This paper presents a simulator that is being developed to study the performance of certain types of vehicle navigation. The performance metric looks at a likelihood of accomplishing a task and the cost of the strategy – measuring both robustness and efficiency. We present results involving only autonomous control strategies, yet the simulator will be used to compare human performance in completing the same task.

There are multiple and even interacting dimensions along which shape representation schemes may be compared and contrasted. In this paper, we focus on the following ques- tion. Are the building blocks in a compositional model lo- calized in space (e.g. as in part based representations) or are they holistic simplifications (e.g. as in spectral representa- tions)? Existing shape representation schemes prefer one or the other. We propose a new shape representation paradigm that encompasses both choices.

We have adapted an expert system of medical diagnosis for use by low to mid-level health workers in remote and rural locations. Key to the successful deployment of this expert system is the rapid adaptation of the database and clinical interface for use in specific regions and by varying user skill.

Development is fundamentally about understanding people, their motivations, behaviors and reactions. We have two primary means of understanding people — observing what they do, and what they say. As the AI4D community has noted, people's increased use of mobile devices has led to a wealth of new data relevant to these topics. We are on the cusp of developing incredibly powerful tools that can help us understand how human beings migrate, transact and acquire wealth. This could have a large impact on how we determine policies and allocate resources. Most of this analysis has tended to focus on what people do — where they go, who they talk to, what they buy, etc. I argue that what people say is an equally rich source of development data, often containing information that cannot be obtained from people's actions, such as their needs, hopes and aspirations. Voice is the most natural form of communication, especially for people who speak a non-mainstream language, and/or have marginal literacy skills.  These are often exactly those populations who are most disenfranchised, and therefore most need their voices to be heard.

Although the various VA methods do Challenges for Global Health (Varmus et al. 2003) have not predict causes of deaths with vague symptoms as helped to reinforce the need for evidence-based global accurately as laboratory diagnostics can, verbal autopsy health priorities. Accurate health metrics and improved can predict causes of death with distinct symptoms with statistics can provide crucial decision-making inputs that some degree of accuracy (WHO 2007). For some areas of enable more efficient allocation of scarce financial the world verbal autopsies provide the only information resources towards the most pressing health needs (Murray about mortality currently available. Provided they can and Frenk 2008). Mortality statistics are a widely-used match or improve upon the accuracy of physician-coded resource for setting spending priorities, but out of 192 VA and expert algorithms, data-driven methods should be countries worldwide, only 23 have high-quality death used because they require less time from doctors or registration data, and 75 have no cause-specific mortality medical experts, and may provide valid reproducible fraction information at all (King and Lu 2008).

Community health worker programs provide healthcare to those living outside the financial and physical reach of the standard health infrastructure. These programs are particularly prevalent in low resource regions. Frequently such programs involve community health workers making household visits across a significant geographical area. We suggest that this problem can be posed as a formal routing and scheduling problem, and to use techniques developed from solving the travelling salesman problem with time windows. In addition, household visits can generate a series of future follow up visits, a feature not often handled in the combinatorial scheduling and routing literature. We present the basic problem and outline potential research directions.

This position paper describes how the nascent area of AI for development can learn from the challenges and successes of its parents: artificial intelligence and information and communication technologies for development (ICT4D). AI suffered from overly ambitious beginnings and years of stumbling before finding its footing, and achieving impactful ICT4D has been an equally challenging endeavor. We describe the history and challenges of both AI and ICT4D research, and present three broad suggestions for AI-for-development researchers: (1) that they spend as much time as possible with the kind of site or the organization they are hoping to impact; (2) that they be ambitious but humble in their goals and expectations; and (3) that they put AI in the service of existing, well-intented, competent development organizations.

A key challenge for policymakers in many developing countries is to decide which intervention or collection of interventions works best to improve learning outcomes in their schools. Our aim is to develop a causal model that explains student learning outcomes in terms of observable characteristics as well as conditions and processes difficult to observe directly. We start with a theoretical model based on the results of previous research, direct experience and experts’ knowledge in the field. This model is then refined through application of supervised learning methods to available data sets. Once calibrated with local data in a country, the model estimates the probability that a given intervention would affect learning outcomes.

The gender divide in the access to technology in developing economies makes gender characterization and automatic gender identification two of the most critical needs for improving cell phone-based services. Gender identification has been typically solved using voice or image processing.   However, such techniques cannot be applied to cell phone networks mostly due to privacy concerns. In this paper, we present a study aimed at characterizing and automatically identifying the gender of a cell phone user in a developing economy based on behavioral, social and mobility variables. Our contributions are twofold: (1) understanding the role that gender plays on phone usage, and (2) evaluating common machine learning approaches for gender identification. The analysis was carried out using the encrypted CDRs (Call Detail Records) of approximately 10,000 users from a developing economy, whose gender was known a priori. Our results indicate that behavioral and social variables, including the number of input/output calls and the in degree/out degree of the social network, reveal statistically significant differences between male and female callers. Finally, we propose a new gender identification algorithm that can achieve classification rates of up to 80% when the percentage of predicted instances is reduced.

There is a wealth of information on the Web about any number of topics. Many communities in developing regions are often interested in information relating to specific topics. For example, health workers are interested in specific medical information regarding epidemic diseases in their region while teachers and students are interested in educational information relating to their curriculum. This paper presents the design of Contextual Information Portals, searchable information portals that contain a vertical slice of the Web about arbitrary topics tailored to a specific context. Contextual portals are particularly useful for communities that lack Internet or Web access or in regions with very poor network connectivity. This paper outlines the design space for constructing contextual information portals and describes the key technical challenges involved. We have implemented a proof-of-concept of our ideas, and performed an initial evaluation on a variety of topics relating to epidemiology, agriculture, and education.