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Team
Me
Key Skills
Research
UI/UX
Prototyping
Duration
14 weeks
Tools
Figma
Runway
Photoshop
After Effects
LUMIS
A Crowdsourced Image-data Collection Platform for AI/ML Training
A platform where subject experts upload and validate images, building a fairer and more transparent AI/ML image dataset in an open, iterative environment.
Problems
Bias in AI/ML training datasets & lack of transparency in data curation process. As AI becomes more integrated into our everyday lives, it should be a resource accessible to all, ensuring that everyone has the opportunity to validate and have access to datasets. This approach fosters a more transparent AI ecosystem, not confined to a select group of people or companies.
Problem Discovery
I trained a model on Runway using my selfies to generate AI photos, but unlike the tutorial's accurate results, mine didn’t resemble me. This led me to question if model performance varies across different racial groups.
Desk Research
Through research, I discovered 2 significant problems:
- In the current AI industry, companies often depend on a certain group of individuals for data labeling such as people in Africa & Philippines for low wages.
- Secondly, a lot of the current AI tools are trained on a datasets called “ImageNet”, which has many inappropriate labels.
Datasets
This platform initially utilizes existing image datasets, such as 'ImageNet' as a base. Through the feedback loop of uploading images and validating them, it evolves and improves over time.
Business Model
This platform aims to sell the datasets to companies for AI/ML training. The strength lies in offering diverse aspect, which enhances AI performance by reducing bias.
Home Page Design
This UI presents images in a floating arrangement, implying that they are randomly displayed based on users’ expertise. Users can create filter chips for ‘Classes’ and ‘Labels’ to display specific images.
Diverse Dataset Expansion
In this community-driven platform, contributors upload images to expand underrepresented data within various categories. This approach ensures those familiar with specific categories guide dataset growth.
LUMIS ensures accurate and culturally nuanced image categorization by involving diverse global experts in the class and label validation process.
Democratizing Data
Curation Process
Other projects
To increase users’ engagement and provide additional incentives, I gamified the experience of validating classes and labels.
Gamified Experience
Typography
I chose the typeface, ‘Sora’ for its blend of neutrality and uniqueness. Its distinct yet approachable style effectively represents the variety and inclusivity.
Color
I chose warm tone colors to give the datasets a more human and warm feel. Consistently using this color scheme throughout the platform underscores our collective effort and strengthens the sense of community.
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