicant’s Candidate
Relevancy or Profile Relevance score. Candidate Relevancy and Profile Relevance rely on artificial
intelligence and machine learning to provide an initial comparison of an applicant’s education,
experience, and skills against the education, experience, and skills requirements in the job
description. This is intended to be one of many factors that a potential employer will review in
making its interview decisions; there are no cut-off scores and all applications remain visible to
employers. Candidates who opt out will have their score listed as “Not available.”
These FAQs provide additional information about the data these tools collect, store, and retain,
and the results of the most recent impartial evaluations of these tools.
1. What is Candidate Relevancy?
ADP’s Candidate Relevancy and Profile Relevance tools (for ease of reference both will jointly be
referred to as “Candidate Relevancy” unless otherwise noted) use artificial intelligence and
machine learning algorithms to conduct an initial review of an application, and are designed to be
utilized by employers as one tool, among others, in the hiring process.
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Specifically, Candidate
Relevancy conducts a mathematical assessment of how close the skills, education and/or
experience on an applicant’s resume match the skills, education, and/or experience listed on the
relevant job description. This process quantifies the “relevance” between the applicant’s resume
and the job posting. The Candidate Relevancy model also leverages past decisions derived from
millions of resumes and job descriptions where the selection decision is already known.
The scores are intended to be used as one of many factors by an employer in determining who to
advance to the next round in the hiring process. Candidate Relevancy is not intended to replace
human judgment during any step of the recruitment process and is designed in such a way that
there are no cut-off scores that would eliminate applicants from being visible to employers in the
user interface. Employers are provided access to all applications, enabling them to make human
decisions on which candidates to pursue.
2. How is the Candidate Relevancy score determined?
The Candidate Relevancy model first parses the information concerning the education,
experience, and skills contained in the applicant’s resume or application and in the relevant job
description. This information is formatted to allow a mathematical assessment to be conducted
of how close the applicant’s education, skills, and experience match those found in the relevant
job description. Candidate Relevancy does not extract or utilize the applicant’s name, address,
race, ethnicity, gender or protected demographic information.
The Candidate Relevancy score is displayed to employers using ADP’s Recruitment Management product, while
the Profile Relevance score is displayed to employers using ADP’s WorkforceNow Recruitment platform.
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Each job requisition is classified using a job and sector taxonomy. The Candidate Relevancy
model creates three sub-scores indicating how close the applicant’s education, skills, and
experience matches those found in the job description. The three scores are then weighted to
create the Candidate Relevancy Score. The weights sum to 1 and reflect the relative importance
of each component. Since the job descriptions do not define the importance of each component,
the importance (i.e., the weights) must be estimated empirically from the data. Separate
weights are created for each sector in which the open job resides. The weights are determined
by a machine learning model.
The resulting weighted score (the final Candidate Relevancy score) is intended to be used by an
employer as only one tool, among others, to aid in the selection of whom to interview or prioritize
during the hiring pipeline.
3. What data does Candidate Relevancy collect and what are ADP’s retention policies
regarding the information?
Type of Data
Resume data
Job descriptions
Collected from
ADP WorkforceNow Recruitment or ADP
Recruitment Management
ADP WorkforceNow Recruitment or
ADP Recruitment Management
Retention Policy
Three years
Three years
4. Is Candidate Relevancy an automated employment decision tool covered by New York
City Local Law 144 (“the NYC Ordinance”)? 
The NYC Ordinance covers automated screening or selection tools that provide “output”—such
as scores, classifications, or recommendations—to an employer, and which are used to
significantly assist or substitute a human’s decision-making process. Under the NYC Ordinance,
to substantially assist or substitute a human’s decision-making process means: (1) to rely solely
on a simplified output without consideration to other factors; (2) to use a simplified output as a
consideration in a list of criteria but weight the output more heavily than other criteria the set;
or (3) to use the output to overrule human decision-making conclusions.
Candidate Relevancy is not intended by ADP to be relied upon solely by employers in making
employment decisions and is not meant to substantially assist or replace discretionary decision
making in employment decisions. Moreover, Candidate Relevancy is not intended to be used as a
criterion that is weighted more than any other criterion in making employment decisions and is
not intended to be used to overrule conclusions derived from other factors, including human
decision-making.
Candidate Relevancy is intended to be one source of assistance in helping to prioritize candidates
selected for next steps. Education, skills, and experience must be evaluated and validated by
employers through person-to-person interviews and background checks, among other
things. Candidate Relevancy is not intended to replace human judgment during any step of the
recruitment process and is designed in such a way that there are no cut-off scores that would
eliminate candidates from being visible to employers in the user interface. Employers are thereby
provided access to all candidates, enabling them to make human decisions on which candidates to
pursue.
If Candidate Relevancy is used as intended by ADP, ADP does not believe Candidate Relevancy to
be an automated employment decision tool as defined by the New York City Ordinance and its
related final rules.
Nothing herein is intended to be a legal opinion and does not constitute legal advice. You should
consult with an attorney before taking any action in reliance on the information provided herein
including whether Candidate Relevancy is an automated employment decision tool.
