AI for Social Good
7 July 2022
Leah Hawkins, RMIT University
Prof Milind Tambe, Harvard University and Director AI for Social Good at Google India
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You’re listening to the ARC Centre of Excellence for Automated Decision-Making and Society podcast. I’m Leah Hawkins, and in this episode, Professor Milind Tambe from Google Research India discusses the importance of the data to deployment pipeline, using AI for social good. Using examples of his research, Professor Tambe details two key studies that exemplify how AI and multi-agent systems can be utilised to overcome existing challenges in public health, conservation, public safety, and security.
To begin, Professor Tambe provides an overview of a study utilising algorithmic intervention to reduce rates of HIV in the homeless youth network of Los Angeles.
Prof Milind Tambe:
So, I’m going to talk to you today about the AI for Social Impact. For the past 15 years me and my research team have been focused on applying AI and multiagent systems research towards public health, conservation, and public safety and security, with the key challenge of how to optimize our limited intervention resources. And I’ll just get down right to some of the lessons that we have learned.
The first lesson is that achieving social impact and AI innovation go hand in hand. So a concrete example is work we’ve done which I’ll cover today in reducing HIV amongst youth experiencing homelessness in Los Angeles. We are living through one pandemic which is the Covid pandemic, but there are other pandemics, for example HIV.
Harnessing the social networks of these youth, we are able to show that our algorithms are far more effective in reducing HIV risk behaviours among these youth, compared to traditional approaches. What this work required was innovation in the area of social network algorithms and so-called bandit algorithms. With respect to conservation, we have large conservation areas to protect limited range of resources. A concrete example of work I’ll cover here, work we’ve done in Uganda and Cambodia, harnessing past poaching data, we are able to predict where poachers may set traps and for the past several years have been able to remove thousands of these traps. This has required advances in combining machine learning and game theory in so-called green security games. In the past we’ve contributed newer algorithms in game theory in the model called Stackelberg Security Games. This is for counterterrorism, and this was work done in collaboration with the US Coast guard for example, which will be using these algorithms we developed or with the Federal Air Marshals Service and other security agencies in the United States. S second lesson is all of the work I will talk about today, is in partnerships with non-governmental organizations, non-profits, and some government organizations. I’m often asked you know; how do you start this work on social impact? My answer is that the work we do is inspired by the work that’s tremendous work, that the non-profits are doing around the world, and our goal is to empower these non-profits to use AI tools and avoid being gatekeepers to AI technologies for social impact.
A third lesson is in doing this work on AI for social impact, we have to pay attention to the entire data to deployment pipeline. It’s not just about improving algorithms. So our work starts by immersing ourselves in the domain with the non-profit, trying to understand the kind of problems they face, the kind of data they have. Following that, a predictive model – a machine learning model – makes predictions of which of the cases the non-profits has are high risk versus low risk. If we had enough resources we would have intervened on all of the high-risk cases but we don’t, and so which cases to actually intervene on given limited resources? That’s the job of our multi-agent reasoning algorithms. And then finally, field testing and deployment is crucial because social impact itself is a key objective of our work. If we are not achieving social impact then it is not AI for social impact.
So start with this social work on social networks. This work is motivated by trying to prevent HIV amongst youth experiencing homelessness in Los Angeles. There’s 6000 youth who sleep on the streets of Los Angeles every night. The rates of HIV amongst this population is 10 times the rate of the normal house population. So homeless shelters will try to- they cannot obviously communicate with all six thousand youth- so they try to bring in some key peer leaders, educate them about HIV prevention and expect these peer leaders to talk to their friends, and their friends to talk to their friends, and in this way information to spread in their social networks. This is real face-to-face interaction. This is not happening for example, over Facebook now some of you may recognize that this is the classical problem of influence maximization in computer science. The input is a social network graph. We have to choose some K peer leader nodes in this graph, say 5 peer leader nodes or 50 peer leader nodes so as to maximize the expected number of influence nodes maximize the expected number of youth who know about HIV prevention. We assume here that information spreads in so-called independent cascade model and I’ll explain that to you next. So given some social networks remember, this is not over Facebook, this is the actual friendship network, this is an actual network of the youth in one of the shelters in Los Angeles friendship networks. And each number here- we’ve in order to protect the identity of these youth we’ve just given them a number that represents a single youth in this network. So if we pick one youth, let’s call them, let’s call this youth A. Then educate them about HIV prevention. Then in this independent cascade model we assume that there is friend B who will know about HIV prevention with a probability of 0.4 and then B will talk to C, their friend with a probability of 0.4. So C will also come to know about hiv prevention with a probability of 0.4. And so information is cascading in the network in this fashion. So if we are given a budget of five nodes, now we have to select which are the best five nodes, which are the best five youth to select in the network to maximize the spread of HIV information in this network. So is it this five, or is it some other five? That’s the key challenge. Once we select these youth then our social work colleagues are shown here in this picture will you know, there’s a day-long session they go through about HIV prevention and how to spread this message in the network, so that’s a picture of this education session in progress. So that’s the challenge that we face now.
