AI News, BOOK REVIEW: 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.

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.

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.

Dan McQuillan is a Lecturer in Creative &

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

How AI, Twitter and digital volunteers are transforming humanitarian disaster response

The following day Patrick Meier at the Qatar Computer Research Institute (QCRI) received a call from the UN Office for the Coordination of Humanitarian Affairs (OCHA) asking him to help deal with the digital fallout -- the thousands of tweets, photos and videos that were being posted on the web containing potentially valuable information about the disaster.

From there the tweets were uploaded to the TweetClicker, and those with images filtered into the ImageClicker to be analysed and tagged depending on the type of information they contained -- infrastructure damage and requests for help, for example -- so they could be distributed to the appropriate agencies.

This was the first test of the Clickers in a real-life disaster situation, and Meier has outlined in a blog post some of the glitches he's already come across -- how the pre-processing filters are supposed to automatically upload the relevant tweets directly to the Clickers, but currently can't, and how the VideoClicker and TranslateClicker would have been really useful, but are still in development.

The effect of Haiti Haiti was a turning point not because of the strategic, successful deployment of digital tools and analysis, but because people's social media and technology use had matured to the point where there was masses of relevant, accessible user-generated data, which for the most part was bewildering to agencies attempting to make sense of it.

With Haitians relying on SMS to communicate in the midst of the crisis, mobile phone charging stations sprung up among the Port au Prince rubble and much of the user-generated data was gathered from thousands of text messages.

The problem for the American Red Cross, says Gloria Huang from the organisation's communications team, was that while they were prepared to broadcast information about the disaster and what the agency was doing through blogs and social media, what they were not prepared for was influx of posts and messages from people who were suffering, or who knew people who were suffering.

"[They] would say things like 'please come help get my cousin out of the rubble in Haiti' or 'there's a lot of need for help here and no-one's helping these people' -- information that was really helpful to people on the ground, but we being a two-person team sitting in the communications department in Washington DC, we didn't have a clear way of dealing with that information, or being able to provide it to the actual search and rescue teams on the ground.

The deployment in Libya involved an effort on behalf of the volunteers unlike any other, requiring people to monitor social media, to geo-locate and verify messages 24 hours a day for a full month.

Applying advanced computing to humanitarian challenges Even before Libya, Meier realised that to lift some of the weight off the volunteers' shoulders, the solution was to develop microtasking tools, but Ushahidi did not have the bandwidth to develop the kind of advanced technology that was required.

It's an open-source tool relying on both human and machine computing, allowing human users to train algorithms to automatically classify tweets and determine whether or not they are relevant to a particular disaster.

And what we want to do with this and other platforms is really to empower our digital humanitarian volunteers to be able to do what they do hundred times better and 100 times faster and with 100 times better user experience as well, so that people don't burn out."

Whether it's purposefully doctored photos, rumours and speculation or simply people getting the wrong end of the stick, in the midst of all the information relating to a disaster, there will always be red herrings and false information amid crowd-sourced data.

Crane won the challenge, which offered $40,000 to the individual or team that could find the correct location of 10 red weather balloons placed discreetly across the US, using social media.

The authors of the study used information forensics to examine the user profiles (how many followers, how many times listed, etc) and tweet content (length, use of punctuation, emoticons and hashtags) of tweets containing links to confirmed false image URLs to draw up a list of classifiers.

Facebook's terms of service mean it's harder to filter public updates, Foursquare isn't as international, or really used by people during disasters, and you can't access metadata for the pictures on Instagram unless they are also posted as a geotagged tweet -- although even that doesn't guarantee accuracy.

"We started looking at what that meant for the services we provided on a regular basis, for the disaster situations, going forward knowing that kind of information would be coming in and knowing that this is the way people would talk to us now as an organisation.

Similarly, the communications team trained staff and volunteers to converse with people using social media, so that when people reached out to them during a disaster they were able to engage with in similar ways over Facebook and Twitter as they would usually do in real-life disaster shelters.

"If they made an impact you want to make that public, you want things coming out from the UN Twitter handles, you want stuff going out on blog posts, you want media or somebody to pick it up, because you want the volunteers to see that the organisation respected and appreciated the work that they did."

"What I find interesting is with new technologies, the big question is, is it reducing those barriers to entry into the humanitarian space, where other organisations, especially those augmented with a lot of virtual support, can start taking on roles that were traditionally mandated to other groups?"

"We want to be able to build this capacity across the country, so that next time a disaster happens, the person whose backyard the disaster has struck, that person can mobilise their own digital volunteers, and be able to respond and engage with their community on the level that we've managed to do here on the national level."

"That's why I wanted to bring MicroMappers in -- to democratise digital humanitarian volunteering, to lower the barriers to entry, so anybody who knows how to get online and use a mouse and speaks English or another language, can actually be a digital humanitarian volunteer,"

Maybe one day artificial intelligence will be so advanced that once again there will be no need for unskilled volunteers, but for now human computing is a huge part of coordinating humanitarian response, and the good news for the agencies and for us is that any human with access to a computer can get involved.

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