The study of artificial intelligence (AI) is a relatively new but rapidly expanding field. In 2015, researchers in the United States published 850 articles on artificial intelligence and robotics in journals tracked by Nature Index; By 2021, that number will increase to 3,651. Such rapid growth has been replicated elsewhere, and while this is cause for excitement, there is also good reason to be wary of biases and inconsistencies in how this research is conducted and interpreted.
One of these potential differences is the way AI relates to urban and rural communities. According to Ganna Pogrebna, executive director of the Artificial Intelligence and Cyber Futures Institute at Charles Sturt University in Bathurst, New South Wales, those living in remote areas are more vulnerable to the dangers of technology than their urban counterparts, but are being neglected by research. .
How will rural communities be differentiated by AI?
It starts with data collection, which is often collected from our smart phones. Urban population is more likely to use iPhones while rural population tends towards Android. 2021 study (DJ Leith in Security and Privacy in Communication Networks (ed. J. Garcia-Alfaro forever) 231–251; Springer, 2021) found that Android phones send 20 times more data to Google than iPhones send to Apple. Android phones dominate in rural countries in Africa where 87.22% of the population use Android phones.
Nature Index 2022 AI and Robotics
Hence more data is collected from rural population than urban population. That’s the first half of this issue.
What are the consequences of this inconsistency in data collection? Are there no benefits to rural areas?
When technology is created off the back of that data, it benefits the urban population, and you have a situation where rural data can be used to enrich urban lives – that’s the second half of this problem. For example, a 2019 study (J. Guerra forever PLoS One 14, e0215278; 2019) found that public-health AI projects primarily use community-based surveillance data collected from rural areas, but studies using these data inform public-health policies in urban areas.
Facial recognition is another important example. Most of the data used to make facial recognition possible is generated from rural environments, but the technology is mainly used in metropolitan areas. Cities in some countries are trialling ‘pay with your face’ schemes on their public transport networks, which recognize passengers’ faces and track their journey from start to finish before charging their credit card. Cities in China are using facial recognition to help with COVID-19 contact tracing. So rural data are being used in urban settings and are not necessarily interchangeable.
How can AI improve rural communities if technology is better accessible?
Rural communities are largely missing out on the benefits of data-driven research, and that’s a shame because AI has the potential to improve country life. I’m in rural Australia where we often experience floods and bushfires; Projects are currently underway trying to use AI to advance disaster management in remote communities. Algorithms are mining social media posts to learn from the language used and whether flooding is occurring and to what extent it is being shared. This can be used to predict which areas will flood later and how badly. This gives us many hours head start in rural areas where resources are stretched.
Are you optimistic that this AI regional-urban gap will ever close? What can be done to fix or improve the situation?
I am optimistic that things will improve. Farmers and local businesses see AI as an opportunity to ensure their products reach where they are needed most. We need to look at the reasons why AI projects are expanding in cities and not in the country.
First, it is about digital infrastructure. As soon as you leave Sydney and head a few miles into the mountains, you lose mobile phone reception. In some parts of the world people are talking about 5G and 6G connections, but frankly, you’re lucky to have 3G here in the countryside, and Australia isn’t alone in this disparity. A 2018 survey conducted by the Pew Research Center found that nearly 25% of adults living in rural areas of the United States said accessing high-speed Internet was a big problem for them, compared to just 13% of adults in urban areas. and 9% in suburban districts. Rural communities are missing out on AI for infrastructural reasons beyond their control. That should change.
Second, recruiting talent to come to these areas and work on AI will be a challenge. I work from Charles Sturt University’s Bathurst campus, which is a three-hour drive inland from Sydney, and it’s hard to find people willing to move to the country. I don’t want this to become an advertisement for our organization, but I think it would be good if more AI researchers worked in rural areas like Bathurst, because it helps you better understand the challenges of living in a rural area as a researcher. area. Having more institutes in rural areas also helps education; Having more AI experts here means we can help raise the local community’s awareness of the benefits of AI.
I am optimistic, but closing the gap will require work from local communities, researchers and government.
This interview has been edited for clarity and length.
This article is part of Nature Index 2022 AI and Robotics, an editorially independent supplement. Advertisers have no influence on content.