This essay examines dataveillance, focusing on the state's use of personal data for both service delivery and surveillance. It argues that while some surveillance is justified, governments often misuse data for control and repression, amplified by modern information technology and social media. The paper highlights key dangers, including wrong identification, profiling, and social inequality, using India's Aadhaar system as a case study. Ultimately, it concludes that unchecked dataveillance threatens individual privacy, civil liberties, and democratic principles.
Table of Contents
- Introduction
- Surveillance and Dataveillance
- Dangers of Dataveillance
- Dataveillance Case Study: India
- Social Media and Dataveillance
- Conclusion
- References
Objectives & Key Themes
This essay aims to evaluate various articles and publications concerning state surveillance. It seeks to clarify the concept of dataveillance, explain how information technology drives its development, and examine the state's role in using data for suppression, as well as the obstacles to data-based democratisation.
- Defining surveillance and dataveillance.
- Impact of information technology on dataveillance.
- The state's use of data for repressive purposes.
- Challenges to data-based democratisation.
- Case study of India's Aadhaar system.
- The role of social media as a facilitator of dataveillance.
Excerpt from the Book
Dangers of Dataveillance
Dataveillance poses numerous threats to individual or public members. Dataveillance is an arbitrary action that occurs even in cases where there was no previous suspicion. Dataveillance gives the state powers to interfere with a person's liberties on the assumption that the person has committed, is committing or is planning to commit a crime. While such actions are justified for the sake of national security, such as terrorism or to prevent organised or violent crimes, the problems with dataveillance include wrong identification. According to Arora (2019), the dataveillance techniques can sometimes lead to unclear and inconsistent conclusions as they are context-dependent. In addition, low-quality data can lead to the arrest or punishment of the wrong people. When the data lacks a common identifier, dataveillance can result in multiple type-1 errors (high proportion of spurious matches) and type-2 errors (undetected matches). Low-quality data are outcomes of incomplete data, misspellings, variants and other inaccuracies.
Dataveillance results in profiling (Büchi et al., 2022). Profiling refers to judging a person based on their past behaviour and actions or comparing them with people who have similar profiles. The use of statistical techniques such as discriminant analysis and multivariate correlation in dataveillance results in the creation of multiple profiles of individuals, which law enforcers use in making systemic reasoning. When these profiles are shared with unprofessional or inadequately trained law enforcers, then such a profiling approach becomes a witch-hunting tool. Profiling seeks to provide a greater probability of detecting undesirable people or criminals before they commit a crime (Büchi et al., 2022). However, in a democratic society, even when the dataveillance is successful, it encounters numerous moral and legal challenges. Despite the immorality and illegality of many dataveillance techniques, governments have increased their efforts in dataveillance initiatives due to their cost-effectiveness and customisation ability (Stockmann, 2023).
Dataveillance is often undertaken with no legal authority or amidst prohibition. The illegality of dataveillance is that it subjects people to public abuse and denies people their rights to undertake certain activities since they are suspects due to a method of profiling. Dataveillance can also fail to achieve its objectives when the person under surveillance suspends their activities temporarily and resumes them at a later date. Arora (2019) explained that protecting an individual or the source may encompass not telling the person that dataveillance has been undertaken and the authorities have checked their data, source of accusation and the information behind the accusation. According to Stockmann (2023), such situations are repugnant under the US and British legal systems. Dataveillance should not compromise a person's ability to defend themselves or prove their innocence. There are cases where a computer program or profiling could wrongly identify the accuser.
