Update participant and project details in submission - #163
Open
Sak-patil wants to merge 1 commit into
Open
Conversation
Updated participant and project details for BiasScan project, including team information, project description, tech stack, and links.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Updated participant and project details for BiasScan project, including team information, project description, tech stack, and links.
Open Source Hackathon 2026 Project Submission
Participant Details
Full Name:
Sakshi Sanjay Patil
GitHub Username:
Sak-patil
Team Name:
Bit
College/University:
Vivekanand Education Society's Institute of Technology, Chembur, Mumbai
Project Details
Project Title:
BiasScan-AI Bias Detection, Explanation & Mitigation System
Project Description:
AI systems today make life-changing decisions — who gets hired, who gets a loan, who gets medical care. But these systems learn from historical data that contains decades of human bias, and they repeat those same discriminatory patterns at massive scale with nobody checking. BiasScan is an automated 8-phase pipeline with an interactive dashboard that detects hidden bias in any AI/ML model, explains why the bias exists in plain language anyone can understand, and fixes it automatically with minimal accuracy loss. It checks for proxy discrimination (where features like zip code secretly encode race or caste), intersectional blind spots (where bias only appears in specific combinations like "older Black women"), and even audits whether the training labels themselves are corrupted by historical discrimination. The dashboard is built so that non-technical stakeholders — HR managers, loan officers, compliance teams, regulators — can understand every finding without a data science background, with clear "What does this mean?" explanations and "What should you do next?" action steps alongside every metric.
Tech Stack Used:
Python, Streamlit, Plotly, Pandas, NumPy, SciPy, Scikit-learn, Fairlearn, SHAP, Imbalanced-learn (SMOTE), Missingno, Prince (MCA), Evidently, PyYAML, SQLAlchemy
GitHub Repository Link:
https://github.com/Sak-patil/Biasness-in-ai-detection.git
Live Demo Link:
https://biasness-in-ai-detection-j2j6v2fdmddhgat3mtwwsf.streamlit.app/
Presentation / Demo Video Link:
https://drive.google.com/file/d/1gBfLwsws52BKGl8kYFbTmuDkkuBXxUaL/view?usp=sharing
Open Source Readiness
Memori Labs Sponsor Task
Please complete these before submitting:
I have starred the Memori Labs GitHub repository
https://github.com/MemoriLabs/Memori
I have followed Memori Labs on LinkedIn
https://www.linkedin.com/company/memorilabs/
I have followed Memori Labs on X
https://x.com/memorilab
I have checked Memori Labs social links
https://linktr.ee/memorilabs
ID Card Verification