Fairness in algorithmic decision systems: A microfinance perspective

Fairness is a crucial concept in the context of artificial intelligence (AI) ethics and policy. It is an incremental component in existing ethical principle frameworks, especially for algorithm-enabled decision systems. Yet, unwanted biases in algorithms persist due to the failure of practitioners to consider the social context in which algorithms operate. Recent initiatives have led to the development of ethical principles, guidelines and codes to guide organisations through the development, implementation and use of fair AI. However, practitioners still struggle with the various interpretations of abstract fairness principles, making it necessary to ask context-specific questions to create organisational awareness of fairness-related risks and how AI affects them. This paper argues that there is a gap between the potential and actual realised value of AI. We propose a framework that analyses the challenges throughout a typical AI product life cycle while focusing on the critical question of how rather broadly defined fairness principles may be translated into day-to-day practical solutions at the organisational level. We report on an exploratory case study of a social impact microfinance organisation that is using AI-enabled credit scoring to support the screening process of particularly financially marginalised entrepreneurs. This paper highlights the importance of considering the strategic role of the organisation when developing and evaluating fair algorithm-enabled decision systems. The paper concludes that the framework, introduced in this paper, provides a set of questions that can guide thinking processes inside organisations when aiming to implement fair AI systems.