I hope the answer is “no” because connecting this technology to killer drones would be truly scary.
https://helda.helsinki.fi/handle/10138/351705
Can a deep neural network predict the political affiliation from facial images of Finnish left and right-wing politicians?
This master's thesis seeks to conceptually replicate psychologist Michael Kosinski's study, published in 2021 in Nature Scientific Reports, in which he trained a cross-validated logistic regression model to predict political orientations from facial images. Kosinski reported that his model achieved an accuracy of 72%, which is significantly higher than the 55% accuracy measured in humans for the same task. Kosinski's research attracted a huge amount of attention and also accusations of pseudoscience.
Where Kosinski trained his model with facial features containing information for example about head position and emotions, in this thesis I use a deep learning convolutional neural network for the same task. Also, I train my model with Finnish data, consisting of photos of the faces of Finnish left- and right-wing candidates gathered from the 2021 municipal elections.
Ye olde ethics.
Artificial Intelligence, Ethics of
The idea of artificial intelligence (AI) predates the introduction of the term “artificial intelligence”. Moreover, the observation that AI raises ethical and social questions predates the current development of the field of AI ethics. Notably, the ancient Greeks already imagined animated instruments that could take over the work they thought human slaves were needed for. They even reflected on what the introduction of artificial intelligence might mean for human society – as is shown in a well-known quote from Aristotle’s Politics, in which Aristotle says that “if each instrument could do its own work, at the word of command, or by intelligent anticipation, like the statues of Daedalus or the tripods of Hephaestus […] managers would not need subordinates and masters would not need slaves” (Aristotle 1996: 15). When Alan Turing later wrote his famous essays in the early 1950s, he discussed whether machines can think, and famously suggested that it is better to reflect on whether machines can imitate intelligent human behavior (Turing 1950). Notably, Turing also raised questions about what this might mean for society, as when he wrote that “it seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers. ... At some stage therefore we should have to expect the machines to take control.” (Turing 2004: 475) This is an early statement of the so-called “control problem”, a topic of on-going interest within discussions of future ethical implications of AI. The term “artificial intelligence” was eventually introduced in 1955 – in a proposal for a research workshop that took place at Dartmouth College in New Hampshire, in the summer of 1956. In that proposal, AI is premised on the idea that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” (McCarthy et al. 1955: 1) In general, then, artificial intelligence is the idea of technologies that can either really be intelligent (whatever that would mean), that could imitate human intelligent behavior, or that could simulate human intelligence.
Worth
considering even
if we don’t like it?
Morally
Repugnant Weaponry? Ethical Responses to the Prospect of Autonomous
Weapons
In
this chapter, political philosopher Alex Leveringhaus asks whether
Lethal Autonomous Weapons (AWS) are morally repugnant and whether
this entails that they should be prohibited by international law. To
this end, Leveringhaus critically surveys three prominent ethical
arguments against AWS: firstly, AWS create ‘responsibility gaps’;
secondly, that their use is incompatible with human dignity; and
thirdly, that AWS replace human agency with artificial agency. He
argues that some
of these arguments fail to show that AWS are morally different from
more established weapons.
However, the author concludes that AWS are currently problematic due
to their lack of predictability.
So, you’re saying we need more laws?
https://cadmus.eui.eu/handle/1814/75116
The impact of facial recognition technology empowered by artificial intelligence on the right to privacy
Facial recognition technology (FRT) is of great interest due to its potential use in different sectors, including aviation, healthcare, marketing, education, military, or security. Moreover, the addition of AI to the FRT means that this technology might have an even greater impact. However, FRT brings potential legal challenges, including privacy, fairness, or accountability, to name a few. These challenges have impacted certain negative attitudes towards this technology. Many stakeholders, privacy advocates, industry members, and even the European Commission have raised some reservations towards FRT. This chapter aims at setting up the current FRT-AI legal scene by conceptualising the technology, the main problems it entails from the fairness, accountability, data protection, and privacy perspectives and proposing some solutions to these conundrums, based on the regulatory instruments from the EU and the US. The chapter demonstrates that the answer to such legal challenges goes through a mixed alliance between law and computer science.
Is a ban likely? Perhaps more complex regulations, but it may be too useful to ban.
Chapter 4: The politics of facial recognition bans in the United States
Chapter 6: Rising global opposition to face surveillance
Is the machine’s word good enough to convict?
https://lmulawreview.scholasticahq.com/article/56556.xml
MAN VS. MACHINE: FACIAL RECOGNITION TECHNOLOGY REPLACING EYEWITNESS IDENTIFICATIONS
As they intersect...
https://link.springer.com/chapter/10.1007/978-981-19-4574-8_8
AI Ethics and Rule of Law
After entering the twenty-first century, the two most significant changes in technology are artificial intelligence and genetic engineering. The two domains have a lot in common: firstly, their applications possess great diffusion effect, which will fundamentally change the operation law of some industries and economic institutions; secondly, their applications also bring enormous ethical risks. Genetic engineering will undoubtedly change the boundaries of social equity and the basic laws of nature, while AI is changing the ethics of human cognition and the corresponding legal ethics; thirdly, both technologies are shaping the future of human civilization. Almost all novels or monographs related to the prediction of human future will be discussed more or less from genetic engineering and AI. Therefore, understanding the boundaries of the two fields is comprehending the prospects of human imagination in the future.
Interesting. Which is the intellectual property? The output from the AI or the AI itself?
https://www.tandfonline.com/doi/abs/10.1080/13600834.2022.2154049
Artificial intelligence, inventorship and the myth of the inventing machine: Can a process be an inventor?
Institutional and academic debates have intensified regarding the recent efforts to claim inventorship of AI-related patent applications, as has notably been seen in the known cases of Thaler v Comptroller (‘DABUS’) that have been examined in various jurisdictions. The pertinent question that has emerged is whether artificial intelligence systems can independently produce patentable subject matter. What has to be looked at, first, is the preliminary question of what the claim of producing inventions ‘autonomously’ can possibly mean under a technological perspective – an essential stage in the debate that is usually bypassed in legal commentary. Once such a technological explanation has been provided, a legal question can reasonably arise as to whether an AI process, such as software, may make a contribution that rewards a patent. AI inventions are legally approached and analysed as processes and as to their relationship with their direct products. Thus, where a process (AI) ‘creates’ or ‘makes’ a product, the focus is reasonably put on if and to what extent disclosing the product can provide a contribution separate to that which has already been provided by the process that created it. It is stressed that the current push for AI-generated products bypasses this key question which is essential in assessing the invention.
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