Personal dataveillance techniques investigate and monitor individual activities based on suspicion. The authorities presume the guilt of data subjects as they exercise their authority. In many cases, the people who engage with the data do not understand the rationale for the decision and often do not admit their lack of understanding (Büchi et al., 2022). As a result, the data subject and the authority may develop an adversarial relationship. These relationships are unequal as the organisation has the size, longevity and information advantages. While most of the cases that authorities follow using dataveillance techniques are deviants who are being sought, some of them are just innocent and different. These innocent people with matching profiles often have a difficult time convincing the authorities of their cases. Dataveillance encourages investigators to emphasise minor offences which can be solved using different methods. As such, they ignore important crimes that need more resources to solve. In addition, law enforcers are at a higher risk of prioritising amateur and occasional violators instead of skilled and repetitive criminals. Therefore, the dataveillance techniques promote inequality in law enforcement (Stockmann, 2023).
Summary of Chapters
Introduction: This section provides an overview of the importance of personal data for the state and introduces dataveillance, setting the stage for the essay's examination of the state's role in this practice.
Surveillance and Dataveillance: This chapter defines traditional surveillance and the more technologically advanced concept of dataveillance, discussing the different types (personal and mass) and how modern digital tools enhance state monitoring capabilities.
Dangers of Dataveillance: This part elaborates on the threats posed by dataveillance, including arbitrary actions, interference with liberties, profiling, and the moral and legal challenges it presents due to potential misidentification and lack of due process.
Dataveillance Case Study: India: This section examines India's Aadhaar system as a significant example of a national digital identity database, highlighting its intended benefits for service access versus its disadvantages in enabling widespread dataveillance and compromising privacy.
Social Media and Dataveillance: This chapter explores how social media platforms serve as vast data sources for states and organizations, discussing the challenges of balancing safety and freedom of speech, the role of AI in content moderation, and how dataveillance via social media hinders democratization.
Conclusion: This final section summarizes the essay's findings, reiterating the definition and techniques of dataveillance, its privacy implications, the failures of systems like Aadhaar, and the significant role of social media in facilitating state dataveillance for e-governance objectives.
Keywords
Dataveillance, State surveillance, Privacy rights, Digitalization, Aadhaar, Social media, E-governance, Information technology, Profiling, Data management, National security, Data-based democratization, Human rights, Data sharing, Mass surveillance.
Frequently Asked Questions
What is this work fundamentally about?
This work fundamentally explores how states utilize dataveillance in the contemporary world, analyzing its definition, techniques, benefits, and the significant moral and legal challenges it poses to citizens' privacy and rights.
What are the central thematic fields?
The central thematic fields include state surveillance, personal data management, the impact of information technology, data privacy, e-governance, social media's role in dataveillance, and the broader implications for democracy and human rights.
What is the primary goal or research question?
The primary goal is to evaluate various articles and publications on state surveillance, clarify the concept of dataveillance, and describe how information technology stimulates its development, ultimately examining the state's role in data suppression and obstacles to data-based democratization.
Which scientific method is used?
The essay primarily employs a literature review and analytical approach, synthesizing findings from various articles and publications to discuss the concepts and implications of dataveillance by the state.
What is covered in the main part?
The main part defines surveillance and dataveillance, details the dangers associated with dataveillance such as profiling and privacy violations, presents a case study of India's Aadhaar system, and analyzes the role of social media as a facilitator of state dataveillance.
What keywords characterize the work?
Key terms characterizing this work are dataveillance, state surveillance, privacy, digital identity, social media, e-governance, profiling, and data ethics.
How does India's Aadhaar system exemplify the challenges of dataveillance?
India's Aadhaar system, while designed to provide unique identification and facilitate access to government services, became an example of dataveillance challenges by enabling extensive state monitoring, compromising privacy, and failing to secure the database against misuse and fraud.
What role does social media play in modern dataveillance?
Social media platforms serve as immense data banks that states and organizations leverage for surveillance. Their integrated AI and vast user engagement allow for data classification, prediction of actions, and influencing citizen behavior, often leading to challenges in balancing free speech and safety.
- Citar trabajo
- Anonymous (Autor), 2023, Dataveillance and the State. Surveillance, Privacy, and Digital Governance in the Age of Big Data, Múnich, GRIN Verlag, https://www.grin.com/document/1607989
