Dr. Ayanna Howard

Professor, Georgia Institute of Technology

Tackling the Human Bias in AI

People tend to overtrust sophisticated computing devices, including AI systems. As these systems become more fully interactive with humans during the performance of day-to-day activities, the role of bias in these human-AI interaction scenarios must be more carefully investigated.  Bias, as a feature of human life, has often been encoded in and can manifest itself through AI algorithms, which humans then take guidance from, resulting in the phenomenon of excessive trust. Bias further impacts this potential risk for trust, or overtrust, in that these systems are learning by mimicking our own thinking processes, inheriting our own implicit gender and racial biases, for example. Consequently, the propensity for trust and the potential of bias may have a direct impact on the overall quality of the interaction between humans and machines, whether the interaction is in the domains of healthcare, job-placement, or other high-impact life scenarios. In this talk, we will discuss this phenomenon of integrated trust and bias through the lens of AI systems that interact with people in scenarios that are realizable in the near-term.

Dr. Milind Tambe

Professor, Harvard University and Director "AI for Social Good" at Google Research India

AI for Public Health and Conservation: Learning and Planning in the Data-to-Deployment Pipeline

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. We focus on the problems of public health and wildlife conservation, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present our deployments from around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact. Achieving social impact in these domains often requires methodological advances; we will highlight key research advances in topics such as computational game theory, multi-armed bandits and influence maximization in social networks for addressing challenges in public health and conservation. In pushing this research agenda, we believe AI can indeed play an important role in fighting social injustice and improving society.

Dr. Maria De-Arteaga

Assistant Professor, University of Texas Austin

A Case for Humans in the Loop

The increased use of algorithmic predictions in sensitive domains has been accompanied by both enthusiasm and concern. To understand the opportunities and risks of these technologies, it is key to study how experts alter their decisions when using such tools. In this work, we study the adoption of an algorithmic tool used to assist child maltreatment hotline screening decisions. We show that, while humans do make use of recommendations, they are less likely to adhere to the machine's recommendation when the score displayed is an incorrect estimate of risk. These results highlight the risks of full automation and the importance of designing decision pipelines that provide humans with autonomy.

Mr. Arbel Vigodny

Chief Operating Officer, Zzapp Malaria

AI in the service of malaria elimination in sub-Saharan Africa

Malaria is one of the most persistent public health problems, responsible for over 400,000 deaths per year. However, the basic tools to fight the disease have been around for over a century, and have successfully eliminated malaria from many countries around the world. In this talk I will discuss how Zzapp uses AI to overcome the challenges involved in the implementation of these tools in sub-Saharan Africa, where the disease burden is highest, and how we bring AI to the most remote and inaccessible regions.