AI and the path to envelopment
Knowledge as a first step towards the responsible regulation and use of AI-powered machines
Bibliographic Data
| ID | 20396708 |
|---|---|
| Authors | Scott Robbins (0000-0002-5338-295X, Delft University of Technology, corresponding author) |
| Year | 2020 |
| Volume | 35 |
| Issue | 2 |
| Pages | 391-400 |
| Publication date | 2020-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | AI & Society (JOURNAL) |
| Journal identifiers | ISSN: 0951-5666 • E-ISSN: 1435-5655 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00146-019-00891-1 |
| OpenAlex | W2936395433 |
| Language | EN |
| Citations received | 13 |
| References cited | 14 |
With Artificial Intelligence (AI) entering our lives in novel ways—both known and unknown to us—there is both the enhancement of existing ethical issues associated with AI as well as the rise of new ethical issues. There is much focus on opening up the ‘black box’ of modern machine-learning algorithms to understand the reasoning behind their decisions—especially morally salient decisions. However, some applications of AI which are no doubt beneficial to society rely upon these black boxes. Rather than requiring algorithms to be transparent we should focus on constraining AI and those machines powered by AI within microenvironments—both physical and virtual—which allow these machines to realize their function whilst preventing harm to humans. In the field of robotics this is called ‘envelopment’. However, to put an ‘envelope’ around AI-powered machines we need to know some basic things about them which we are often in the dark about. The properties we need to know are the: training data, inputs, functions, outputs, and boundaries. This knowledge is a necessary first step towards the envelopment of AI-powered machines. It is only with this knowledge that we can responsibly regulate, use, and live in a world populated by these machines
Applications of artificial intelligence · Data science · Harm · Machine learning · Political science · Robot · Robotics · Salient · Artificial Intelligence in Healthcare and Education · Computer Science · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI · Law · Mathematics · Artificial Intelligence
Managing the tension between opposing effects of explainability of artificial intelligence
Counter-Terrorism
Recommender Systems
Development of the potential of the digital economy of Russian regions through artificial intelligence humanisation
Ethical, Legal and Political Challenges of Artificial Intelligence
Framing the effects of machine learning on science
What machines shouldn’t do
A neo-aristotelian perspective on the need for artificial moral agents (AMAs)
Towards an effective transnational regulation of AI
Making moral machines
Developing safer AI–concepts from economics to the rescue
The promises and challenges of addressing artificial intelligence with human rights
Sustainable AI
Robots should be slaves
Computer systems
A survey on deep learning in medical image analysis
Meaningful Human Control over Autonomous Systems
Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation
The Black Box Society
Children of the Fourth Revolution
The Value of Transparency
| Unique citing works | 13 |
|---|---|
| Citations per year | 2.6 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 12 |