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SBIR Phase II: Social Platform with Machine Learning Moderation

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2051354
Agency Tracking Number: 2051354
Amount: $999,999.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: IT
Solicitation Number: N/A
Timeline
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-05-15
Award End Date (Contract End Date): 2023-04-30
Small Business Information
220 Collingwood St Ste 140
Ann Arbor, MI 48103
United States
DUNS: 080182956
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Jill Dimond
 (206) 799-1190
 jill@sassafras.coop
Business Contact
 Jill Dimond
Phone: (206) 799-1190
Email: jill@sassafras.coop
Research Institution
N/A
Abstract

This SBIR Phase II project aims to address the problem of abuse and harassment on social network sites by creating a new ad-free, anti-abuse social network platform. Online abuse and harassment are rampant on existing social networks sites, and is especially severe for women, people of color, and the LGBTQ community. However, technical and design approaches that could curb such abuse cannot be realistically implemented in advertisement-based business models, as such anti-abuse approaches often limit advertisement impressions. The new social platform has innovative anti-abuse technologies and a novel business model with no advertisements. In this SBIR Phase II project, the innovation uses novel deep learning techniques to provide a new and innovative hybrid human-machine moderation system. This moderation system is able to learn from moderation decisions and is grounded in the values of the online community. In addition, the innovation uses applied theoretical concepts of consent to provide groundbreaking design in how content is displayed and standards for how users interact. The goal and scope of this research is to improve upon the algorithmic efficiency, develop more moderation models, refine the moderation API, develop user-driven retraining of the models, conduct co-design with target end users, and develop the front-end of the product. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

* Information listed above is at the time of submission. *

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