Code of Ethics for A.I.

Any code of ethics needs to understand relevant laws, not just to comply with them, but to assess harmonies or clashes with other existing codes of ethics or those in development and you also need to take notice of the guiding motivations and underlying values which inform any regulations. This can be informative for codes both inside and outside the EU.

Ethics is terms of A.I. is the driving force behind the law as well as the moral responsibility code but the relationship between ethics and law is complex.

The law of ethics determines moral structures, assessment of value of any action, interaction, behaviour and code of interaction and the law tries to codify those things and enforcing those things; however look at any legal school and give them an ethical criteria and rapidly a multitude of different interpretations will arise each with superficially valid supporting arguments, 

Law does an excellent job of codifying ethics once the ethical framework is created but as A.I. develops, a pretty significant shift in a lot of different areas of society and this will change the ethical framework. This means that any legal model will need a flexibility that has previously been missing in law. A.I. will evolve faster than the currently cumbersome legislative process allows and indeed will need to be amended at times without the ability of proper pre-amendment scrutiny, usually a recipe for bad law. Add to this mix the current partisan nature and vested interests of politics and the reality is that any effective A.I. law will be developed around a code of behaviour and interactivity that will need to be commercially led as well as commercially responsible. 



In terms of artificial intelligence,  significant shift in a lot of different areas of society is about to occur and we’re only just looking at the tip of the iceberg. We’re already employing artificial intelligence into a lot of spheres of human activity that are morally important and where there are complex criteria as well as significant consequences for people’s lives.

Humans are very poor at determining the criteria for many of the decisions that will arise, as well as suspicious about the commercial motives and underlying bias that exists in any decision matrix, particularly once computers are added to the mix.

 

Medicine, A.I.  and the allocation of scarcity
We are already use to headlines about the use of A.I. in diagnosis by doctors and in assisting surgeons in complex surgery, but far fewer people are looking at the criteria for use of A.I. in clinical need selection and scarce resource allocation. 

Clinical need selection and scarce resource allocation is an area that is time intensive in human terms as well as something that humans are not very good at, even the trained ones. There are so many variables that measurement at all interfaces of the clinical assessment is poor. By contrast, A.I. if properly integrated is

 

 

 

 

 

 

 


. in The field of solid organ transplantation has seen significant advances in surgical techniques, medical diagnosis, selection process, and pharmacotherapy over the past 6 decades. Despite these advances, however, there remains a significant imbalance between the supply of organs available for transplant and the number of patients registered on transplant waiting lists. Notably, the past decade has shown gradual increases in the number of candidates waiting for a kidney, while the number of transplants performed in the United States has declined every year for the past 3 years. The waiting list for heart transplants has been the most rapidly growing list. Fortunately, policies designed to improve procurement, screening, and distribution are helping to make transplantation more efficient and organs more accessible, allowing sicker patients to undergo transplants more quickly. This article presents an overview of the most common solid organ transplantations performed (kidney, liver, heart, and lung), along with the requirements, risks, and complications associated with them.

 

 

 

 

rns the distribution of benefits and burdens or whether it stands to make some people better off and some people worse off. And we’re seeing that take place right now with artificial intelligence. That adds a lot of new wrinkles to these decisions because oftentimes the decisions of AI are inscrutable. They’re opaque to us, they’re difficult to understand, they might be totally mysterious. And while we’re fascinated by what AI can do, I think oftentimes the developers of AI have gotten out ahead of their skis, to borrow a phrase from former vice president, Joe Biden, and have implemented some of these technologies before we fully understand what they’re capable of and what they’re actually doing and how they’re making decisions. That seems problematic. That’s just one of the reasons why ethicists and been concerned about the development and the deployment of artificial intelligence.