Reimagining property tax: AI-powered assessment
The taxes imposed by property have been one of the main revenue generators to the government in the last decades for financing diverse ways public services, from education to infrastructure to public safety. The traditional approaches to property tax assessment, however, have a number of inefficiencies and inaccuracies due to human biases, which end up giving quite inconsistent property valuations. These inefficiencies create a foundation for different financial burdens, conflicts, and administrative inefficiencies. Rapid advances in artificial intelligence (AI) technology will also serve to provide more energy toward putting forth benefits that come with AI models designed to make property tax assessment more accurate, efficient, and transparent. Attributes tagged for this AI-powered tool such as few human biases, real-time property value assessment, and streamlined tax collection will include machine learning algorithms, GIS, and big data analytics in tax administration. While all that sounds hunky-dory, ethical questions, fairness in algorithms, and the regulatory compliance and data privacy hurdles are some of the greatest challenges posed by AI in tax administration. The research intends to explore the role of AI in property tax assessment by looking at its advantages, problems, and its use in realistic situations. It speaks of legal and ethical implications of tax together with the integration of AI and suggests some recommendation mechanisms toward ensuring transparency, equity, and compliance. This should therefore contribute to expanding the discourse on transforming tax administration for effective governance in examining transformative AI in property tax assessments.
Publication Details
- Journal
- World Journal of Advanced Engineering Technology and Sciences
- Published
- 2024-10-30
- DOI
- https://doi.org/10.30574/wjaets.2024.13.1.0475
- Citations
- 1
- Primary Topic
- Bacterial Identification and Susceptibility Testing
- Type
- article
- Field-Weighted Citation Impact
- 0.38