Validation of a Framework for Bias Identification and Mitigation in Algorithmic Systems

Bias in algorithmic systems is a major cause of unfair and discriminatory decisions in the use of such systems. Cognitive bias is very likely to be reflected in algorithmic systems as humankind aims to map Human Intelligence (HI) to Artificial Intelligence (AI). We conducted an extensive literature review on the identification and mitigation of bias, leading to precise measures for project teams building AI systems. Moreover, we developed an awareness-raising framework for use as a guideline for project teams, addressing AI responsibility, AI fairness, and AI safety. The framework proposes measures in the form of checklists to identify and mitigate bias in algorithmic systems considering all steps during system design, implementation, and application. We validated the framework successfully in the context of industrial AI projects

Context-aware Intelligent Model Selection System

After more than 40 years of research in computational decision support, model selection and management is still one of the most crucial problems. With organisations facing turbulences in an environment that include constant changes in social, political, technical and economic challenges, the selection of appropriate models for decision support has become even more difficult. Most research efforts do not consider model selection itself as a major aspect of research nor do they reflect on context awareness. The paper explores the early use of Artificial Intelligence (AI) techniques to improve model selection and reviews modern Intelligent Decision Support Systems (IDSS). Since model selection is a central problem for decision makers we specifically analyse research on model selection and identify important characteristics. Based on this analysis we suggest a framework and architecture for a Context-aware Intelligent Model Selection System (CIMSS). The paper concludes with further suggestions for future research

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Sensory semantic user interfaces (SenSUI)

Rapid evolution of the World Wide Web with its underlying sources of data, knowledge, services and applications continually attempts to support a variety of users, with different backgrounds, requirements and capabilities. In such an environment, it is highly unlikely that a single user interface will prevail and be able to fulfill the requirements of each user adequately. Adaptive user interfaces are able to adapt information and application functionalities to the user context. In contrast, pervasive computing and sensor networks open new opportunities for context aware platforms, one that is able to improve user interface adaptation reacting to environmental and user sensors. Semantic web technologies and ontologies are able to capture sensor data and provide contextual information about the user, their actions, required applications and environment. This paper investigates the viability of an approach where semantic web technologies are used to maximize the efficacy of interface adaptation through the use of available ontology