AI News, How can AI help in a humanitarian crisis?

How can AI help in a humanitarian crisis?

This is not because computers have achieved human-like consciousness, but because of advances in machine learning, where computers learn from huge databases how to classify new data.

AI is being applied to echocardiograms to predict heart disease, to workplace data to predict if employees are going to leave, and to social media feeds to detect signs of incipient depression or suicidal tendencies.

Any walk of life where there is abundant data – and that means pretty much every aspect of life – is being eyed up by government or business for the application of AI.

The way machine learning consumes big data and produces predictions certainly suggests it can both grasp the enormity of the humanitarian challenge and provide a data-driven response.

But the nature of machine learning operations mean they will actually deepen some of the problems of humanitarianism, and introduce new ones of their own.

The maths Exploring these questions requires a short detour into the concrete operations of machine learning, if we are to bypass the misinformation and mystification that attaches to the term AI.

Machine learning, then, is not just a method but a machinic philosophy where abstract calculation is understood to access a truth that is seen as superior to the sense-making of ordinary perception.

This constructs risk in the same way that Twitter determines trending topics, allocating and withholding resources in a way that algorithmically demarcates the deserving and the undeserving.

By embedding the logic of the powerful to determine what happens to people at the periphery, humanitarian AI becomes a neocolonial mechanism that acts in lieu of direct control.

This is as important for humanitarian AI as for AI generally – for, if an alternative technicsis not mobilised by approaches such as people’s councils, the next generation of humanitarian scandals will be driven by AI.

How can AI help in a humanitarian crisis?

This is not because computers have achieved human-like consciousness, but because of advances in machine learning, where computers learn from huge databases how to classify new data.

AI is being applied to echocardiograms to predict heart disease, to workplace data to predict if employees are going to leave, and to social media feeds to detect signs of incipient depression or suicidal tendencies.

Any walk of life where there is abundant data – and that means pretty much every aspect of life – is being eyed up by government or business for the application of AI.

The way machine learning consumes big data and produces predictions certainly suggests it can both grasp the enormity of the humanitarian challenge and provide a data-driven response.

But the nature of machine learning operations mean they will actually deepen some of the problems of humanitarianism, and introduce new ones of their own.

The maths Exploring these questions requires a short detour into the concrete operations of machine learning, if we are to bypass the misinformation and mystification that attaches to the term AI.

Machine learning, then, is not just a method but a machinic philosophy where abstract calculation is understood to access a truth that is seen as superior to the sense-making of ordinary perception.

This constructs risk in the same way that Twitter determines trending topics, allocating and withholding resources in a way that algorithmically demarcates the deserving and the undeserving.

By embedding the logic of the powerful to determine what happens to people at the periphery, humanitarian AI becomes a neocolonial mechanism that acts in lieu of direct control.

This is as important for humanitarian AI as for AI generally – for, if an alternative technicsis not mobilised by approaches such as people’s councils, the next generation of humanitarian scandals will be driven by AI.

AI will be used by humanitarian organisations – this could deepen neocolonial tendencies

This is not because computers have achieved human-like consciousness, but because of advances in machine learning, where computers learn from huge databases how to classify new data.

AI is being applied to echocardiograms to predict heart disease, to workplace data to predict if employees are going to leave, and to social media feeds to detect signs of incipient depression or suicidal tendencies.

Any walk of life where there is abundant data – and that means pretty much every aspect of life – is being eyed up by government or business for the application of AI.

The way machine learning consumes big data and produces predictions certainly suggests it can both grasp the enormity of the humanitarian challenge and provide a data-driven response.

But the nature of machine learning operations mean they will actually deepen some of the problems of humanitarianism, and introduce new ones of their own.

Exploring these questions requires a short detour into the concrete operations of machine learning, if we are to bypass the misinformation and mystification that attaches to the term AI.

Machine learning, then, is not just a method but a machinic philosophy where abstract calculation is understood to access a truth that is seen as superior to the sense-making of ordinary perception.

This constructs risk in the same way that Twitter determines trending topics, allocating and withholding resources in a way that algorithmically demarcates the deserving and the undeserving.

By embedding the logic of the powerful to determine what happens to people at the periphery, humanitarian AI becomes a neocolonial mechanism that acts in lieu of direct control.

This is as important for humanitarian AI as for AI generally – for, if an alternative technics is not mobilised by approaches such as people’s councils, the next generation of humanitarian scandals will be driven by AI.

Digital Humanitarians

“Patrick Meier is a passionate evangelist for the power of big data to help us respond to natural disasters and other crises.

“An insider’s guide to the humanitarian data revolution, seen through the eyes of a thought leader, scholar, and expert practitioner on the front lines of a

Meier’s new book, Digital Humanitarians, has the potential to relieve suffering by showing activists, citizens, and technologists how to use everything from satellite imagery to big data techniques and social media to save lives in natural disasters and other crises that require humanitarian response.

“Since it became possible for nearly anyone with a cell phone or an internet connection to send data, photos and other information around the world with a few key strokes, we’ve seen a number of books attempt to

Dan McQuillan is a Lecturer in Creative &

Prior to academia he worked as Amnesty International's Director of E-communications.

Digital humanitarians, big data and disaster response

The information overflow that occurs in the wake of a disaster can paralyze humanitarian response efforts.

Computers, mobile phones, social media, mainstream news, earth-based sensors, humanitarian drones, and orbiting satellites generate vast volumes of data during major disasters.

My new book, Digital Humanitarians, charts the sudden and spectacular rise of these “Digital Jedis” by sharing their remarkable, real-life stories, highlighting how their humanity coupled with innovative Big Data solutions has changed how humanitarians will respond to disasters.

Within a week, thousands of digital volunteers from dozens of countries around the world had come together online to map the latest reports from Port-au-Prince;

Ten days into these digital humanitarian efforts, the head of FEMA, Craig Fugate, referred to these crisis maps as the most detailed and useful tools available to the humanitarian community.

Digital Jedis also use the MicroMappers platform to combine crowdsourcing with artificial intelligence to automatically identify relevant features in pictures, satellite imagery, and even aerial imagery.

While AIDR, MicroMappers, Verily, and TweetCred seem promising—I’m obviously biased since my team and I at QCRI are the ones developing these free and open source solutions—what is far more important are the scientific methodologies driving these platforms—namely human computing (crowdsourcing) and machine learning (artificial intelligence).

Perhaps the most important message of the book is that people would not use or develop these next generation humanitarian technologies if they lacked a desire to help others in need.

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