Running a Scoring Pilot allows users to test and refine HrFlow.ai's latest AI Scoring model on a subset of data before full deployment.
The main goals of running a scoring pilot are:
Evaluating the scoring model performance on real data
Identifying any data issues or gaps
Laying the groundwork for broader deployment
This guide covers best practices for customers looking to implement AI-powered recruiting.
STEP 1: Confidentiality agreement
Before undertaking an AI scoring pilot, both parties sign a mutual non-disclosure agreement (MNDA) to protect data confidentiality. This MNDA should clearly outline the types of shared data, the purposes for which it can be used, and measures to prevent unauthorized access or leaks.
Any sensitive personnel data or profiles provided for testing should be properly anonymized and handled according to applicable regulations. Responsible data sharing and governance practices are key to developing ethical AI tools.
STEP 2: Preparing test data
To run our model effectively, data is the linchpin. The data required encompasses:
Profiles: These can be raw files (PDF, PNG, JPEG, etc.) or structured objects such as JSON and YAML, each profile having a unique ID.
Jobs: Each job listing has a unique identifier and includes a detailed description.
Trackings (Applications History): It contains crucial data points, including profile and job IDs and the status of each application.
To proceed, we would need a sample of:
10 job offers (title + description + URL)
You need to fill the Excel Spreadsheet with :Title: job title
Description: context of the mission + description of the tasks
URL: link to the job published
To get the Excel file, contact sales@hrflow.ai
Up to 100 CVs/job
You can export your applicants in PDF format then add them into a .zip fileA good organization is 1 folder/job that contains all its applicants
Recognizing that data distribution varies from client to client, we understand the necessity of a tailored approach, so we've adopted a multistage testing process:
We have produced a foundational general model easily adaptable to retraining using client-specific data. This approach ensures our AI stays in sync with the ever-evolving data distribution.
Two fundamental points should be taken into consideration when preparing your test data:
The data should be selected randomly. If any selection bias is applied during this selection process, the model's performance will be reduced.
The tracking data should not be very skewed (for example having a 5% or less positive matching rate between profiles and jobs). If this is the case, we highly recommend sending more data to have a good enough representation of each application status.
STEP 3: Sharing test data
Clients have two convenient methods for sharing their data with us:
Method 1 - Upload Notebook
Use our APIs directly to upload data using the following resources:
Parsing Notebook (if using raw data): This Notebook helps you parse your raw data and turn it into structured objects.
Upload Notebook: This Notebook helps you upload your structured data
However, this first approach is usually slower and error-prone. This is why we recommend following the second method.
Method 2 - Email a secured Zip file
Send a password-locked zip file to the following email address: support+[Custommer_Subdomain_name]@hrflow.ai. This is our recommended method for its speed and reliability.
Embracing diverse data formats and an adaptable training process allows us to tailor our solutions to the unique needs of each client.