There are standard techniques in computer science for doing influence maximization, but if we try to apply them directly in our work there are three key challenges that come together, and they arise because of lack of data and uncertainty. Now often in the domains we face in AI for social good, lack of data is actually a feature, it’s just the way it is. I’ve seen students who work with me who will complain that you know, they want to do data science but there’s not real clean data sets available in these kinds of domains that we work with, working with marginalized communities and so on. And that’s just the way it is. We have to work with the fact that we have limited data. And so one of the challenges as a result, is that we do not know the exact propagation probability like 0.4. We don’t have immersion in this domain which clearly shows it is hard to get those exact probabilities. Peer leaders themselves sometimes don’t show up, so we may say we want to select these five youth but these are youth under difficult circumstances, so some may- we may have selected a youth but that youth may not have a bus fare to come to the shelter to go through the session. They may send their friend for example, so we have to handle this peer leader no-shows. And thirdly, normally in computer science when this problem is studied, they assume that the whole social network is given as input, but in our domain we do not know the social network ahead of time. All we can do is ask a few of the youth who their friends are. We cannot ask it of the entire set of youth, so we may say you know, we can go to a shelter and in one day ask 20 youth about who their friends are and in this way uncover a small fraction of the social network, but that’s all we get and based on that we have to figure out who are the key influencers. So again, these are challenges that we just have to address in our domain.
So, for example, uncertainty over social propagation probability normally in influence maximization as we discussed. It is assumed that if we talk to a youth – C, then an adjacent youth, their friend D will get informed with the probability of 0.4 which is known ahead of time about HIV prevention in our domain. This probability is not known so we can say that we are sampling this from some distribution, but we may not even know the mean of this distribution. So therefore we can say the mean of the distribution lies within some interval. So now we are faced with the problem of influence maximization. When there’s uncertainty in the propagation probability and this problem we handle by this, basically this is a problem of robust influence maximization. We solve this problem as a game. So on the one hand our algorithm is trying to choose peer leaders to maximize the spread of influence, but we assume that nature is trying to choose parameter settings, is trying to choose those propagation probabilities to cause our algorithm to perform as worse as possible. And so for those who are familiar we’re basically just trying to solve this as a zero-sum game against nature, where we are trying to maximize the payoff, nature is trying to minimize the payoff, and the payoff is a measure of regret. A ratio of the outcome of the policy that we have chosen, to what we could have chosen optimally had we known nature’s parameter settings in advance. So I’m not going to go into details, but basically by solving these kinds of algorithms we can get a robust policy.
The second problem I’ll discuss is sampling of social networks. You know, we could imagine sending our social work colleagues to a homeless shelter sitting, they can sit there, they can sit there for days, they can talk to all of the youth and kind of figure out the entire social network within a shelter as to who’s who’s friends with whom. This is very costly and if you want this work to be applied in different cities in Los Angeles, San Francisco, etc, then you know, this is not going to scale up. We need a fast, simple way for this technique to work. And to that end we assume that we can only query, let’s say 15 percent of the nodes, we can go get our social work colleagues to go to a homeless shelter, ask questions about say 15 of the youth that may show up in the center that day, that’s it. And they’ll ask a question who are your top five friends, something like that, just note that down. That allows them to essentially get the understanding of the social network. The key here for us is the right sampling algorithm. You know, who do you sample? That’s the secret sauce that we bring to the table. And so the idea is that once we sample exactly this small fraction of the population, then we output the K peer leader nodes and at the same time we guarantee that the performance of our algorithm, even though we have chosen peer leaders from a small sample of the network, is similar to the optimal possible if we knew the full network ahead of time. And this is a done by sampling nodes, randomly estimating sizes of their communities, and then choosing seeds from largest communities.
