Personal autonomy and surveillance capitalism: possible future
developments
- URL: http://arxiv.org/abs/2302.08946v1
- Date: Fri, 17 Feb 2023 15:27:14 GMT
- Title: Personal autonomy and surveillance capitalism: possible future
developments
- Authors: Davide Foini
- Abstract summary: I focus on the threat imposed on the autonomy of human beings in the context of surveillance capitalism.
I provide an analysis of the reasons why this threat exists and what consequences we could face if we take no action against such practices.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rise of social media and the increase in the computational capabilities
of computers have allowed tech companies such as Facebook and Google to gather
incredibly large amounts of data and to be able to extract meaningful
information to use for commercial purposes. Moreover, the algorithms behind
these platforms have shown the ability to influence feelings, behaviors, and
opinions, representing a serious threat to the independence of their users. All
of these practices have been referred to as "surveillance capitalism", a term
created by Shoshana Zuboff. In this paper I focus on the threat imposed on the
autonomy of human beings in the context of surveillance capitalism, providing
both an analysis of the reasons why this threat exists and what consequences we
could face if we take no action against such practices.
Related papers
- Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI [67.58673784790375]
We argue that the 'bigger is better' AI paradigm is not only fragile scientifically, but comes with undesirable consequences.
First, it is not sustainable, as its compute demands increase faster than model performance, leading to unreasonable economic requirements and a disproportionate environmental footprint.
Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate.
arXiv Detail & Related papers (2024-09-21T14:43:54Z) - A+AI: Threats to Society, Remedies, and Governance [0.0]
This document focuses on the threats, especially near-term threats, that Artificial Intelligence (AI) brings to society.
It includes a table showing which countermeasures are likely to mitigate which threats.
The paper lists specific actions government should take as soon as possible.
arXiv Detail & Related papers (2024-09-03T18:43:47Z) - Computing Power and the Governance of Artificial Intelligence [51.967584623262674]
Governments and companies have started to leverage compute as a means to govern AI.
compute-based policies and technologies have the potential to assist in these areas, but there is significant variation in their readiness for implementation.
naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power.
arXiv Detail & Related papers (2024-02-13T21:10:21Z) - Carthago Delenda Est: Co-opetitive Indirect Information Diffusion Model
for Influence Operations on Online Social Media [6.236019068888737]
We introduce Diluvsion, an agent-based model for contested information propagation efforts on Twitter-like social media.
We account for engagement metrics in influencing stance adoption, non-social tie spreading of information, neutrality as a stance that can be spread, and themes that are analogous to media's framing effect and are symbiotic with respect to stance propagation.
arXiv Detail & Related papers (2024-02-02T21:15:24Z) - Decoding the Silent Majority: Inducing Belief Augmented Social Graph
with Large Language Model for Response Forecasting [74.68371461260946]
SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics.
Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
arXiv Detail & Related papers (2023-10-20T06:17:02Z) - Navigating Surveillance Capitalism: A Critical Analysis through
philosophical perspectives in Computer Ethics [0.0]
Surveillance capitalism is the practice of collecting and analyzing massive amounts of user data.
Tech companies like Google and Facebook use users' personal information to deliver personalized content and advertisements.
Another example of surveillance capitalism is the use of military technology to collect and analyze data for national security purposes.
arXiv Detail & Related papers (2023-05-05T18:37:56Z) - Machine Learning Featurizations for AI Hacking of Political Systems [0.0]
In the recent essay "The Coming AI Hackers," Schneier proposed a future application of artificial intelligences to discover, manipulate, and exploit vulnerabilities of social, economic, and political systems.
This work advances the concept by applying to it theory from machine learning, hypothesizing some possible "featurization" frameworks for AI hacking.
We develop graph and sequence data representations that would enable the application of a range of deep learning models to predict attributes and outcomes of political systems.
arXiv Detail & Related papers (2021-10-08T16:51:31Z) - Explainable Patterns: Going from Findings to Insights to Support Data
Analytics Democratization [60.18814584837969]
We present Explainable Patterns (ExPatt), a new framework to support lay users in exploring and creating data storytellings.
ExPatt automatically generates plausible explanations for observed or selected findings using an external (textual) source of information.
arXiv Detail & Related papers (2021-01-19T16:13:44Z) - Overcoming Failures of Imagination in AI Infused System Development and
Deployment [71.9309995623067]
NeurIPS 2020 requested that research paper submissions include impact statements on "potential nefarious uses and the consequences of failure"
We argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances, as well as viable proxies for assessing harms in the widest sense.
arXiv Detail & Related papers (2020-11-26T18:09:52Z) - Politics of Adversarial Machine Learning [0.7837881800517111]
adversarial machine-learning attacks and defenses have political dimensions.
They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy them.
We show how defenses against adversarial attacks can be used to suppress dissent and limit attempts to investigate machine learning systems.
arXiv Detail & Related papers (2020-02-01T01:15:39Z) - Quantifying the Vulnerabilities of the Online Public Square to Adversarial Manipulation Tactics [43.98568073610101]
We use a social media model to quantify the impacts of several adversarial manipulation tactics on the quality of content.
We find that the presence of influential accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation.
These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
arXiv Detail & Related papers (2019-07-13T21:12:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.