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Bias in data can have unintended consequences which propagate to the design, development, and deployment of machine learning models. In the financial services sector, this can result in discrimination from certain financial instruments and services. At the same time, data privacy is of paramount importance, and recent data breaches have seen reputational damage for large institutions. Presented in this paper is a trusted model-lifecycle management platform that attempts to ensure consumer data protection, anonymization, and fairness. Specifically, we examine how datasets can be reproduced using deep learning techniques to effectively retain important statistical features in datasets whilst simultaneously protecting data privacy and enabling safe and secure sharing of sensitive personal information beyond the current state-of-practice.

Nairobi is one of the fastest growing metropolitan cities and a major business and technology powerhouse in Africa. However, Nairobi currently lacks monitoring technologies to obtain reliable data on traffic and road infrastructure conditions. In this paper, we investigate the use of mobile crowdsourcing as means to gather and document Nairobi's road quality information. We first present the key findings of a city-wide road quality survey about the perception of existing road quality conditions in Nairobi. Based on the survey's findings, we then developed a mobile crowdsourcing application, called CommuniSense, to collect road quality data. The application serves as a tool for users to locate, describe, and photograph road hazards. We tested our application through a two-week field study amongst 30 participants to document various forms of road hazards from different areas in Nairobi. To verify the authenticity of user-contributed reports from our field study, we proposed to use online crowdsourcing using Amazon's Mechanical Turk (MTurk) to verify whether submitted reports indeed depict road hazards. We found 92% of user-submitted reports to match the MTurkers judgements. While our prototype was designed and tested on a specific city, our methodology is applicable to other developing cities., Comment: In Proceedings of 17th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2015)

According to the UN-HABITAT, the city of Nairobi loses half a million USD daily due to congestion on roads designed for a city 10 times smaller. Therefore, there is a great need for traffic management and awareness solutions. Many existing solutions are unsuitable for cities like Nairobi due to economic constraints, dynamic events, uncertainty, and poor infrastructure. Recently, a novel approach called Frugal Innovation has been adopted at IBM Tokyo Research. The approach combines very low quality images (VLQI) captured by existing low-cost cameras with network flow algorithms to accurately estimate traffic flow. We extend their work to develop a mobile app, called Twende-Twende, that provides drivers with real-time traffic information and suggested routes. We incorporate locally relevant context (such as references to landmarks) to predict congestion and create traffic awareness. We deployed the app and evaluated its effectiveness, accuracy and usability. Our initial evaluation indicates that the app enhances the driving experience and can be deployed in other developing countries.

According to UN-HABITAT, the city of Nairobi loses half a million USD daily due to congestion on roads designed for a city 10 times smaller. To address the traffic congestion problem in Nairobi, we develop a platform called Traffic 411 that provides drivers with real-time traffic and routing information. Traffic 411 incorporates locally relevant context (such as references to landmarks) to predict congestion and create traffic awareness. Our work extends a novel approach called Frugal Innovation developed at the IBM Tokyo Research Lab (TRL), where Web cameras covering only 3.5% of the roads in Nairobi are used to estimate traffic conditions. We deployed the Traffic 411 platform in Nairobi and our initial evaluation indicates that Traffic 411 enhances the driving experience and can be deployed in similar cities.