Skip to content

Threat and Remedy: AI’s Technological and Normative Role in Democratic Discourse and Counter Speech

Sep 2026 · Media and Communication · 0 citations · 80 references
Hate Speech and Cyberbullying Detection

Abstract

Artificial intelligence (AI) has fundamentally transformed online discourse, serving simultaneously as a source of and a potential solution to threats to democracy. This article conceptually examines AI’s multifaceted role in digital public spheres, beginning with an analysis of how AI shapes democratic processes online, ranging from information curation and political participation to dialogue facilitation and the construction of epistemic infrastructures. We identify several key risks: AI amplifies harmful content through automated content generation, coordinated manipulation, and the algorithmic mainstreaming of borderline content that often evades traditional detection systems. Within this broader democratic context, we position counter speech as a critical intervention strategy and examine both reactive measures (e.g., automated detection and content moderation) and proactive approaches (e.g., prebunking, algorithmic downranking, friction design, and AI-mediated dialogue). We argue that effective counter speech increasingly relies on human–AI collaboration, where AI supports human counter speakers with factual resources, emotional scaffolding, and scalability, while human actors preserve the authenticity and agency that make counter speech normatively meaningful. However, realizing the potential of such collaboration requires moving beyond technological fixes toward democratically legitimated governance structures. Drawing on examples from Wikipedia and decentralized platforms, we demonstrate how transparent and participatory institutional arrangements can foster more resilient discourse environments. We conclude that societies can harness AI’s democratic potential while mitigating its risks only by proactively establishing legitimate deliberative processes grounded in broadly shared discourse norms.

Read PDF

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8

PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection

Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.

Jinhe Bi, Yifan Wang, Danqi Yan et al. · 73 citations · ⚡4
#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.

Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al. · 59 citations · ⚡8

Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study

An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.

Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al. · 56 citations · ⚡6

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.