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How AI will change the future of mass surveillance

The most worrying aspect of using AI by governments appears in their endeavor to exploit the capabilities of AI to monitor people – unlawfully – on a large scale.

ai surveillance
Nihad A. Hassan
Nihad A. Hassan Contributor
Dec 31, 2023 Updated: 31 December 2023 4 min read

Comparison with traditional spying methods

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AI's role in automating surveillance

Data management

  • Data collection: AI-powered web scrapers can gather online data from various sources without human intervention. In addition, these scrapers can collect unstructured data easily compared to traditional web scrapers tools. For instance, the web contains many unstructured data, such as textual, multimedia, metadata, and raw data, to name a few. Textual data include social media posts and forum threads, such as Reddit. Multimedia content includes images (e.g., visual content including infographics and memes), videos (such as those generated by TikTok and YouTube users), and audio content (e.g., podcasts and audio recordings). Using traditional tools to get information from content that does not conform to a specific data model or is not organized in a predefined manner is no longer applicable. Natural Language Processing (NLP) allows AI tools to adapt quickly to collect unstructured data without needing a predefined template for each data model on which to base their collection work.
  • Data mapping: After gathering the data, we must find patterns and entity relationships. AI tools and Machine Learning (ML) algorithms can do a great job in mapping data automatically from sources to their target destination. For instance, by using ML algorithms, we can discover relationships between a specific organization and its digital interactions with other entities, whether people or organizations, without needing to discover these relationships manually, which can take a tremendous amount of time.
  • Data quality: Collecting and mapping data is not the final phase when gathering and analyzing a mass volume of data. For instance, we still need to discover any errors or inconsistencies in the collected datasets before using them. AI tools can help in this direction by discovering anomalies in datasets and filling missing data with estimated values without sacrificing their accuracy.

Speech analysis

  • AI can identify individuals worldwide by inspecting their unique vocal characteristics. For example, this allows sifting across mass volumes of audio recordings to find all those related to a specific person.
  • Natural Language Processing (NLP) algorithms can be adapted to extract specific keywords from voice conversations. For instance, finding particular keywords can trigger an action to shift the conversation to be analyzed by a human agent.
  • AI can understand the emotional states of the people involved in the voice communication (e.g., stress, anger, excitement, or other emotional states).
  • AI tools can create a text transcript of intercepted voice communications automatically. This allows Intelligence services to index tapped voice communications and store them for later analysis.
  • The automatic translation of foreign languages is a significant advantage of using AI tools in mass surveillance operations. For instance, AI can automatically translate intercepted digital communications from any spoken language and save them as text or voice recordings.

Simplify tracking objects in public places

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