By the prosocial AI editorial team.

Theoretical Underpinnings for an AI Model Aimed at Alleviating Individual Poverty

Introduction

Poverty remains one of the most pervasive challenges of the 21st century, affecting billions of individuals worldwide (World Bank, 2020). Despite significant technological advancements, the gap between the rich and the poor continues to widen in many societies (United Nations, 2020). Global wealth creation has yet to manage to bridge this gap. Artificial Intelligence (AI), with its transformative potential, offers new avenues to address the multifaceted dimensions of poverty. This article outlines the theoretical foundations for a model of AI designed to help individuals overcome poverty, providing practical examples for each framework and recommending the most compelling theoretical approach.

Understanding Poverty: Theoretical Perspectives

Definitions of Poverty

Poverty is a complex and multidimensional phenomenon. It can be defined in absolute terms—as the lack of resources necessary for basic survival—or, in relative terms—as economic inequality within a society (Sen, 1999). Absolute poverty is consistent over time and between countries, often measured by the international poverty line (e.g., living on less than $1.90 per day) (World Bank, 2020). Relative poverty is context-dependent, considering individuals poor if they fall significantly below the average living standards of their society (Townsend, 1979).

Theories on Causes of Poverty

Several theories attempt to explain the root causes of poverty:

1. Structural Theories: These posit that poverty results from systemic economic disparities, unequal access to education, and labor market segmentation (Wilson, 1987).For example, in many developing countries, rural populations may lack access to quality education, limiting their employment opportunities and perpetuating poverty cycles.
2. Behavioral Theories focus on individual behaviors and choices, suggesting that poverty arises from personal failings such as lack of effort or irresponsible decision-making (Lewis, 1966).For instance, individuals may not invest in education due to a lack of awareness of its long-term benefits, affecting their economic prospects.However, this perspective is often criticized for ignoring structural factors and the broader socio-economic context (Small etal., 2010).
3. Capability Approach: Proposed by economist Amartya Sen, this framework emphasizes expanding individuals’ capabilities—the freedom to achieve well-being (Sen, 1999). A practical example is providing vocational training to empower individuals with skills, enhancing their employment opportunities and ability to escape poverty.

Role of Information Asymmetry and Social Capital

Information asymmetry—where one party has more or better information than another—can exacerbate poverty by limiting access to opportunities (Stiglitz, 2002). For example, farmers in remote areas may lack market information, leading to lower prices for their produce. Social capital, the networks and relationships facilitating collective action, is crucial in providing support and resources (Putnam, 2000). Community microfinance groups exemplify how social capital can provide financial resources to people experiencing poverty.

The Role of AI in Addressing Poverty

AI can mitigate structural and informational barriers perpetuating poverty (Brynjolfsson & McAfee, 2014). Its capabilities include:

• Personalized Education and Skills Development: AI-driven educational platforms can tailor learning experiences to individual needs, bridging gaps in knowledge and skills (Luckin et al., 2016). For example, platforms like Khan Academy use AI to adapt lessons to a student’s learning pace, helping those behind catch up.
• Access to Information and Resources: AI can facilitate the disseminating of critical information regarding health, employment, and social services, especially in underserved communities (Lepri et al., 2018). Chatbots providing agricultural advice to farmers via mobile phones illustrate this application.
• Financial Inclusion: AI algorithms can assess creditworthiness using alternative data, enabling access to financial services for those without traditional credit histories (Kshetri, 2018). For instance, AI models analyze mobile phone usage patterns to provide microloans to individuals without formal banking histories.

Theoretical Frameworks Underpinning AI Models for Poverty Alleviation

Machine Learning and Personalization

Supervised Learning involves training algorithms on labeled data to make predictions or classifications (Bishop, 2006). In poverty alleviation, supervised Learning can predict which individuals are at risk of specific deprivations, allowing for targeted interventions (Jean et al., 2016). For example, satellite imagery analyzed using supervised Learning can identify impoverished regions by detecting features like housing materials and infrastructure.

Reinforcement Learning focuses on agents learning optimal behaviors through trial-and-error interactions with an environment (Sutton & Barto, 2018). AI models can use reinforcement learning to adapt educational content based on user interactions, optimizing learning outcomes. An example is an AI tutor that adjusts difficulty levels in real time based on student responses.

Statistical Learning Theory provides the foundation for understanding how and why algorithms generalize from data (Vapnik, 1998). This theory underpins the development of models that can predict poverty trends based on various socio-economic indicators, assisting policymakers in resource allocation.

