Data Source Attribution System for Consumer Profile Transparency
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Solution Overview
Problem
Individuals lack control over and access to their collected personal data, leading to inaccuracies, privacy concerns, and imbalances in power with data collectors, with existing systems failing to provide transparency and effective means for consumers to correct errors or manage their information.
Innovation Solution
A Data Source Attribution (DSA) system utilizing a unique identifier to aggregate and display user profile information from various sources, allowing users to verify accuracy, manage privacy, and correct inaccuracies by identifying the source of the data, thereby providing transparency and control over their online information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data collectors aggregate consumer information from multiple sources, then the quantity and completeness of data increases, but consumer control and transparency over their information decreases
Solution Approach 1:
The system provides feedback to consumers by displaying their aggregated profile information and the sources from which it was collected. This transparency allows consumers to see what data exists about them and take corrective actions, creating a feedback loop that balances data aggregation with consumer control.
Solution Approach 2:
The system acts as an intermediary between data collectors and consumers. It aggregates data from multiple sources on behalf of consumers and presents it in a unified interface, mediating the relationship between the need for comprehensive data collection and consumer rights to access and control their information.
2Productivity
If data is collected without consumer consent or knowledge, then data collection efficiency increases, but accuracy and reliability of the data decreases
Solution Approach 1:
The system performs preliminary aggregation of data from multiple sources before presenting it to the consumer. By collecting and organizing data in advance, it enables consumers to review and correct inaccuracies proactively, rather than discovering errors later when it may be too late to correct them.
Solution Approach 2:
By displaying aggregated profile information to consumers, the system creates a feedback mechanism that allows consumers to identify and correct inaccuracies. This feedback loop improves data reliability by enabling consumers to verify and correct their information.
3Loss of information
If consumers are provided access to their aggregated profile information, then transparency and consumer control improve, but the complexity of the system increases
Solution Approach 1:
The system merges data from multiple disparate sources into a single unified consumer profile. By combining information about what data exists, where it came from, and what consumers can do about it, into one interface, it reduces the complexity consumers would otherwise face by having to check multiple sources separately.
4Adaptability or versatility
If data entities maintain control over consumer information without disclosure, then their operational flexibility increases, but consumer trust and system credibility decrease
Solution Approach 1:
The system introduces feedback by displaying aggregated consumer profile information and data sources to consumers. This transparency creates accountability for data entities while maintaining the flexibility to continue data collection practices, as consumers can see and correct inaccuracies rather than prohibiting data collection entirely.
Data Source
AI summary
A data attribution system uses a unique identifier (UID) that uniquely identifies a particular individual. A search is conducted of different data sources and, different types of profile information associated with the UID is extracted from the data sources. The different types of profile information associated with the same UID is aggregated together and displayed in a same screen presentation on a user interface.