Anonymous Harassment Reporting App with Advisor Matching
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Solution Overview
Problem
Academic harassment is a prevalent issue in research and educational institutions, with less than 2% of targets reporting due to fear of retaliation, job loss, and mobbing, largely due to the lack of robust and comprehensive data on harassment types and behaviors, and existing reporting systems are often insecure and require target identity disclosure.
Innovation Solution
A portable electronic device and method that assesses and reports educational harassment, connecting targets with advisors through a secure, multi-platform app that collects and analyzes data in a discipline-specific manner, allowing targets to remain anonymous and providing tailored educational programs for institutions, while connecting them with support systems like counselors and survivors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing reporting systems require target identity disclosure, then institutional accountability can be established, but target security and willingness to report decrease
Solution Approach 1:
The system segments the reporting process into multiple stages: anonymous incident submission, separate advisor matching, and phased information disclosure. This allows institutional accountability to be established through tracked reporting channels while maintaining target anonymity throughout the process, resolving the contradiction between accountability and security.
Solution Approach 2:
The system introduces an intermediary matching mechanism that connects targets with advisors without requiring direct identity disclosure. The intermediary platform facilitates accountability tracking and support connection while protecting target identity, thereby maintaining both institutional accountability and target security.
2Loss of information
If comprehensive data collection on harassment types is implemented, then understanding of harassment behaviors improves, but system complexity and data management burden increase
Solution Approach 1:
The data collection system is segmented into discipline-specific categories and harassment type classifications. This structured segmentation enables comprehensive data collection across multiple dimensions while organizing information in manageable segments that reduce the burden on the overall data management system.
Solution Approach 2:
The system changes parameters by collecting data in standardized, discipline-specific formats with predefined categories. This parameter standardization allows comprehensive harassment data to be collected and managed efficiently, transforming unstructured comprehensive data into organized, analyzable information that reduces management complexity.
3Object-affected harmful factors
If anonymous reporting is enabled, then target security improves and reporting increases, but data reliability and traceability decrease
Solution Approach 1:
The system performs preliminary actions by establishing secure anonymous reporting channels and pre-configured advisor matching protocols before incidents occur. This preliminary setup ensures target security is maintained from the outset while traceability is preserved through pre-established tracking mechanisms that don't require target identity disclosure.
Solution Approach 2:
The system implements feedback loops where anonymous reports are tracked through the processing pipeline, allowing the system to monitor data flow and reliability metrics without exposing target identities. This feedback mechanism maintains data traceability for quality control while preserving target anonymity and security.
Data Source
AI summary
A portable electronic device is programmed to perform a method for assessing educational, or other, harassment, including, at least, including determining whether educational harassment has occurred. If educational harassment has occurred, connect a target of the educational harassment with a connection, such as an advisor. The method includes (i) storing data on types of educational harassment reported through an application and assessing the stored data, including storing data in discipline specific categories, (ii) determining whether the target has accepted the advisor, if the target has accepted the advisor, and (iii) connecting the target and the advisor through a chat system. The method may also include creating tailored educational programs based on the educational harassment reported through the application for delivery to specific institutions experiencing that educational harassment.