AI in Eco-Social Work and Climate Change

Background and Purpose: The world is currently facing a convergence of unprecedented social and environmental crises, including climate change, pandemics, rising global inequality, and geopolitical instability. These interlinked challenges are not only reshaping human and ecological systems but are also deepening vulnerabilities among already marginalized populations. Social work has been criticized for focusing primarily on the social dimensions of a person’s environment while neglecting the natural environment that is essential for holistic well-being. Artificial Intelligence (AI) and environmental social work are in the infant stage of real-world practice. Considering the limited literature and evidence-based practice, This study offers a comprehensive knowledge and understanding of eco-social work, AI in environmental social work in response to climate change, highlighting some of the AI innovations for climate adaptation, the role of social work at the micro and macro level, ethical considerations and way forward for environmental justice.

Methods: The study adapts the Environmental Justice Framework to promote Eco-Social Work by addressing the unequal distribution of environmental harms. This highlights real-world case examples from California’s AI-driven wildfire detection systems, Sub-Saharan Africa’s AI-enhanced water management systems, Amazon Rainforest AI-driven deforestation monitoring system, and the Bangladesh AI-powered flood prediction models to explore how vulnerable groups often bear ecological challenges and how AI enables to the better adaptation of climate-related disaster situations. These case examples of AI-supported intervention approaches enable social workers to address both immediate concerns and the underlying systemic issues that contribute to them. It also empowers practitioners to challenge and transform the broader systems of inequality and environmental harm that disproportionately affect marginalized communities.

Results: The AI-driven eco-social work models enhance marginalized community capacity to support disaster preparedness, advocate for policy changes, and promote sustainable solutions. AI-driven wildfire detection systems help social workers support displaced communities by coordinating shelter, medical aid, and mental health services for affected individuals in California. AI-enhanced water management systems aid social workers in addressing drought crises by ensuring an equitable distribution of water resources and reducing the burden on impoverished communities. AI-driven deforestation monitoring enables social workers and Indigenous rights advocates to track environmental destruction, protect vulnerable communities, and push for policy interventions. AI-powered flood prediction models assist NGOs and social workers in implementing preemptive measures, reducing harm to vulnerable populations by facilitating early evacuations and resource allocation.

Conclusion and Implication: AI in Eco-Social Work offers significant potential to enhance both micro and macro-level interventions aimed at achieving environmental sustainability and social justice. Although AI-supported tools provide a high potential to adapt the eco-social work practice, there are ethical issues around training data, bias in AI models, and digital divide. As AI continues to evolve, it is crucial to prioritize vulnerable communities, address biases, and integrate social work principles to ensure equitable climate solutions.