{
  "id": 1979,
  "url": "https://link.springer.com/article/10.1007/s10462-026-11627-1",
  "title": "Balancing fairness and influence spread in social networks: a multi-objective evolutionary approach",
  "summary": "Influence maximization in social networks has received increasing attention, particularly in applications where fairness among demographic groups is an important concern. However, many existing approaches either overlook group-level disparities or primarily optimize influence spread without explicitly modeling fairness-related trade-offs. In this paper, we propose a group-aware multi-objective evolutionary framework that decomposes seed sets into group-specific sub-solutions. Each demographic gr",
  "authors": null,
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-11T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-artificial-intelligence-review",
  "source_name": "Artificial Intelligence Review",
  "source_homepage": "https://link.springer.com/journal/10462",
  "ethics_ai_record_url": "https://ethics.ai/record/1979",
  "original_url": "https://link.springer.com/article/10.1007/s10462-026-11627-1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}