5. Did ADP conduct a bias audit on Candidate Relevancy?
Yes. At ADP integrity is everything and is at the foundation of how we design and develop our
solutions and services. Although ADP believes that Candidate Relevancy, if used as intended by
ADP, does not fall within the scope of the NYC Ordinance, ADP is committed to ensuring that
transparency and accountability is embedded in ADP’s offerings.
ADP obtained an independent bias audit of Candidate Relevancy and Profile Relevance from
BLDS, LLC, an independent auditor, in April of 2023. The independent auditors concluded that no
valid statistical evidence of bias is present in the scoring produced by Candidate Relevancy or
Profile Relevance.
6. What was the result of the bias audit conducted on Candidate Relevancy?
In April of 2023, an independent auditor, BLDS, LLC, performed an impartial evaluation of
Candidate Relevancy. The version of Candidate Relevancy audited became available on October
29, 2022. The independent auditors concluded that no valid statistical evidence of bias is
present.
A summary of the scoring rates and impact ratios
2
based on sex and race/ethnicity and the
intersection of sex and race/ethnicity, and adjusted for Simpson’s Paradox, are set forth in the
following charts:
Sex Categories
Applicants
Scoring Rate
2
Impact Ratio
Consistent with the New York City Ordinance, impact ratio means either (1) the selection rate for a
category divided by the selection rate of the most selected category or (2) the scoring rate for a category
divided by the scoring rate for the highest scoring category.
Female
Male
Unknown Gender
573,856
478,161
397,979
47.3%
46.6%
--
1.000
0.986
--
Race/Ethnicity Categories
Applicants
Scoring Rate
Impact Ratio
Asian
Black or African American
Hispanic or Latino
Two or More Races
White
Unknown Race/Ethnicity
97,576
278,592
196,581
40,542
385,751
439,250
47.0%
46.5%
47.5%
48.9%
49.2%
--
0.956
0.946
0.965
0.994
1.000
--
Intersectional Categories
Applicants
Scoring Rate
Asian
40,617
48.4%
Black or African
166,039
46.7%
American
Female
Hispanic
99,243
46.9%
Two or More Races
22,186
48.8%
White
196,823
48.5%
Asian
51,661
46.1%
Black or African
103,143
45.3%
American
Male
Hispanic
87,864
47.1%
Two or More Races
14,187
47.1%
White
183,819
48.6%
Unknown Intersectionality
447,475
--
Impact Ratio
0.992
0.957
0.962
1.000
0.993
0.945
0.928
0.965
0.966
0.996
--
American Indian or Alaska Natives or the Native Hawaiian or Other Pacific Islanders were not
included in computing the Impact Ratio because both categories had less than 1% of the
population and the New York City Ordinance does not require their inclusion when computing the
Impact Ratio. In the opinion of the independent auditors, the inclusion of such small numbers
would allow the race/ethnicity or intersectional categories of American Indian or Alaska Natives
or Native Hawaiian or Other Pacific Islanders to be the highest selection rate based on a small
number of cases. Allowing such a small sample as the reference group to judge other categories
is questionable as the standard for judging the results of other categories for many jobs/sectors
would be set based on only a handful of cases. The table below reports the data adjusted for
Simpson’s Paradox on the categories that were not used in computing the Impact Ratio.
Populations Less Than 1%
Native American / Alaska Native
Native Hawaiian / Pacific Islander
Female Native American / Alaska Native
Male Native American / Alaska Native
Female Native Hawaiian / Pacific Islander
Male Native Hawaiian / Pacific Islander
Applicants
5,050
4,329
2,662
1,749
2,240
1,706
Scoring Rate
45.4%
48.7%
44.7%
46.4%
50.5%
52.3%
This analysis was conducted across all uses of Candidate Relevancy where sufficient self-ID
information was available. Nothing in these FAQ’s should be taken as a guarantee that a particular
client’s use of Candidate Relevancy will never result in adverse impact or bias.
7. What was the result of the bias audit conducted on Profile Relevance?
An independent bias audit of Profile Relevance was also conducted by BLDS, LLC in April of
2023. The version of Profile Relevance audited became available on January 4, 2023.
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The
independent auditors concluded that no valid statistical evidence of bias is present.
This analysis defined “selection” as candidates placed in the “High” category and in the “High or
Medium” category. A summary of the selection rates and impact ratios based on sex and
race/ethnicity and the intersection of sex and race/ethnicity, and adjusted for Simpson’s Paradox,
are set forth in the following charts:
Sex Categories
Selection Classified as High
Applicants
Selections
Scoring Rate
965,033
403,384
41.8%
753,234
313,345
41.6%
Selection Classified as High or Medium
Applicants
Selections
Scoring Rate
1,016,605
754,321
74.2%
798,979
589,647
73.8%
3,321,033
--
--
Female
Male
Impact Ratio
1.000
0.996
Female
Male
Unknown Sex
Impact Ratio
1.000
0.994
--
3
Candidate Relevancy and Profile Relevance rely on the same algorithm to produce a numerical relevancy score (1
to 100). Candidate Relevancy displays the numerical score (1 to 100) to recruiters, while Profile Relevance converts
the numerical score into a High, Medium, or Low relevancy category. Because the interface is different at this time,
ADP obtained separate independent bias audits for Candidate Relevancy and Profile Relevance.

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