Algorithmic bias in economic decision making (resource allocation, lending, hiring)

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Algorithmic bias in economic decision making has become a topic of growing concern as artificial intelligence and machine learning algorithms play an increasingly prominent role in various domains, including resource allocation, lending, and hiring. This paper aims to explore the multifaceted dimensions of algorithmic bias in these areas and its potential consequences for individuals and society.
First, the paper submerges into the concept of algorithmic bias, examining how biases can emerge within algorithms due to factors such as biased training data, flawed algorithms, or systemic inequalities. It discusses the ethical and social implications of algorithmic bias, highlighting the potential reinforcement of existing inequalities and the erosion of fairness and equal opportunity.
Next, the paper focuses on the specific domains of resource allocation, lending, and hiring, depicting how algorithmic decision-making systems have been employed in these areas. It explores the ways in which algorithmic bias can influence resource allocation decisions, potentially perpetuating disparities in access to critical resources. The paper also examines how biased algorithms in lending can disproportionately impact marginalised communities, exacerbating financial inequality and reinforcing historical patterns of discrimination. Additionally, it explores the role of algorithms in hiring processes, highlighting concerns related to unfairness, lack of diversity, and the potential for perpetuating biased practices.
To better understand and address algorithmic bias, the paper presents a range of methodologies and strategies that have been proposed to mitigate bias in economic decision making. These include approaches such as algorithmic auditing, transparency and accountability measures, diverse and representative training data, and regular evaluation of algorithms for bias. The paper also discusses the challenges and limitations associated with these mitigation strategies.
Overall, this paper seeks to contribute to the existing body of knowledge on algorithmic bias in economic decision making and stimulate further discussions on creating a more inclusive and just AI-powered economy.