Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Human-Centered Artificial Intelligence: The Superlative Approach to Achieve Sustainable Development Goals in the Fourth Industrial Revolution
110
Zitationen
1
Autoren
2022
Jahr
Abstract
Artificial intelligence (AI) is currently being developed by large corporations, and governments all over the world are yearning for it. AI isn’t a futuristic concept; it is already here, and it is being implemented in a range of industries. Finance, national security, health care, criminal justice, transportation, and smart cities are all examples of this. There are countless examples of AI having a substantial impact on the world and complementing human abilities. However, due to the immense societal ramifications of these technologies, AI is on the verge of disrupting a host of industries, so the technique by which AI systems are created must be better understood. The goal of the study was to look at what it meant to be human-centred, how to create human-centred AI, and what considerations should be made for human-centred AI to achieve sustainability and the SDGs. Using a systematic literature review technique, the study discovered that a human-centred AI strategy strives to create and implement AI systems in ways that benefit mankind and serve their interests. The study also found that a human-in-the-loop concept should be used to develop procedures for creating human-centred AI, as well as other initiatives, such as the promotion of AI accountability, encouraging businesses to use autonomy wisely, to motivate businesses to be aware of human and algorithmic biases, to ensure that businesses prioritize customers, and form multicultural teams to tackle AI research. The study concluded with policy recommendations for human-centred AI to help accomplish the SDGs, including expanding government AI investments, addressing data and algorithm biases, and resolving data access issues, among other things.
Ähnliche Arbeiten
The global landscape of AI ethics guidelines
2019 · 4.617 Zit.
The Limitations of Deep Learning in Adversarial Settings
2016 · 3.876 Zit.
Trust in Automation: Designing for Appropriate Reliance
2004 · 3.432 Zit.
Fairness through awareness
2012 · 3.293 Zit.
Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer
1987 · 3.184 Zit.