Is they is or is they ain’t the same? Isn’t that the goal?

https://link.springer.com/chapter/10.1007/978-94-6265-523-2_2

Artificial Intelligence Versus Biological Intelligence: A Historical Overview

The discipline of artificial intelligence originally aimed to replicate human-level intelligence in a machine. It could be argued that the best way to replicate the behavior of a system is to emulate the mechanisms producing this behavior. But whether we should try to replicate the human brain or the cognitive faculties to accomplish this is unclear. Early symbol-based AI systems paid little regard to neuroscience and were rather successful. However, since the 1980s, artificial neural networks have become a powerful AI technique that show remarkable resemblance to what we know about the human brain. In this chapter, we highlight some of the similarities and differences between artificial and human intelligence, the history of their interconnection, what they both excel at, and what the future may hold for artificial general intelligence.

I’ll keep gathering ideas...

https://link.springer.com/article/10.1007/s43681-022-00194-0

Reconsidering the regulation of facial recognition in public spaces

This paper contributes to the discussion on effective regulation of facial recognition technologies (FRT) in public spaces. In response to the growing universalization of FRT in the United States and Europe as merely intrusive technology, we propose to distinguish scenarios in which the ethical and social risks of using FRT are unattainable from other scenarios in which FRT can be adjusted to improve our everyday lives. We suggest that the general ban of FRT technologies in public spaces is not an inevitable solution. Instead, we advocate for a risk-based approach with emphasis on different use-cases that weighs moral risks and identifies appropriate countermeasures. We introduce four use-cases that focus on presence of FRT on entrances to public spaces (1) Checking identities in airports (2) Authorisation to enter office buildings (3) Checking visitors in stadiums (4) Monitoring passers-by on open streets, to illustrate the diverse ethical and social concerns and possible responses to them. Based on the different levels of ethical and societal risks and applicability of respective countermeasures, we call for a distinction of public spaces between semi-open public spaces and open public spaces. We suggest that this distinction of public spaces could not only be helpful in more effective regulation and assessment of FRT in public spaces, but also that the knowledge of different risks and countermeasures will lead to better transparency and public awareness of FRT in diverse scenarios.

(Related)

https://ojs.victoria.ac.nz/wfeess/article/view/7645

Ethics of Facial Recognition Technology in Law Enforcement: A Case Study

Facial Recognition Technology (FRT) has promising applications in law enforcement due to its efficiency and cost-effectiveness. However, this technology poses significant ethical concerns that overshadow its benefits. Responsible use of FRT requires consideration of these ethical concerns that legislation fails to cover. This study investigates the ethical issues of FRT use and relevant ethical frameworks and principles designed to combat these issues. Drawing on this, we propose and discuss a code of ethics for FRT to ensure its ethical use in the context of New Zealand law enforcement.

Similar to what ICE and TSA are doing?

https://obiter.mandela.ac.za/article/view/14254

THE LEGAL ISSUES REGARDING THE USE OF ARTIFICIAL INTELLIGENCE TO SCREEN SOCIAL MEDIA PROFILES FOR THE HIRING OF PROSPECTIVE EMPLOYEES

The fourth industrial revolution has introduced advancement in technologies that have affected many commercial sectors in South Africa, and the employment sector is no exception. One of these advancements is the creation of artificial intelligence technologies that can assist humans to make everyday tasks quicker and more efficient. It has become common for organisations to screen social media profiles in order to gain information about a prospective employee. With the aid of artificial intelligence, employers can use such systems to easily sift through social media profiles and access the data it needs. Although these technological creations have many successful outcomes, artificial intelligence systems can also have drawbacks, such as inadvertently discriminating against certain groups of people when data is collected, processed and stored. Issues surrounding privacy breaches are also raised where artificial intelligent systems seek to access personal information from social media profiles. Prospective employees will need to be informed that their social media profiles are being screened and the artificial intelligence system needs to be programmed properly to ensure that data is correctly and fairly processed and collected.

The Terminator on trial?

https://link.springer.com/chapter/10.1007/978-94-6265-523-2_8

Prosecuting Killer Robots: Allocating Criminal Responsibilities for Grave Breaches of International Humanitarian Law Committed by Lethal Autonomous Weapon Systems

The fast-growing development of highly automated and autonomous weapon systems has become one of the most controversial sources of discussion in the international sphere. One of the many concerns that surface with this technology is the existence of an accountability gap. This fear stems from the complexity of holding a human operator criminally responsible for a potential failure of the weapon system. Thus, the question on who is to be held criminally liable for grave breaches to international humanitarian law when these crimes are not intentional arises. This chapter explains how we will need to rethink the responsibilities, command structure, and everyday operations within our military when engaging in the use of fully autonomous weapon systems to allow our existing legal framework to assign criminal responsibility. For this purpose, this chapter analyses the different types of criminal responsibilities that converge in the process of employing lethal autonomous weapons and determine which of them is the most appropriate for grave breaches of international humanitarian law in this case.

(Related) Who programmed the Terminator?