All right so, having addressed some of these challenges, we built a system that I’ll refer to as sampling healer. Sampling, because it’s sampling the network. It is generating a robust policy and what it does is it selects a set of peer leaders. It says: bring in these five leaders, these five youth, and then after seeing who’s present and who’s absent, it’ll say bring in the next five youth, and so forth. So at this time, it was time to do a pilot test with the sampling healer. In each, there are three arms to this pilot test sampling healer which is our sample network. This is our actual algorithm, we recruited 60. Youth healer is just like sampling healer except that it knows the full network, it doesn’t have just the sample of the network, and degree centrality is the traditional approach to bring in the most popular youth. It makes sense right, to bring in the youth with the highest degree, the higher degree centrality- again about 60 youth in that. And we then selected 12 peer leaders in each case as per the recommendation of that algorithm, and this is an actual picture of our social work colleague educating the youth who were identified as peer leaders by these algorithms.
There were obviously 12 different peer leaders in each of these pilot tests, and then the question was at the end of a month how many of the non-peer leaders, the ones we did not bring in for the education session, got informed about HIV? And what we find, is after a month with degree centrality 25 percent of the non-peer leaders got informed about HIV. With healers 75 percent of the non-peer leaders got informed about HIV. So our influence maximization algorithm is clearly far more effective in spreading HIV information, compared to traditional approaches. With sampling healer we seem to be even higher than healer, and that just could be this particular sample that was chosen, because we are doing a small pilot test at this point. But given the performance of sampling healer we decided to now compare it on a larger sample. So in this actual final test we are not only interested in the actual ‘how much information was spread’, but whether there was actual change in behavior. And this is work done with professor Eric Rice. Of social work as far as we know, this is the first large-scale application of influence maximization in social networks for public health, and this was done in collaboration with three non-profits: My French Place, Los Angeles LGBT center, and Safe Place for Youth. We recruited 750 youth in the study, 215 each of the three arms. On the first term was our sampling healer algorithm, the second was degree centrality, the third was no intervention at all. In each case we recruited peer leaders as per the recommendation of the algorithm. 15 of the youth were chosen as peer leaders in each case, and then we wanted to understand what the change in behavior after a month was, and then after three months. And here’s what we found at the end of one month in terms of reduction in condomless anal sex, which is one of the HIV risk behaviors. With sampling healer there was more than 30 percent reduction in condomless anal sex. With degree centrality and control group, there was no reduction at all at the end of one month. At the end of three months we see that degree centrality begins to catch up to sampling healer but still, sampling healer is still better. What’s important here is that the speed at which sampling healer is able to cause changes in behavior is important because this is a HIV risk behavior. So having this behavior change occur faster is important, and also because youth in this community come and go. And therefore, having this behavior change happen faster is important.
Following on, Prof Tambe provides an example of successful AI intervention in wildlife conservation, using technology to assist National Park rangers in Uganda with their patrols and poaching prevention.
