Artificial Intelligence: Shaping the Future of Social Welfare in Our Nations
In the realm of social protection, governments worldwide are exploring the use of artificial intelligence (AI) to streamline and enhance the efficiency of their programmes. The applications of AI are diverse, ranging from client support, automating back-office processes, and fraud detection, to developing more personalised interventions and improving predictive analytics.
Currently, AI is being used to provide digital assistance to clients, automate administrative tasks, and detect fraudulent activities. For instance, chatbots and digital assistants are being employed to offer support to clients, while AI is being implemented to analyse data and detect potential errors or fraudulent activities in social programmes.
However, the introduction of AI in social protection is being done with caution. Governments are focusing on building trust and fostering transparency when using AI, recognising the significant concerns around privacy, data protection, and the potential for AI to reinforce existing biases.
Public skepticism towards AI use in processing and approving social program applications is prevalent, with only about 40% of people across OECD countries feeling that it benefits users. Key issues include privacy risks linked to data collection and usage, lack of transparency in AI algorithms and decision-making processes, the potential to reinforce existing biases, difficulties people face in understanding decisions made or assisted by AI, and the preference among many for human support alongside digital tools.
To address these concerns, governments need to improve communication about how AI is used, ensure safe and trustworthy deployment, and maintain human oversight to prevent harmful errors or misjudgments. Research and evaluation on the outcomes, costs, and benefits of AI in social protection remain important to ensure these technologies are effective and equitable.
Looking ahead, potential future uses of AI in social protection include predictive analytics to forecast demand and shocks, early identification of clients needing support, harm prevention, and tailoring interventions more precisely to individual needs. AI also offers potential for enhanced real-time policy adaptation and monitoring by analysing large datasets on beneficiary outcomes, improving program effectiveness.
In summary, the use of AI in social protection presents a promising blend of capabilities with significant trust and ethical challenges. Governments must navigate these challenges carefully to ensure the benefits of AI are realised while maintaining the trust and transparency essential for the successful implementation of these technologies.
| Current AI Uses | Potential Future Uses | Trust & Transparency Concerns | |----------------------------------------|-------------------------------------------|--------------------------------------------------| | Client support (chatbots, assistants) | Predictive analytics (demand, shocks) | Public skepticism towards government AI use | | Automating back-office processes | Early identification of vulnerable clients| Privacy and data protection risks | | Fraud and error detection | Tailoring interventions | Lack of algorithmic transparency | | Collecting feedback | Real-time monitoring and adaptive policy | Risk of bias and unfair outcomes | | | | Need to retain human support alongside AI tools |
This mix of promising capabilities with significant trust and ethical challenges defines the current discourse on AI in social protection by governments.
Artificial Intelligence (AI) is being utilized to offer digital assistance to clients and automate administrative tasks, with potential future developments including predictive analytics for forecasting demands and shocks, early identification of vulnerable clients, and tailoring interventions more precisely to individual needs. However, concerns about transparency, privacy, data protection, biases, and public skepticism towards AI use persist, necessitating improved communication, safe deployment, human oversight, and retaining human support alongside AI tools.
