Better Data for Doing Good: Responsible Use of Big Data and Artificial Intelligence

The Evolution of Artificial Intelligence

Historically, the term "artificial intelligence" has been applied where computer systems imitate thinking or behavior that people associate with human intelligence, such as learning, problem solving, and decision-making. Modern AI comprises a rich set of subdisciplines and methods that leverage technologies such as visual, speech, and text recognition, as well as robotics. Machine learning is one such subdiscipline. Whereas hand-coded software programs typically contain specific instructions on how to complete a task, machine learning allows a computer system to recognize patterns and make predictions. Deep learning, a subset of machine learning, goes one step further ­– with deep artificial neural networks, based on complex algorithms, computers can learn from large volumes of data while reaching new levels of accuracy. 

In sum, AI is enabling computer systems to collect, analyze, and process large amounts of data in real time to recognize patterns, make decisions, and, more significantly, to learn from said data and from their own experiences.

Meanwhile, recent advances in sensors and imaging technologies and data storage, processing, and transfer technologies, as well as complex and self-improving algorithms, to name but a few, are the range of expanding AI applications available today. AI is already incorporated in several online products, including Google search, Google Translate, and Facebook's automatic photo-tagging and translation applications. Financial companies rely on AI to produce the financial modeling that underpins their insurance, banking, and asset management products. Moreover, leading research hospitals have started using AI tools to help medical professionals diagnose and choose the best course of treatment for their patients.

Although the current application of AI is mostly limited to internet business, digital marketing, gaming, and selfdriving cars, a wealth of opportunities exist for AI methods to perform different tasks that can accelerate achievement of the SDGs and inform humanitarian practice. Box 3.2 describes how AI can help transform traditional sectors, such as transport.

Box 3.2 Artificial intelligence and the transport sector

The proliferation of big data is helping to transform the transport sector. Fueled by data and connectivity, a variety of intelligent transport systems have been introduced as the sector rapidly evolves.

Alongside other disruptive technologies, such as connected vehicles and automated driving, these intelligent systems are soon expected to completely change the way people and goods are moved. Big data can be combined with predictive analytics, for example, to optimize cargo transport networks based on projected shipping demand. Data exchanged among vehicles and infrastructure will soon be used to automatically optimize vehicle routes and speeds in real time, reducing congestion and emissions. In the Philippines, for example, real-time traffic data shared using open source tools is being used to optimize traffic flows in Manila and Cebu City. In Indonesia, location information from GPS-stamped tweets is being used to reveal commuting statistics in the Greater Jakarta area. 

The potential for data-driven intelligent transport systems to transform the world's transportation systems is immense, particularly if the data is combined with new ways to link disparate data sets and creative methods to visualize data.