all over the world in conservation areas there are wonderful wildlife, but their threats to the wildlife snares are traps by the thousands that are placed in order to maim and kill wildlife. So you can imagine the job of a ranger to search these forest areas trying to look for traps, and you can see that this is a very difficult task if you have a very small number of rangers who are in charge of thousands of square kilometres of National Parks that they have to protect. So for example this is the Queen Elizabeth National Park in Uganda where we’ve done a lot of work, and so in order to figure out where to send rangers, you can divide up the park into a one kilometre by one kilometre grid square. Each grid square is so-called target areas where there is water. You can imagine there are more animals, so this is a more important area for poachers. So we are trying to learn poacher response model at each target to figure out how they respond to our patrols. So based on that we can recommend patrolling strategies to these rangers. We have 14 years of data from Uganda when we started this work. So for example it has ranger patrol frequency per each grid square, animal density per each grid square, this obviously we know from maps- what is the distance to nearby rivers, roads, villages, etc. And so based on these different features, we are trying to predict the probability of finding a trap per one kilometre grid square of the park. We did do this prediction using an ensemble of classifiers. In the interest of time I’m going to skip the details of that we produced all kinds of results in the lab, to show recall precision this, that, etc, it doesn’t matter. Our colleagues in the field are not convinced they wanted to do an actual field test, so we selected two nine square kilos. This is our first pilot test- two nine square kilometre areas in Queen Elizabeth National Park, that were infrequently patrolled. These are shown by the green dots. The red dots are where previously a lot of snare traps had been found by the rangers. We are asking the rangers to go to completely new areas, very infrequently patrolled there. One found any snares there and we told them once you patrol them, our model predicts you are going to find snares there. And so the rangers patrol for a month and every day they would report back an email as to what they saw. Initially there was nothing but then they found a poached elephant with its tusks cut off and soon thereafter a whole elephant snare roll as pictured here, that they removed. So poachers were active in the area. They were killing elephant, but before they could kill the next set of elephants, we were able to remove this elephant snare role, hoping that this would or has saved lives of elephants. Then we had 10 antelope snares that were found and removed. So this pilot test was seen to be a success and at this point there was a longer test that was done in Queen Elizabeth and Murchison Falls National Parks in Uganda, and Sripak wildlife sanctuary in Cambodia. In each park we selected 24 areas in frequently patrolled areas of nine square kilometre each. We predicted some of these to be high risk- more snares will be found, some of these to be low risk- less snares would be found. So basically we are not saying go to you know, infrequently patrolled areas and you’re going to find snares. We are saying even there we can discriminate there are some areas at a high risk and some are low risk, and then rangers went out for six months and came back with what they found, and where we predicted high risk indeed more snares were found compared to where we predicted low risk. And this was seen across all of these parks today. What we find in Sripak wildlife sanctuary in Cambodia, these are pictures of the snares that were found because of our predictions. The number of snare captures jumped from 101 to 521, more than five fold increase once our system, which is called Pause, started getting used, and is now in regular use in 2021.
Just in March they found what 1000 snares in the park, and so this is just showing that these techniques that you are all learning about can be very useful for the fight against poaching. And so today Pazuse has gone global, we have made integrated laws with the platform called Smart that is a collaboration with WWF, WCS and 13 other wildlife conservation agencies. Smart is what is active across the globe in hundreds of national parks by rangers, and so by make integrating Pause with smart we’ve made Pause available to all of these rangers. We are testing Pause out in all of the many national parks, and so this very exciting time in terms of Pause getting used in order to make predictions to save wildlife.
So I’m going to just say one more thing which is that there are drones that get used for conservation and we’ve built image recognition to automatically detect poachers in these images. Human beings who shouldn’t be in the park, who are there illegally, and wildlife. So with that I’m going to stop, I’m going to skip over some other details and come to the last part which is key lessons. I wanted to repeat achieving social impact an AI innovation goes hand in hand. We don’t have to sacrifice AI innovation when we try to work on social impact. We want to ultimately empower non-profits to use AI tools and avoid being gatekeepers to AI technology for social impact. Non-profits is where we are going to get our AI for social impact problems. It’s a partnership we have in AI for social impact. We have to look at the whole data to deployment pipeline. It’s no use just trying to concentrate on improving algorithms, because that doesn’t achieve social impact. It’s important to step out of the lab and into the field. I can give you many instances where sitting in the lab we had no clue what was going on and once we went into the field is when the problem became clearer. To embrace interdisciplinary work, whether it’s social work with conservation and others, and of course lack of data as I mentioned is the norm, it’s a feature of this problem set and it should be part of our project strategy
We’ll now hear some questions from the audience…
Thanks Milinde for the talk, that’s really inspiring. I’ve got a more general sort of question around working with NGO’s. You know, seemingly AI and NGO’s are as far as you can imagine on some spectrum of technology use, and how do you sort of bridge this gap, and what’s your experience with engaging with them, talking the same language as they talk, and in general do you sort of engage in some process of co-development of these models with these NGO’s or what’s the process? How do you go about it?