Decision Support Systems

AI-powered Decision Support Systems can aid individuals in making informed choices by processing vast amounts of data and presenting actionable insights (Shim et al., 2002). For example, a farmer’s app that uses AI to recommend optimal planting times based on weather predictions and market prices helps increase crop yields and income.

Theories of human interaction and Cognitive Load inform the design of these systems to ensure they complement human decision-making without overwhelming users (Sweller, 1988; Amershi et al., 2019). Practical applications include user-friendly interfaces that present complex data in an accessible manner, such as visual dashboards for small business owners to track expenses and revenues.

Network Theory and Social Capital

AI can enhance social networks by connecting individuals to resources and communities (Easley & Kleinberg, 2010). Network Theory models how connections form and influence behavior, which can be leveraged to build platforms that increase social capital for marginalized groups (Granovetter, 1973). For instance, AI-powered platforms can connect artisans in remote areas to global markets, expanding their customer base.

A practical example is AI algorithms in job matching platforms that connect job seekers with potential employers based on skills and experience, reducing unemployment rates among disadvantaged populations.

Behavioral Economics and Nudging

Incorporating principles from Behavioral Economics, AI models can design interventions that “nudge” individuals towards beneficial behaviors without restricting freedom of choice (Thaler & Sunstein, 2008). For example, an AI-powered savings app might round up purchases to the nearest dollar, depositing the difference into a savings account, thus encouraging saving habits.

Theories of Nudging and Choice Architecture provide a framework for subtle guidance embedded within AI applications. A practical application is personalized notifications reminding users to engage in beneficial activities, such as attending job training sessions or completing educational modules.

Ethical and Societal Considerations

Fairness and Bias in AI

AI systems must be reasonably designed to avoid perpetuating existing biases (Barocas & Selbst, 2016). Theoretical frameworks such as Equal Opportunity and Demographic Parity guide the development of algorithms that provide equitable outcomes across different groups (Hardt et al., 2016)—for instance, ensuring that a loan approval AI model does not discriminate based on gender or ethnicity.

Recognizing and mitigating biases in data and models is crucial. A practical approach is regularly auditing AI systems for biased outcomes and adjusting algorithms accordingly.

Privacy and Data Protection

The Contextual Integrity framework emphasizes that privacy norms depend on the context and the type of information shared (Nissenbaum, 2004). AI models must adhere to data protection principles, ensuring that personal information is used ethically and with consent (Acquisti et al., 2015). For example, AI applications should anonymize user data when analyzing patterns to prevent misuse of personal information.

Implementing robust encryption and allowing users to control their data are practical steps towards maintaining privacy.

Empowerment and Agency

Maintaining individual agency is essential. Philosophical and ethical considerations stress that AI should empower users rather than make decisions for them (Floridi et al., 2018). This involves designing AI systems that enhance individuals’ capabilities to make their own informed choices.

A practical example is an AI assistant that provides options and information without dictating actions, such as suggesting various job opportunities based on skills but leaving the final choice to the individual.

Recommendation of the Best Theoretical Framework

Among the theoretical frameworks discussed, the Capability Approach combined with Machine Learning and Personalization offers the most comprehensive strategy for alleviating individual poverty.

The Capability Approach emphasizes expanding individuals’ freedoms and abilities, aligning to empower people to improve their lives (Sen, 1999). When integrated with Machine Learning and Personalization, AI models can tailor interventions to individual needs, maximizing effectiveness.

For instance, personalized educational AI platforms can enhance learning outcomes for students in impoverished areas by adapting to their learning styles and pacing. This addresses the immediate educational gap and expands their future capabilities, contributing to long-term poverty alleviation.

Moreover, Machine Learning models can identify at-risk individuals and communities by analyzing diverse data sources, enabling proactive and targeted support. This approach ensures that resources are utilized efficiently, addressing structural and individual factors contributing to poverty.

While other frameworks, such as Behavioral Economics and Network Theory, offer valuable insights, the Capability Approach with Machine Learning provides a holistic solution that empowers individuals, respects their agency, and addresses the multifaceted nature of poverty.

Conclusion

Integrating AI into poverty alleviation strategies holds significant promise, underpinned by theoretical frameworks across various disciplines. We can develop AI models that empower individuals to overcome poverty by harnessing the Capability Approach through machine learning and personalization while rigorously addressing ethical considerations. Practical applications demonstrate the potential of AI to tailor interventions, provide decision support, and enhance social networks. However, deploying such technologies must be cautiously approached, ensuring fairness, privacy, and the preservation of human agency. Continued interdisciplinary research and collaboration are essential to realize the full potential of AI in creating a more equitable world.

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