Hack Five
Hack Five

Open Source AI Hackathon 2024

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Papireddy Eppala

@papireddy01

pranav krishna

pranav krishna

@ipranav_t13

AI-Powered ATS Screening

Submitted Jan 2, 2024

Category: AI tools/SDK innovation

Track : AI in education

AI-Powered ATS Screening is an AI-driven system designed to streamline the initial stages of recruitment. By leveraging natural language processing and matching algorithms, the system intelligently analyzes resumes and job descriptions, providing recruiters with a curated selection of candidates that closely align with the job requirements.

Accurately extracts key information from resumes, enabling seamless analysis.

Applies advanced natural language processing to understand the semantic context of both resumes and job descriptions.

Implements a dynamic threshold to efficiently filter and present the most relevant candidates.

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  • A

    Akshobhya

    @akshobhya_j Editor & Promoter

    @papireddy01, thank you for your proposal submission to The Fifth Elephant Open Source AI Hackathon. This submission needs to be updated based on the following considerations.

    Concept and Innovation

    The concept of using AI to streamline the initial stages of recruitment is not entirely new, but the proposal's emphasis on leveraging natural language processing and matching algorithms to curate a selection of candidates is promising. Consider providing more details on how the system will ensure fairness and avoid bias in candidate selection.

    Open Source Alignment

    It would be beneficial to consider how the code and algorithms used in the system can be made open source to ensure transparency and foster community collaboration in advancing ATS screening technology.

    Technical Feasibility and Roadmap

    The technical feasibility of the proposal is evident, given the advancements in natural language processing. However, the roadmap lacks specific milestones and deliverables. Consider providing a more detailed plan of action, including the implementation of the dynamic threshold and how it will be calibrated.

    Datasets and LLM Usage

    More information is needed on the datasets used for training and testing, as well as the approach to leveraging Large Language Models (LLMs) in the system. Detailed information on data privacy and security measures is also important to address.

    Action Items and README.md

    The proposed action items lack specificity, particularly in detailing the implementation of the dynamic threshold and the integration of the system with existing ATS platforms. Consider providing a more detailed README.md with comprehensive documentation on the system's architecture and usage.

    Potential Challenges and Mitigation Strategies

    Potential challenges related to bias in AI-based candidate selection and scalability of the system should be addressed. It's important to outline effective mitigation strategies for these challenges.

    Mentorship and Support

    Consider seeking mentorship or guidance from experts in HR tech or ethical AI to enhance the project's alignment with industry best practices.

    Provide the GitHub repository link in your proposal for easy access and review by mentors and the jury.

    Ensure that the GitHub repository contains a comprehensive README.md documenting the project's purpose, technical details, setup instructions, and example use cases.

    Utilize the available platforms such as The Fifth Elephant WhatsApp group to engage with mentors and seek guidance on technical and implementation aspects of your project.

    Feedback

    The proposal shows potential with its application of advanced NLP for ATS screening. However, more comprehensive details on technical implementation, fairness and bias considerations, and collaboration with the HR domain are needed to strengthen the proposal.

    The focus on leveraging NLP for streamlined recruitment is commendable. With additional detail and consideration of fairness and bias, this project has the potential to make a positive impact in AI-powered recruitment processes.

    Posted 1 year ago

  • Simrat Hanspal

    @simrathanspal Editor

    Hello Papireddy, nice idea. Thanks for submitting.
    Have you started building yet? What is your open-source tech stack?

    Two key challenges for mapping Job description with resume, in my mind -

    1. Retrieving relevant information from different sections of the resume
    2. Aggregating data points in resume to match values in job description, eg: total years of experience or x years of experience in managing projects

    For the first one, I feel paper like PDFTriage (https://arxiv.org/abs/2309.08872) is a good inspiration. The paper shows that sharing the structure of the document and then providing only relevant sections as context to the LLM gives better results than treating the doc as flat text file and chunking.

    For the second one, I feel this 2 step process, fetch relevant information and then aggregating would work well.

    Looking forward to hear how you are thinking of solving this problem.
    Please feel free to ping me. You can find my number on HasGeek whatsapp / telegram group.

    Posted 1 year ago (edited 1 year ago)
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