Author(s): Venkat Kalyan Uppala
ABSTRACT
The application of artificial intelligence (AI) in renter screening is revolutionizing the tenant selection process, offering unprecedented accuracy and efficiency. Traditional methods, heavily reliant on credit scores, often fail to capture the full financial stability of potential tenants, particularly those with limited credit histories or recent immigrants. AI-driven screening tools provide a more comprehensive assessment by analyzing diverse data sources, such as payment histories and social graphs, reducing the reliance on conventional credit metrics. However, the integration of AI in renter screening also raises concerns about potential biases that could disproportionately affect certain demographic groups. This paper explores the dual impact of AI on enhancing accuracy while addressing the challenges of algorithmic bias. By leveraging advanced data analysis techniques and ethical AI design principles, the proposed AI solutions aim to create a more equitable and reliable renter screening process, benefiting both landlords and tenants. The paper discusses the technical processes behind AI-driven screening, the benefits of incorporating alternative data sources, and the importance of continuous auditing and transparency to mitigate bias and ensure fairness in tenant selection.
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