Artificial intelligence is being deployed in many different areas. Within higher education, it is used for college admissions and financial aid decisions. Health researchers employ it to scan the scientific literature for chemical compounds that may generate new medical treatments. E-commerce sites deploy algorithms to make product recommendations for consumers based on their areas of interest.1
But one of the most important growth areas lies in finance and operations. Both public and private sector organizations have large budgets to manage and it is important to operate efficiently and effectively. Accusations of budget inefficiencies or wasteful spending decrease public confidence and make it important to figure out how to manage resources in fair ways.
To help with budgetary oversight, AI is being used for financial management and fraud detection. Advanced algorithms can spot abnormalities and outliers that can be referred to human investigators to determine if fraud actually has taken place. It is a way to use technology to improve budget audits, personnel performance, and organizational activities.
Yet is it crucial to overcome several problems that plague public sector innovation: procurement obstacles, insufficiently trained workers, data limitations, a lack of technical standards, cultural barriers to organizational change, and making sure anti-fraud applications adhere to responsible AI principles.
In this paper, I make 10 recommendations for ways to overcome these issues so that managers and workers can gain the benefits of digital innovation without incurring serious ethical or operational problems. Among my specific recommendations are:
- Be proactive about developing responsible AI by hiring ethicists, creating review boards, and developing mitigation strategies early in product design and deployment.
- Use evidence-based evaluation to determine the efficacy of new projects.
- Expand the geographical opportunities for the technical workforce pool by encouraging remote or hybrid work.
- Develop partnerships with higher education, community colleges, technical institutes, online course providers, or firms offering personalized learning or certificate programs to train current and future workers.
- Embrace lifelong learning and expand professional development programs for technical and non-technical staff.
- Develop clear standards for data collection and analysis that will improve AI algorithms.
- Reform government procurement…
Continue reading: https://www.brookings.edu/research/using-ai-and-machine-learning-to-reduce-government-fraud/