Soc196_W6_Privacy, Surveillance, and Ethical Challenges2_Recap

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Course Information

  • Big Data & Society: Privacy, Surveillance, and Ethical Challenges

  • Soc 196 - Week 6

  • Instructor: Fabien Accominotti

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Roadmap for Today

  1. Discussion on implications of informational risk and ethics of big data research.

  2. Recap of Week 6: Focus on privacy, surveillance, and ethical challenges related to big data.

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Week 6 Recap

  • Review of key concepts: Big Data, privacy, and ethical implications in society.

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Defining Big Data and Its Social Implications

  • Exploring the influence of big data on the social world:

    • Speculative and normative perspectives on the data revolution (e.g., Klosowski reading).

    • Empirical analysis through case studies discussing the impacts of big data (e.g., Brayne, Brensinger readings).

  • Central question: How does big data affect specific social settings, particularly in terms of surveillance and privacy?

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Surveillance and Monitoring

  • Who Uses Digital Data for Surveillance?

    • Governments and Law Enforcement: Primarily for security purposes (Klosowski, Brayne).

    • Firms: To monetize personal data, transforming daily lives into profitable information (Fourcade and Healy).

    • Social Scientists: For academic research (Salganik).

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Personal Surveillance and the Virtual Panopticon

  • Role of the Individual: Each person contributes to the surveillance culture via online activity (Rayner).

  • Foucault's Theory: Knowledge as power, leading to self-regulation in behavior to fit societal standards.

  • Implication: Non-anonymous traces of online behavior may reflect social desirability bias.

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Theoretical Analysis of Facial Recognition

  • Pros of Facial Recognition:

    • Enhanced security and public safety (improved policing effectiveness).

    • Convenience in daily life (e.g., keyless access).

  • Cons of Facial Recognition:

    • Privacy invasion and Fourth Amendment violations.

    • Potential for wrongful identifications.

    • Algorithmic bias—less effective recognition of racial minorities (James Rule's observations).

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Empirical Insights on Big Data in Law Enforcement

  • Brayne Reading Insights:

    • Not all changes due to big data are uniform.

      • Some practices are amplified (reactive to predictive policing).

      • Some practices transform social norms entirely (collection of data on offenders and civilians).

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Population Disparities in Data Impact

  • Big Insight 2: Big Data affects different demographic groups unequally, especially racialized minorities.

  • Collection methods amplify existing issues, potentially expanding police databases indiscriminately.

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Data Breaches and Economic Insecurity

  • Brensinger Reading Insights: Informational risk has amplified in the digital age:

    • Direct consequences of data breaches (e.g., material hardships).

    • Psychological effects on trust both personally and systemically.

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Consequences of Informational Risk Across Demographics

  • Big Insight 2: Consequences of data breaches differ by demographic:

    • Low-income and minorities face more personal trust issues.

    • Coping strategies vary significantly by economic status, potentially worsening inequalities.

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Ethical Challenges in Big Data Research

  • Researcher's Ethical Responsibility: Awareness of privacy infringement through unconsented observation or experimentation.

    • Ethical concerns arise from infamous studies without informed consent (e.g., emotional contagion studies).

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Recommendations for Ethical Research Practices

  • Four Essential Principles:

    • Respect for Persons: Prioritize informed consent when possible.

    • Beneficence: Minimize risks and enhance potential benefits of research.

    • Justice: Equitably distribute risks and benefits among affected groups.

    • Respect for Law and Public Interest: Adherence to legal and ethical standards in research.