Bias and Discrimination in Artificial Intelligence

2020 ◽  
pp. 256-283
Author(s):  
Jan C. Weyerer ◽  
Paul F. Langer
Keyword(s):  
Artificial Intelligence ◽  
Human Rights ◽  
Future Research ◽  
Practical Relevance ◽  
Research Directions ◽  
Research And Practice ◽  
The Future ◽  
Future Research Directions ◽  
Opening Up ◽  
Bias And Discrimination

Artificial intelligence (AI) has become an integral part of e-business and our lives, promising significant benefits to e-business companies and society. However, at the same time, AI systems in e-business may produce biased outcomes, leading to discrimination of minorities and violating human rights. Against this background, this chapter first describes the foundations of bias and discrimination in AI, highlighting its scientific and practical relevance, as well as describing its meaning, emergence, functioning, and impact in the context of e-business. Based on these foundations, the chapter further provides implications for research and practice on how to deal with AI-related bias and discrimination in the future, opening up future research directions as well as outlining solutions and recommendations for eliminating and preventing AI-related bias and discrimination in e-business.

Harambee: Pulling it All Together

2017 ◽  
Vol 119 (1) ◽  
pp. 1-6
Author(s):  
Adrienne D. Dixson ◽  
Gloria Ladson-Billings
Keyword(s):  
Teacher Educators ◽  
Culturally Relevant ◽  
Future Research ◽  
Special Issue ◽  
Research Directions ◽  
The Future ◽  
Future Research Directions ◽  
Common Understanding

The articles in this special issue represent both our attempt as editors to survey the field and provide some clarity for practitioners and teacher educators on fundamental ideas that frame CRP, not to limit its implementation or future research directions, but to ensure that as a community of educators and scholars, we share a common understanding of exactly what it means to be culturally relevant. The articles in this special issue provide both that clarity of the field, and vision for the future.

Integrating Odor Sensing in Robotics

2018 ◽  
pp. 224-243
Author(s):  
Alauddin Yousif Al-Omary
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Chemical Sensors ◽  
Mobile Robotics ◽  
Future Research ◽  
Research Directions ◽  
The Future ◽  
The Status ◽  
Future Research Directions ◽  
Technical Challenges

In this chapter, the benefit of equipping the robot with odor sensors is investigated. The chapter addresses the types of tasks the mobile robots can accomplish with the help of olfactory sensing capabilities, the technical challenges in mobile robot olfaction, the status of mobile robot olfaction. The chapter also addresses simple and complex electronic olfaction sensors used in mobile robotics, the challenge of using chemical sensors, the use of many types of algorithms for robot olfaction, and the future research directions in the field of mobile robot olfaction.

Future Research Directions in E-Tourism Studies

Big Data ◽  
2016 ◽  
pp. 2368-2387
Author(s):  
Hajime Eto
Keyword(s):  
Big Data ◽  
Future Research ◽  
Research Directions ◽  
Scientific Methods ◽  
Tourism Studies ◽  
The Future ◽  
Future Research Directions ◽  
And Behaviors ◽  
Public Voice

As this book has the limited numbers of chapters and pages, many important issues remain unanalyzed. This chapter picks up and roughly discusses some of them for the future analyses in more analytical ways. The focuses are placed on how to apply the data scientific methods to the analyses of public voice, claims and behaviors of tourists, customers and the general publics by using the big data already acquired and stored somewhere.

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

Artificial Intelligence

2019 ◽  
pp. 13-22
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Future Research ◽  
Human Intelligence ◽  
Research Directions ◽  
Intelligent Machines ◽  
History Of ◽  
Future Research Directions ◽  
New Research

Artificial intelligence is the science of creating intelligent machines. Human intelligence is comprised of numerous pieces of knowledge as well as processes for utilizing this knowledge to solve problems. Artificial intelligence seeks to emulate and surpass human intelligence in problem solving. Current research tends to be focused within narrow, well-defined domains, but new research is looking to expand this to create global intelligence. This chapter seeks to define the various fields that comprise artificial intelligence and look at the history of AI and suggest future research directions.

scholarly journals Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

2020 ◽  
Vol 9 (2) ◽  
pp. 21 ◽  
Author(s):  
Martins O. Osifeko ◽  
Gerhard P. Hancke ◽  
Adnan M. Abu-Mahfouz
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Energy Efficient ◽  
State Of The Art ◽  
Future Internet ◽  
Future Research ◽  
Research Directions ◽  
Efficient Data ◽  
Future Research Directions ◽  
Efficient Data Collection

Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard.

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