AI & Human Values: Surveillance and Prediction Lecture 8

A data analytics company founded in 2003 by Peter Thiel and Alex Karp, known for its work with government agencies and its focus on data analysis for security purposes.

The process of extracting meaningful patterns and insights from large datasets. It involves analyzing vast amounts of information to identify trends, patterns, and anomalies.

Collaborative efforts between government agencies and private companies, where both parties contribute resources and expertise to achieve a common goal.

The heightened focus on national security and intelligence gathering in the United States following the September 11, 2001 attacks.

Utilizing data analysis techniques to identify potential threats, monitor suspicious activities, and improve security measures.

The act of closely observing or monitoring a person or group, often for the purposes of control or security.

An architectural design that allows a single observer to watch over a group of people without them knowing when they are being watched, creating a sense of constant observation. It was envisioned by Jeremy Bentham.

A system where power is structured in a hierarchy, with those at the top having the ability to observe and control those below them.

The process of imposing and maintaining social norms through surveillance and the threat of punishment, shaping individuals' behavior and attitudes.

The collective cultural understanding and assumptions about surveillance, often shaped by literature, film, and media.

Technologies that utilize vision to gather information, monitor, and control, including cameras, facial recognition software, and other visual sensing systems.

The collection and analysis of data about individuals for the purposes of surveillance, often used to track behavior, target advertising, or influence decision-making.

The idea that in the digital age, constant surveillance is possible through data collection and analysis, creating a feeling of being constantly monitored.

Transforming surveillance into digital data. This includes facial recognition, increased data storage, and analyzing metadata to infer information about individuals.

Technologies and social systems that enable surveillance, such as cell phones, internet traffic, and social media platforms.

Collaboration between governments and corporations in surveillance activities, often leveraging global corporate monopolies and infrastructural dependencies.

Surveillance that is widespread, granular, cheap, retrospective, predictive, continuous, and global, using vast amounts of data.

A societal norm where surveillance becomes pervasive, shaping daily life and contributing to self-monitoring and social control.

Individuals voluntarily sharing their data, often through social media, leading to self-monitoring and performance optimization.

Classic models of surveillance emphasizing centralized state control, which are insufficient to capture the reality of modern data-driven surveillance.

The uneven impact of surveillance across different groups, particularly affecting marginalized communities.

A societal environment where surveillance practices are widely accepted and integrated into daily life. This includes both overt and covert monitoring, often involving technology.

The act of individuals willingly participating in the monitoring of their own behavior, often through technology, sharing information about themselves online, or using tracking apps.

AIxWorlds provides a framework for analyzing the complex intersections of Artificial Intelligence, human behavior, and the social and technological structures that shape surveillance culture. It helps us understand how technologies and social practices influence the ways we are monitored and how we monitor ourselves.

Combining datasets from different sources allows for the creation of comprehensive profiles and insights. This can facilitate surveillance efforts by governments, corporations, or even individuals, potentially leading to the identification of patterns and insights.

The course explores how AI and various technologies intersect with human behavior, social structures, and values. It focuses on understanding the implications of these intersections, particularly in the context of surveillance, privacy, and societal impact.