Artificial Intelligence (AI) is undeniably one of the most transformative technologies of our era. Its evolution mirrors that of the internet—spanning sectors, offering wide accessibility and consistently improving while maintaining low marginal costs for additional users. AI's integration into our daily lives and workplaces is advancing rapidly, with tech companies deploying increasingly sophisticated models.

In the social sector, AI's promise is just as compelling. Governments, NGOs, and social enterprises are embedding AI into programs to improve lives, from healthcare diagnostics and disaster preparedness to education and agricultural advisories. However, this enthusiasm must be tempered with lessons from the past—numerous promising technologies have faltered in the social sector because they lacked rigorous evaluation, contextual understanding, or scalability.

To fully realize AI’s potential for social good, embracing evidence-based implementation through rigorous impact evaluations is crucial. By learning from decades of research, we can integrate AI effectively into social programs, evaluate its real-world impact, and disseminate insights to maximize benefits while minimizing harm.

Three Pillars of Evidence-Based AI Integration

• Leveraging Research to Maximize Impact: Years of rigorous research using randomized controlled trials (RCTs) provide insights into where AI can most effectively support social programs. For instance, healthcare, education, and disaster management sectors can benefit significantly when AI tools are thoughtfully designed to address well-defined challenges.
• Drawing Lessons from Past Successes and Failures: Technology adoption in the social sector has a mixed history. Initiatives such as One Laptop Per Child and smokeless stoves fell short of expectations because they were not grounded in evidence or lacked adaptability to field realities. AI implementers must heed lessons from these experiences to design programs that succeed in practice, not just in theory.
• Promoting Rigorous and Concurrent Evaluations: Concurrent impact evaluations of AI-infused programs are essential to understanding their real-world effects. Independent evaluations can provide actionable insights, build trust among stakeholders, and ensure that AI programs are scaled responsibly.

AI’s Potential Across Social Sectors

From ongoing research at J-PAL and elsewhere, we have identified five critical areas where AI holds transformative potential:

• Improved Targeting and Needs Prediction: Due to inefficient tools like surveys and thresholds, social programs often struggle to identify the right beneficiaries. AI’s ability to process large datasets can enhance targeting precision, ensuring resources reach those who need them most. For instance, an AI model deployed in India predicts flood risks in Bihar, enabling timely warnings and resource allocation to vulnerable households. However, such tools must be rigorously evaluated to avoid misuse or exclusion.
• Increasing Access to Services: AI can bridge gaps in service delivery, especially in resource-constrained settings. For example, machine learning applications in agriculture allow farmers to receive instant, tailored advice via smartphones, overcoming limitations in the availability of extension workers. Ensuring trust in such tools is vital for widespread adoption.
• Enhancing Frontline Worker Capabilities: AI can augment the capabilities of frontline workers, enabling them to address complex challenges. For instance, community health workers in the Global South, supported by AI diagnostic tools, can offer improved care without formal medical training. Such tools, grounded in rigorous evaluations, can lead to better health outcomes and resource efficiency.
• Reducing Bias and Ensuring Fairness: When properly designed, AI can identify and mitigate biases inherent in human systems. For example, algorithms can improve fairness in hiring processes by focusing on candidate potential rather than conventional metrics. However, constant evaluation is necessary to prevent unintended bias amplification.
• Boosting Resource Efficiency for Global Challenges: AI can address fiscal constraints by automating labour-intensive processes like tax audits. Machine learning models in India have shown promise in identifying tax evaders, thereby expanding the tax base without significantly increasing manpower. Evaluations can ensure these tools complement human judgment rather than replace it.

The Path Forward: Evidence-Informed AI

While AI's potential to combat poverty, inequality, and climate change is immense, its impact depends on careful design, implementation, and evaluation. Crucial questions remain: Will AI displace jobs or enable upward mobility for low-skilled workers? Can it foster equitable global development, or will it exacerbate existing disparities?

Answering these questions requires a commitment to evidence-based practices. By integrating rigorous impact evaluations into AI initiatives, we can uncover what works and what doesn’t. This approach optimizes AI’s contributions to social welfare and builds stakeholder trust, ensuring AI’s role as a catalyst for positive change.

By harnessing AI thoughtfully and guided by evidence, we can transform the social sector—creating a future where technology amplifies human potential and drives meaningful progress.

Disclaimer: This article is an extract from the authored article "AI for Social Good" By Iqbal Dhaliwal, Global Executive Director, J-PAL, Department of Economics, MIT & Michael Hou, Senior Policy Associate, J-PAL Global.

Source: SSIR

Image source: Unsplash