Are machine learning technologies ready to be used for humanitarian work and development?
arXiv:2307.01891 · doi:10.1142/S0219525924400022
Abstract
Novel digital data sources and tools like machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize data about development and can contribute to monitoring and mitigating humanitarian problems. The potential of applying novel technologies to solving some of humanity's most pressing issues has garnered interest outside the traditional disciplines studying and working on international development. Today, scientific communities in fields like Computational Social Science, Network Science, Complex Systems, Human Computer Interaction, Machine Learning, and the broader AI field are increasingly starting to pay attention to these pressing issues. However, are sophisticated data driven tools ready to be used for solving real-world problems with imperfect data and of staggering complexity? We outline the current state-of-the-art and identify barriers, which need to be surmounted in order for data-driven technologies to become useful in humanitarian and development contexts. We argue that, without organized and purposeful efforts, these new technologies risk at best falling short of promised goals, at worst they can increase inequality, amplify discrimination, and infringe upon human rights.
arXiv comment15 pages, 2 figures
References in corpus (11)
- Artificial Intelligence: the global landscape of ethics guidelines
- The role of artificial intelligence in achieving the Sustainable Development Goals
- Principles alone cannot guarantee ethical AI
- The Fallacy of AI Functionality
- Social media fingerprints of unemployment
- An analytical framework to nowcast well-being using mobile phone data
- Narratives and Counternarratives on Data Sharing in Africa
- Analyzing gender inequality through large-scale Facebook advertising data
- Mobile Communication Signatures of Unemployment
- Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide
- Interpreting wealth distribution via poverty map inference using multimodal data