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4159762

Are programmers in or 'out of' control? The individual criminal responsibility of programmers of autonomous weapons and self-driving cars

The increasing use of autonomous systems technology in cars and weapons could lead to a rise of harmful incidents on the roads and in the battlefield potentially amounting to crimes. Such a rise has led to questions as to who is criminally responsible for these crimes – be it the users or the programmers? This chapter seeks to clarify the role of programmers in crimes committed with autonomous systems by focusing on the use of autonomous vehicles and autonomous weapons. In assessing whether a programmer could be criminally responsible for crimes committed with autonomous technology, it is necessary to determine whether the programmer had control over this technology. Risks inherent in the use of these autonomous technologies may allow for a programmer to escape criminal liability but some risks may be foreseeable and thus considered under the programmer’s control. The central question is whether programmers exercise causal control over a chain of events leading to the commission of a crime. This chapter contends that programmers’ control begins at the initial stage of the autonomous system development process but continues in the use phase, extending to the behaviour and effects of autonomous systems technology. Based on criminal responsibility requirements and causation theories, this chapter develops a notion of meaningful human control (MHC) that may function to trace back responsibility to the programmers who could understand, foresee, and anticipate the risk of a crime being committed with autonomous systems technology.

It’s not my fault, the computer did it!

https://link.springer.com/chapter/10.1007/978-94-6265-523-2_14

Contractual Liability for the Use of AI under Dutch Law and EU Legislative Proposals

In this chapter, the contractual liability of a company (the ‘user’) using an AI system to perform its contractual obligations is analysed from a Dutch law and EU law perspective. In particular, we discuss three defences which, in the event of a breach, the user can put forward against the attribution of that breach to such user and which relate to the characteristics of AI systems, especially their capacity for autonomous activity and self-learning:

(1) the AI system was state-of-the-art when deployed,

(2) the user had no control over the AI system, and

(3) an AI system is not a tangible object and its use in the performance of contractual obligations can thus not give rise to strict liability under Article 6:77 of the Dutch Civil Code.

Following a classical legal analysis of these defences under Dutch law and in light of EU legislative proposals, the following conclusions are reached. Firstly, the user is strictly liable, subject to an exception based on unreasonableness, if the AI system was unsuitable for the purpose for which it was deployed as at the time of deployment. Advancements in scientific knowledge play no role in determining suitability. Secondly, a legislative proposal by the European Parliament allows the user to escape liability for damage caused by a non-high-risk AI system if the user took due care with respect to the selection, monitoring and maintenance of that system. Thirdly, the defence that the user is not liable because an AI system is not a tangible object is unlikely to hold.

Bigger must mean better?

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4152035

Big Data Policing Capacity Measurement

Big data, algorithms, and computing technologies are revolutionizing policing. Cell phone data. Transportation data. Purchasing data. Social media and internet data. Facial recognition and biometric data. Use of these and other forms of data to investigate, and even predict, criminal activity is law enforcement’s presumptive future. Indeed, law enforcement in several major cities have already begun to develop a big data policing mindset, and new forms of data have played a central role in high-profile matters featured in the “Serial” and “To Live and Die in LA” podcasts, as well as in the Supreme Court’s leading Carpenter v. U.S. opinion. Although the ascendancy of big data policing appears inevitable, important empirical questions on local law enforcement agency capacity remain insufficiently answered. For example, do agencies have adequate capacity to facilitate big data policing? If not, how can policymakers best target resources to address capacity shortfalls? Are certain categories of agencies in a comparatively stronger position in terms of capacity? Answering questions such as these requires empirical measurement of phenomena that are notoriously difficult to measure. This Article presents a novel multidimensional measure of big data policing capacity in U.S. local law enforcement agencies: the Big Data Policing Capacity Index (BDPCI). Analysis of the BDPCI provides three principal contributions. First, it offers an overall summary of more than 2,000 local agencies’ inadequacy in big data policing capacity using a large-N dataset. Second, it identifies factors that are driving lack of capacity in agencies. Third, it illustrates how differences between groups of Agencies might be analyzed based on size and location, including an illustrative ranking of the fifty U.S. states. This Article is meant to inform stakeholders on agencies’ current positions, advise on how best to improve such positions, and drive further research into empirical measurement and big data policing.

Should your CPO be an AI?

https://aisel.aisnet.org/amcis2022/sig_sec/sig_sec/8/

Exploring the Characteristics and Needs of the Chief Privacy Officer in Organizations

Over the past two decades, the growth in technology (i.e. social networking, big data, smartphones, Internet of Things, artificial intelligence, etc.) and increased collection of customer data mixed with various data breaches has increased the need to focus more on information privacy. Various laws and regulations have been established, such as the GDPR in Europe and various state level regulations in the United States, to ensure the protection of customers and their data. The Chief Privacy Officer role was established in the 1990’s with a strong research focus in the early 2000s. However, little attention has been given to the role of the CPO in the past decade. Due to the increases in technology, private data collections, breaches, and privacy regulations, there is a need to reevaluate the role of the CPO and the evolving responsibilities it entails.

Looking at what we’re looking at.

https://link.springer.com/chapter/10.1007/978-94-6265-523-2_23

Ask the Data: A Machine Learning Analysis of the Legal Scholarship on Artificial Intelligence

In the last decades, the study of the legal implications of artificial intelligence (AI) has increasingly attracted the attention of the scholarly community. The proliferation of articles on the regulation of algorithms has gone hand in hand with the acknowledgment of the existence of substantial risks associated with current applications of AI. These relate to the widening of inequality, the deployment of discriminatory practices, the potential breach of fundamental rights such as privacy, and the use of AI-powered tools to surveil people and workers. This chapter aims to map the existing legal debate on AI and robotics by means of bibliometric analysis and unsupervised machine learning. By using structural topic modeling (STM) on abstracts of 1298 articles published in peer-reviewed legal journals from 1982 to 2020, the chapter explores what the dominant topics of discussion are and how the academic debate on AI has evolved over the years. The analysis results in a systematic computation of 13 topics of interest among legal scholars, showing trends of research and potential areas for future research.