Prof Milind Tambe:
So that’s a beautiful question. It’s a deep partnership that has to be developed. There has to be trust, there has to be time spent in building up a relationship, and for example with these wildlife conservation organizations it took me years just to initially, you know, I was sort of thinking well I’ll be sitting in my office in the US, I’ll be sending a few emails and then they’ll just say great! We’ll just send you our data, and now let’s just work together. That’s not the way things happen. So this group in Uganda kept saying that you have to come down here and then we can talk and so finally I was saying you know, I was sort of reluctant but then I just flew down to Kampala City, I met with officials there, I went to Queen Elizabeth actually. I went to Murchison falls national park in that trip and met with people in the park and then you know, there was some kind of an understanding, some kind of a relationship that got built. And then after that I received data from Uganda, same with Cambodia too. I mean you know WWF Cambodia, they’re very interested in working, but they wanted us to see actual situation on the ground. And so we went there, we visited with them and after that a very nice relationship got built up, and then we have been working with them throughout. So yeah I agree it takes a little time to build up that trust, build up that relationship, but the end result I feel is very powerful and it’s worth it. And you know for AI researchers I sometimes have to sort of drag people out of the lab to say hey you know, you can’t just assume that people will just come to the lab and say here USB key all my data, process everything, give me back everything, and then I’ll just use it, and you know wonderful. That’s not the way things will happen. And it’s true I mean, they also want to make sure you understand the context in which they’re working and going to Cambodia made us appreciate what a difficult job – I mean the pay for the rangers is low, poachers are shooting at you, the place is like, well I stayed there in the park for one or two nights and it’s just like they gave us the best accommodation and even then it was, I can’t imagine how people can stay there for six months at a time. They’re away from their families. So it gave us a deeper appreciation of the hard work that goes in and when they say something that you know, saying that this patrol is hard, we will change our algorithm to make sure that the patrols are better than you know, what we had given you rather than just saying to just do it. You know, something like that. So yeah, it’s a very good question thank you.
And finally, Prof Tambe discusses appropriate expectations of how future AI systems might be built to incorporate societal implications or human-centred behaviour in their decision-making processes.
Prof Milind Tambe:
I think one of the things that I feel is a recognition that the entire data to deployment pipeline is important because when we talk about hot research areas in AI we often focus on just algorithmic portion, you know. This reinforcement learning is now hot, versus something else being hot etc. And so at least for AI for social impact, my view is that the idea that we actually have to see impact in the real world is something that will become more important. What this means is that understanding how to efficiently measure impact, how to actually do clever experiments to measure impact, because these are things like in public health there’s a whole field called implementation science as you may know, whose job it is to actually measure impact and how to actually do clever experiments. So you can see differences, you know does something work in Australia may not work the same way in Los Angeles, in India, or something like that. So these are all questions of culture context impact. So these kinds of things we often say ‘oh that’s just like you know, somebody doing experiments in the field’, but that’s science. I mean that’s like testing an AI algorithm versus some other algorithm understanding. Why something worked, why didn’t it work, all these sorts of things. So that’s on the experimental side. And on the data side I mean there is sort of this idea that there’s so much data like we are drowning in data, everybody you know, but in our domains this is not true, and so we have to come up with all these clever techniques of sampling social networks. And you know this restless bandit, because we can’t quite figure out who’s in what state and all of these kinds of things so these are all outcomes of the fact that we don’t have adequate data, and we have to be sensitive and selective and all these kinds of things about data. So at least in AI for social impact I feel paying attention to the whole pipeline will be an important next step and I think I just feel like there needs to be, and there will be, a greater recognition that we want to bring AI’s benefits to the large sections of the world, you know, who have not benefited from AI benefits, have largely been confined to a smaller section of the planet. And in order to actually show that impact in the field, and not just write papers about it, we have to actually engage in experiments on the ground and measure impact, and deal with the fact that the data are not there and all these kinds of wonderful things. I think all of these are going to become important. I feel they should become important. Hopeful they’re going to be important.
Professor Tambe joined us for this event as part of the ADM+S Tech Talks series, bringing together leading researchers and industry experts in the ADM field, to discuss the impacts and opportunities of technological advancements.
You can find similar ADM+S Tech Talks on our YouTube channel. Thanks for listening!