Could an AI decision aid improve housing systems that assist homeless youth? There are nearly 2 million homeless youth in the United States each year. Coordinated entry systems are being used to provide homeless youth with housing assistance across the nation. Despite these efforts, the number of homeless youth still homeless and unstably housed on the street remains very high. Motivated by this fact, we initiate a first study to create AI decision aids for improving the current housing systems for homeless youth. First, we determine whether the current rubric for prioritizing youth for housing assistance can be used to predict youth's homelessness status after receiving housing assistance. We then consider building better AI decision aids and predictive models using other components of the rubric. We believe there is much potential for effective human-machine collaboration in the context of housing allocation. We plan to work with HUD and local communities to develop such systems in the future.

Researchers, including two of Indian-origin, have developed an artificial intelligence (AI) algorithm which can help predict susceptibility to substance use disorder among young homeless individuals. The study, presented at the International Joint Conference on Artificial Intelligence, revealed that this algorithm can suggest personalized rehabilitation programs for highly susceptible homeless youth. "Proactive prevention of substance use disorder among homeless youth is much more desirable than reactive mitigation strategies such as medical treatments for the disorder and other related interventions," said study author Amulya Yadav from the Penn State University in the US. For the results, the research team built the model using a dataset collected from approximately 1,400 homeless youth, ages 18 to 26, in six US states. The dataset was collected by the Research, Education, and Advocacy Co-Lab for Youth Stability and Thriving (REALIST), which includes Anamika Barman-Adhikari, assistant professor at the University of Denver and co-author of the paper.

New York: Researchers, including two of Indian-origin, have developed an artificial intelligence (AI) algorithm which can help predict susceptibility to substance use disorder among young homeless individuals. The study, presented at the International Joint Conference on Artificial Intelligence, revealed that this algorithm can suggest personalised rehabilitation programmes for highly susceptible homeless youth. "Proactive prevention of substance use disorder among homeless youth is much more desirable than reactive mitigation strategies such as medical treatments for the disorder and other related interventions," said study author Amulya Yadav from the Penn State University in the US. For the results, the research team built the model using a dataset collected from approximately 1,400 homeless youth, ages 18 to 26, in six US states. The dataset was collected by the Research, Education and Advocacy Co-Lab for Youth Stability and Thriving (REALYST), which includes Anamika Barman-Adhikari, assistant professor at the University of Denver and co-author of the paper.

The primary method of soliciting donations will be via podcast. Fils-Aimé will join longtime New York games journalist Harold Goldberg in a seven-episode series, Talking Games with Reggie and Harold, which will feature interns being helped by the program, as well as high-profile executives and developers in the video game industry. Goldberg is a freelancer who has written previously for The Washington Post.

According to the CDC, the suicide rate for individuals 10-24 years old has increased 56% between 2007 and 2017. In comparison to the general population, more than half of people experiencing homelessness have had thoughts of suicide or have attempted suicide, the National Health Care for the Homeless Council reported. Phebe Vayanos, assistant professor of Industrial and Systems Engineering and Computer Science at the USC Viterbi School of Engineering has been enlisting the help of a powerful ally--artificial intelligence--to help mitigate the risk of suicide. "In this research, we wanted to find ways to mitigate suicidal ideation and death among youth. Our idea was to leverage real-life social network information to build a support network of strategically positioned individuals that can'watch-out' for their friends and refer them to help as needed," Vayanos said.

This thesis proposal makes the following technical contributions: (i) we provide a definition of the Dynamic Influence Maximization Under Uncertainty (or DIME) problem, which models the problem faced by homeless shelters accurately; (ii) we propose a novel Partially Observable Markov Decision Process (POMDP) model for solving the DIME problem; (iii) we design two scalable POMDP algorithms (PSINET and HEALER) for solving the DIME problem, since conventional POMDP solvers fail to scale up to sizes of interest; and (iv) we test our algorithms effectiveness in the real world by conducting a pilot study with actual homeless youth in Los Angeles. The success of this pilot (as explained later) shows the promise of using influence maximization for social good on a larger scale.

The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is enormous, particularly in the areas of healthcare, conservation and public safety and security. Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these real-world problems are characterized by uncertainties about social network structure and influence models, and previous research in AI fails to sufficiently address these uncertainties. This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the design and development of new influence maximization algorithms which can handle various uncertainties that commonly exist in real-world social networks. These algorithms utilize techniques from sequential planning problems and social network theory to develop new kinds of AI algorithms. Further, this thesis also demonstrates the real-world impact of these algorithms by describing their deployment in three pilot studies to spread awareness about HIV among actual homeless youth in Los Angeles. This represents one of the first-ever deployments of computer science based influence maximization algorithms in this domain. Our results show that our AI algorithms improved upon the state-of-the-art by 160% in the real-world. We discuss research and implementation challenges faced in deploying these algorithms, and lessons that can be gleaned for future deployment of such algorithms. The positive results from these deployments illustrate the enormous potential of AI in addressing societally relevant problems.

When it comes to fighting substance abuse, research suggests the company you keep can make the difference between recovery and relapse. So, while group intervention programs can play an important role in preventing substance abuse, especially in at-risk populations such as homeless youth, they can also inadvertently expose participants to negative behaviors. Now, researchers from the USC Center for Artificial Intelligence in Society have created an algorithm that sorts intervention program participants - who are voluntarily working on recovery - into smaller groups, or subgroups, in a way that maintains helpful social connections and breaks social connections that could be detrimental to recovery. "We know that substance abuse is highly affected by social influence; in other words, who you are friends with," says Aida Rahmattalabi, a USC computer science graduate student and lead author of the study. "In order to improve effectiveness of interventions, you need to know how people will influence each other in a group."

Homeless youth are prone to human immunodeficiency virus (HIV) due to their engagement in high-risk behavior such as unprotected sex, sex under influence of drugs, and so on. Many nonprofit agencies conduct interventions to educate and train a select group of homeless youth about HIV prevention and treatment practices and rely on word-of-mouth spread of information through their one single social network Previous work in strategic selection of intervention participants does not handle uncertainties in the social networks' structure and evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed PSINET, a decision-support system to aid the agencies in this task. PSINET includes the following key novelties: (1) it handles uncertainties in network structure and evolving network state; (2) it addresses these uncertainties by using POMDPs in influence maximization; and (3) it provides algorithmic advances to allow high-quality approximate solutions for such POMDPs. Simulations show that PSINET achieves around 60 percent more information spread over the current state of the art.

This issue features expanded versions of articles selected from the 2015 AAAI Conference on Innovative Applications of Artificial Intelligence held in Austin, Texas. We present a selection of three articles describing deployed applications plus two more articles that discuss work on emerging applications. Since then, we have seen examples of AI applied to domains as varied as medicine, education, manufacturing, transportation, user modeling, military operations, and citizen science. The 2015 conference continued the tradition with a selection of 6 deployed applications describing systems in use by their intended end users, 13 emerging applications describing works in progress, and three papers in a new category for challenge problems. In the first article, Activity Planning for a Lunar Orbital Mission, John Bresina describes a deployed application of current planning technology in the context of a NASA mission called LADEE (Lunar Atmospheric and Dust Environment Explorer).