HiredScore’s Focus on Compliance and Bias Mitigation
Compliance has been core to our business model since 2016. Whether required by laws or not, building compliant by design, ethical AI has been part of our mission statement and is a part of everything we build. We take compliance very seriously, and regularly work with outside experts in AI compliance, employment law, and ethical AI to ensure adherence to best practices and the highest standards.
Overview of HiredScore AI Functionality
- Spotlight is HiredScore’s proprietary and bias-mitigating AI that matches information submitted by candidates to job requisitions.
- Fetch identifies potential matches for open jobs with people who have applied for other jobs and have consented to being considered for jobs at the company.
- HiredScore parses information contained in job requisitions and resumes and matches information on candidates and job requirements, reflecting who meets the employer-defined qualifications for the role against the candidate-stated job experience and relevance, taking the candidate data submitted by the candidate at face value.
- HiredScore does not write job requisitions, nor does it edit job requisitions in any way. If a job requisition itself is biased, HiredScore has no editing abilities that would supersede the employer-defined criteria for the role. HiredScore never adds requirements or ignores requirements in job requisitions.
- HiredScore only takes candidate data that is submitted by the candidate at face value. HiredScore does not use social media or any other data, nor does HiredScore evaluate or assess a candidate beyond the data they submit when applying for the employer’s job.
HiredScore’s AI Matching: How It Works
HiredScore’s fully explainable AI reports how much of a match candidates are with the job requirements using a simple A, B, C, D system, where A denotes the highest match of the candidate’s qualifications with the employer defined job criteria. Put another way, this is like a “smart mirror” reflecting back the candidates who most closely fit the job requirements set by the employer. The A,B,C,D scores are never “curved.” This means that if a job requisition has commonly held requirements, there may be many more As and Bs than Cs and Ds. Conversely, if a job requisition has very uncommonly held requirements, there may be many more Cs and Ds than As and Bs and each candidate will receive a score of how they align to the requirements that is independent of the pool or the other candidates’ alignment or misalignment.
Just as it is true that meeting job requisition requirements is not the end of the inquiry for employers selecting candidates for employment, so too, this is the case with HiredScore matching scores.
The model’s output, the A,B,C,D designation of smart-mirror reflections does not supplant human judgment. It merely helps recruiters and hiring managers expediently identify which applicants have stated that they meet which requirements of the jobs to which they apply. Recruiters and hiring managers always have complete discretion on which applicants they select, and what information they weight or consider. HiredScore does not influence any decision making. It just indicates which candidates match the stated job requirements and to what extent.
NYC Local Law 144 Context and Definitions
In 2021, the New York City Council proposed Local Law 144. It regulates how automated employment decision tools or “AEDTs” either make job decisions or influence humans to make job decisions. In April 2023, the New York City Department of Consumer and Work Protection (DCWP) set forth guidelines for understanding this rule. Local Law 144 goes into effect as of July 5, 2023.
Is HiredScore an AEDT?
AEDT stands for “automated employment decision tool.” AEDTs are defined differently by different laws and regulations. Whether HiredScore’s applications meet the definition of an AEDT or not, HiredScore is deeply committed to building and deploying unbiased and ethical AI. Under the current definition of the NYC local law 144, HiredScore does not believe that its AI applications meet the definition of an AEDT, and therefore the laws’ requirements do not apply to Spotlight or Fetch. Nonetheless, HiredScore routinely conducts bias audits of its systems and is proud to be transparent about this ongoing work.
Quick legal disclaimer: Nothing contained herein is in any way an admission or concession about HiredScore’s applications’ legal status or whether NYC Local Law 144 or any other law or regulation applies to HiredScore’s technology in whole or in part.
New York City Local Law 144
Definitions from The NYC Bias Audit Law (LL 144) defines the automated employment decision systems (AEDTs) that are in scope of the legislation and what a bias audit is.
- Automated employment decision system - any computational process, derived from machine learning, statistical modeling, data analytics, or artificial intelligence, that issues simplified output, including a score, classification, or recommendation, that is used to substantially assist or replace discretionary decision making for making employment decisions that impact natural persons.
- Machine learning, statistical modeling, data analytics, or artificial intelligence - a group of computer-based mathematical, computer-based techniques that generate a prediction of a candidate’s fit or likelihood of success or classification based on skills/aptitude. The inputs, predictor importance, and parameters of the model are identified by a computer to improve model accuracy or performance and are refined through cross-validation or by using a train/test split.
- Simplified output - a prediction or classification that may take the form of a score, tag or categorization, recommendation, or ranking.
- Employment decision - to screen candidates for employment or employees for promotion within the city.
- Bias audit - an impartial evaluation by an independent auditor. Such bias audit shall include but not be limited to the testing of an automated employment decision tool to assess the tool’s disparate impact on persons of any component 1 category (race/ethnicity and sex/gender at minimum).
- Metrics. Under LL 144, bias is assessed using impact ratios to determine whether one group is favored by the system over another. When the system results in a binary outcome (classification), the following metric is used to calculate the impact ratio:
- Adverse impact (4/5th rule): Under the Equal Employment Opportunity Commission’s Uniform guidelines, adverse impact (bias) is said to be occurring when the selection rate of one subgroup is less than four-fifths (80%) of the group with the highest selection rate. As such, this is the threshold used in this audit to determine if a system is biased based on the impact ratios metrics provided above.
- Intersectional impact ratios. In addition to examining bias at the standalone level, where the rates of single subgroups (such as black and white) are compared, impact ratios are also calculated for intersectional groups. Here, impact rations are calculated for groups based on their gender and ethnicity intersectional categorization.
- Data Quality Assurance. Given that the 4/5th rule is most appropriate for larger sample sizes, the data is cleaned to remove groups with small sample sizes before the data is analyzed. This includes removing any missing responses or responses from those who have indicated they would “prefer not to say”. Data cleaning can also involve combining smaller groups into a single “other” category.
- Small Sample Sizes. A small sample is defined here as the group representing less than 5% of the individuals or there being fewer than 3 individuals in this group. If a group with a small sample size has the highest rate or score, it is not used as the denominator for the impact ratio metric and instead use the group with the highest score/rate that has a sufficient sample size. Therefore, the impact ratio for the underrepresented group would be greater than 1.
HiredScore Bias Audit Summary of Results:
HiredScore commissioned an external auditor to conduct a third-party, impartial, audit of HiredScore’s systems in accordance with New York City Local Law Int. No. 1894-A (1894-2020).
Date of Audit: 2 July 2023
Distribution Date: 1 July 2022 - 2 July 2023
Data Collected Description: HiredScore grades showing how much of a match candidates are against the job requirements, as defined by the employer, that they applied to with an A, B, C, D system. For this audit it includes a random sample of 10 Global Large Customers data over the last 12 months of reqs and candidates, covering 147,077 requisitions that were filled between July 2022 to July 2023.
Source of Data Collected: The ATS system of the employers, which includes HiredScore grades for applicants.
Audit Outcome: There is no evidence of disparate impact based on the calculated impact ratios as well as the data analyzed for the standalone and intersectional analysis.
Audit Detailed Results: