Opening the path to ethics in Artificial Intelligence
The publication of this article has been extracted by Researchgate.net Website
In many respects, artiﬁcial intelligence (AI) is still in its youth and recent advancements have only been made possible due to the vast increases in the volume of data.
Although slow, in comparison to some of the earliest predictions, the field of AI has recently made striking advances. Many experts argue that it is likely that we will see signiﬁcant breakthroughs sometime in this century, possibly reaching an artiﬁcial general intelligence level. Others believe we are still far from this ideal.
General purpose artiﬁcial intelligence
Getting to that level, general purpose artiﬁcial intelligence, with the ﬂexibility of human intelligence, isn’t some small step from where we are now; instead, it will require an immense amount of foundational progress, not just more of the same sort of thing that’s been accomplished in the last few years, but as we will show, something entirely diﬀerent.
While it’s been said that, when it comes to AI, we have only scratched the surface so far, we can also see how these relatively small developments have already signiﬁcantly echoed drastic changes, social, ethical and political.
The ethical debate is not new
However, as these developments unfold at a faster pace, the time calls for more concrete discussions around the ethics of AI, at a global level. In an attempt to address some of these challenges, we currently have several ethical AI frameworks in place worldwide, with more being released or in development around the world every day.
From the European Commission’s Guidelines for Trustworthy AI to the Asilomar AI Principles, the message is usually similar: more transparency and explicability. However, navigating the broad number of resources currently available is not a simple process.
More importantly, when it comes to building AI, we are far from the practice of these ideals. There is still no common or unifying discussion on how to govern ethics in AI implementations, or the ongoing auditability once machine learning is improving without human intervention.
AI ethical guidelines
In a recent paper, the author refers to this problem as “principle proliferation”. There are too many diﬀerent frameworks available, not only making it diﬃcult to navigate through them but also opening an opportunity for choice when there should not be one. Additionally, in a recent study, researchers found that the eﬀectiveness of ethical guidelines or ethical codes is almost zero and that they do not change the behavior of professionals from the tech community.
More importantly, ethical research also requires internalizing a commitment to it, aided by training and education on codes and appropriate research methods, mentoring and workplace cultures that foster ethics, transparency about how the research was conducted, and forums (in person and in writing, local and international) where researchers can share their experiences and the challenges they face.”