Political Propagation of Social Botnets: Policy Consequences

• URL: http://arxiv.org/abs/2205.04830v1
• Date: Tue, 10 May 2022 12:08:03 GMT
• Title: Political Propagation of Social Botnets: Policy Consequences
• Authors: Shashank Yadav
• Abstract summary: The 2016 US election was a watershed event where an electoral intervention by an adversarial state made extensive use of software robots and data driven communications. We reflect upon the policy consequences of the use of Social Botnets and understand the impact of their adversarial operation. For future work, it is important to understand the agency and collective properties of these software robots.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The 2016 US election was a watershed event where an electoral intervention by an adversarial state made extensive use of networks of software robots and data driven communications which transformed the interference into a goal driven functionality of man-machine collaboration. Reviewing the debates post the debacle, we reflect upon the policy consequences of the use of Social Botnets and understand the impact of their adversarial operation in terms of catalysing institutional decay, growing infrastructural anxieties, increased industry regulations, more vulnerable Individuals and more distorted ideas, and most importantly, the emergence of an unintended constituency in form of the bot agency itself. The article first briefly introduces the nature and evolution of Social Botnets, and then moves over to discussing the policy consequences. For future work, it is important to understand the agency and collective properties of these software robots, in order to design the institutional and socio-technical mechanisms which mitigate the risk of adversarial social engineering using these bots from interfering into democratic processes.

Related papers

• Charting the Landscape of Nefarious Uses of Generative Artificial Intelligence for Online Election Interference [11.323961700172175]
  This paper explores the nefarious applications of GenAI, highlighting their potential to disrupt democratic processes. Malicious actors exploit these technologies to try influencing voter behavior, spread disinformation, and undermine public trust in electoral systems.
  arXiv  Detail & Related papers  (2024-06-04T00:26:12Z)
• Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
  Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal. We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
  arXiv  Detail & Related papers  (2024-04-17T02:57:42Z)
• A multidisciplinary framework for deconstructing bots' pluripotency in dualistic antagonism [7.152948800435588]
  Bot-disseminated misinformation could subtly yet profoundly reshape societal processes. We propose an interdisciplinary framework to characterise bots' emergent risks to civic discourse.
  arXiv  Detail & Related papers  (2024-02-23T06:08:44Z)
• Human-machine social systems [0.0]
  We review recent research from across a range of disciplines and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion, and collective decision-making. To ensure more robust and resilient human-machine communities, researchers should study them using complex-system methods. Engineers should explicitly design AI for human-machine and machine-machine interactions, and regulators should govern the ecological diversity and social co-evolution of humans and machines.
  arXiv  Detail & Related papers  (2024-02-22T09:54:41Z)
• LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control [80.86089324742024]
  We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem. CNN processes localized perception; a graph neural network (GNN) facilitates robot communications. Evaluations show that the LPAC models outperform standard decentralized and centralized coverage control algorithms.
  arXiv  Detail & Related papers  (2024-01-10T00:08:00Z)
• The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
  The development and regulation of AI seems to have reached a critical stage. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. This paper analyses the most advanced legal proposal, the European Union's AI Act.
  arXiv  Detail & Related papers  (2023-11-03T12:51:37Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• Asynchronous Perception-Action-Communication with Graph Neural Networks [93.58250297774728]
  Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments. The robots must execute a Perception-Action-Communication loop -- they perceive their local environment, communicate with other robots, and take actions in real time. Recently, this has been addressed using Graph Neural Networks (GNNs) for applications such as flocking and coverage control. This paper proposes a framework for asynchronous PAC in robot swarms, where decentralized GNNs are used to compute navigation actions and generate messages for communication.
  arXiv  Detail & Related papers  (2023-09-18T21:20:50Z)
• Socialbots and the Challenges of Cyberspace Awareness [0.0]
  We examine the mechanisms of developing situation awareness in cyberspace and the governance issues that socialbots bring into this existing paradigm of cyber situation awareness. We conceptualise Cyberspace Awareness as a socio-technical phenomena with Syntactic, Semantic, and Operatic dimensions. The paper contributes to the ideas of situational awareness in cyberspace, and characterises the challenges therein around tackling the increasingly social and often pervasive, automation in cyber threat environments.
  arXiv  Detail & Related papers  (2023-03-05T08:45:05Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• A machine-learning software-systems approach to capture social, regulatory, governance, and climate problems [0.0]
  It will discuss the role of an artificially-intelligent computer system as critique-based, implicit-organizational, and an inherently necessary device, deployed in synchrony with parallel governmental policy, as a genuine means of capturing nation-population in quantitative form, public contentment in societal-cooperative economic groups, regulatory proposition, and governance-effectiveness domains.
  arXiv  Detail & Related papers  (2020-02-23T13:00:52Z)

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.