Ethical explainable artificial intelligence for green innovation: a systematic review and meta-analysis of transparency, fairness, and accountability in sustainable technologies
Abstract
Artificial Intelligence (AI) is increasingly deployed across sustainability-oriented domains such as smart energy systems, precision agriculture, water management, and sustainable manufacturing to support the United Nations Sustainable Development Goals (SDGs). However, the growing use of complex and opaque models has raised concerns regarding transparency, accountability, fairness, and ethical governance in high-impact sociotechnical decision-making contexts. This study presents a PRISMA-guided systematic literature review and meta-analysis of 140 peer-reviewed empirical studies (2020–2025) that integrate Explainable Artificial Intelligence (XAI) within sustainability-focused AI applications. Following the structured identification, screening, and eligibility assessment of 550 records retrieved from Scopus, Web of Science, IEEE Xplore, ScienceDirect, and SpringerLink, quantitative synthesis was conducted using random-effects models to explicitly accommodate cross-study heterogeneity. The meta-analysis indicates that XAI-integrated AI systems reported consistently high descriptive performance levels within the analysed literature (pooled classification accuracy ≈ 95.84%, aggregated regression fit R2 ≈ 0.964). These pooled estimates do not represent transferable or generalisable performance benchmarks, as substantial heterogeneity exists across studies in terms of algorithmic paradigms, dataset characteristics, feature-engineering strategies, and experimental protocols (I2 ≈…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.