{
  "id": 13871,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1885910",
  "title": "TRuE-XAI: causal and explainable ai framework for trustworthy corporate earnings growth forecasting",
  "summary": "Forecasting corporate earnings growth is fundamental to investment, credit, and regulatory decision-making. Existing forecasting approaches either rely on restrictive linear assumptions or provide limited interpretability, making them less suitable for high-stakes financial applications. This study proposes a transparent and causally informed framework for predicting future corporate earnings growth from financial statement data. We present TRuE-XAI (Transparent, Rule-based, and Explainable Arti",
  "authors": "Gopal Singh Jamnal",
  "category": "research",
  "topics": "regulation,safety-alignment,transparency,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T00:00:00.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/13871",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1885910",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}