Just War Theory and Remote Military Technology: A Primer

Hallgarth, Matthew W.

doi:10.1093/acprof:oso/9780199926121.003.0002

Cited by 17 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Drones and the JWT Hallgarth (2013) mentions that the nucleus of the JWT is the question surrounding when we are justified in declaring war (jus ad bellum) and our conduct during wartime (jus in bello), with Bass (2004) contributing a recent addition to the theory: our conduct after the fact (jus post bellum). These three sections can be fleshed out as follows:…”

Section: Drones: a Multi-use Technologymentioning

confidence: 99%

“…Within this framework, Hallgarth (2013) observes that drones can help provide the evidence necessary to justify a declaration of war through its surveillance and reconnaissance capabilities. This surveillance can also help inform military officers of the level of proportionate force they should use in conflict.…”

Section: Jus Post Bellum: Once the Conflict Has Endedmentioning

confidence: 99%

See 1 more Smart Citation

Trustworthy AI Alone Is Not Enough.

Pérez y Madrid,

Wright

2023

View full text Add to dashboard Cite

Recent advances in AI through machine learning (ML) 1 and deep learning (DL) 2 techniques have arrived with a lot of momentum in society. In the 2010s there were several developments. The large amount of available data and the development of advanced model fitting algorithms based on artificial neural networks (ANN) 3 enabled the creation of advanced pattern recognition systems. First, the coveted task of handwritten digit recognition was achieved. The next stage was the recognition of shapes in images and faces, and from then its usefulness in other fields began to be tested, from industrial applications to others related to human behaviour.The great hope was and still is the possibility of solving problems considered reserved for human intelligence. During these years it has been possible to create models which we classify in two categories: GLOSSARY Artificial neural networks (ANNs): usually simply called neural networks (NN) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains (see also https://en.wikipedia.org/ wiki/Artificial_neural_network). Black box: A black box is a system that can only be known in terms of its inputs and outputs without knowing its inner workings. Its mode of operation is opaque, hence the name black. AI algorithms using artificial neural networks typically require a huge number of parameters. This makes human understanding of their operation impossible. For this reason, such algorithms are considered black boxes. Data Science: Data science is an interdisciplinary field focused on extracting knowledge from large data sets and applying that knowledge and insights to solve problems in a wide range of application domains. The field includes preparing data for analysis, framing data science problems, analysing data, developing data-driven solutions and presenting results to inform high-level decisions in a wide range of application domains. As such, it incorporates knowledge of computer science, statistics, mathematics, data visualisation, information visualisation, data integration, graphic design, complex systems, communication and business (See also https://en.wikipedia.org/wiki/Data_ science). Deep Learning (DL): Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Generative AI: is a type of artificial intelligence system capable of generating text, images or other media in response to instructions. Generative AI models learn the patterns and structure of their input training data and then generate new data with similar characteristics. Machine learning (ML): is a branch of AI that allows machines to learn from large amounts of data. Metadata: data that describe other data, e.g. their information content. They are analogous to indexes used in a library, such as author, title, publisher, date, etc. In the case of Spotify this could be singer, composer, style, etc.

show abstract

Section: Drones: a Multi-use Technologymentioning

confidence: 99%

Section: Jus Post Bellum: Once the Conflict Has Endedmentioning

confidence: 99%

Trustworthy AI Alone Is Not Enough.

Pérez y Madrid,

Wright

2023

View full text Add to dashboard Cite

show abstract

Drone Killings in Principle and in Practice

Dige

2017

Ethic Theory Moral Prac

View full text Add to dashboard Cite

It is a widely accepted claim that whether a given technology is being justly used in the real world is a separate question from moral issues intrinsic to technology. We should not blame the technology itself for immoral ways it happens to be used. There is obviously some truth to that. But I want to argue that what we see in the real world cases of drone killings is not merely an accidental or contingent use of drone technology. The real life use reflects to a large extent features that are inherent of the dominant drone systems that have been developed to date. What is being imagined "in principle" is thus to a large extent drone killings in dreamland. I use an historic example as a point of reference and departure: the debate over the lawfulness of nuclear weapons.

show abstract

The Vital Significance of Military Ethics

Stanar

2021

Journal of Military Ethics

View full text Add to dashboard Cite

Just War Theory and Remote Military Technology: A Primer

Cited by 17 publications

References 0 publications

Trustworthy AI Alone Is Not Enough.

Trustworthy AI Alone Is Not Enough.

Drone Killings in Principle and in Practice

The Vital Significance of Military Ethics

Contact Info

Product

Resources

About