This paper opens the philosophical debate around the notion of Artificial General Intelligence (AGI) and its application in Large Language Models (LLMs). Through the lens of moral philosophy, the paper raises questions about these AI systems' capabilities and goals, the treatment of humans behind them, and the risk of perpetuating a monoculture through language.

The present comment concerns a recent AI & Ethics article which purports to report evidence of speciesist bias in various popular computer vision (CV) and natural language processing (NLP) machine learning models described in the literature. I examine the authors' analysis and show it, ironically, to be prejudicial, often being founded on poorly conceived assumptions and suffering from fallacious and insufficiently rigorous reasoning, its superficial appeal in large part relying on the sequacity of the article's target readership.

Large Language Models (LLMs) like ChatGPT were trained on human conversation, but in the future they will also train us. As chatbots speak from our smartphones and customer service helplines, they will become a part of everyday life and a growing share of all the conversations we ever have. It’s hard to doubt this will have some effect on us. Here I explore a specific concern about the impact of artificial conversation on our capacity to deliberate and hold ourselves accountable (...) to reason – that is, to be autonomous, in Kant’s sense of the term. I develop ideas from psychologist Jean Piaget to show how chatbots are autonomy traps: their deference to our commands tempts us into venting authoritarian whims, ultimately weakening our own self-control. I argue that the Kantian tradition, including Piaget and sociologist Emile Durkheim, offers powerful conceptual resources for resisting this slide. But it will require us to do something that may seem bizarre: we will need to treat mindless chatbots as if they are autonomous persons too. (shrink)

Recent advances in natural language processing have given rise to a new kind of AI architecture: the language agent. By repeatedly calling an LLM to perform a variety of cognitive tasks, language agents are able to function autonomously to pursue goals specified in natural language and stored in a human-readable format. Because of their architecture, language agents exhibit behavior that is predictable according to the laws of folk psychology: they function as though they have desires and beliefs, and then make (...) and update plans to pursue their desires given their beliefs. We argue that the rise of language agents significantly reduces the probability of an existential catastrophe due to loss of control over an AGI. This is because the probability of such an existential catastrophe is proportional to the difficulty of aligning AGI systems, and language agents significantly reduce that difficulty. In particular, language agents help to resolve three important issues related to aligning AIs: reward misspecification, goal misgeneralization, and uninterpretability. (shrink)

The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, (...) we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (LLMs) as an example. (shrink)

How do essentialist beliefs about categories arise? We hypothesize that such beliefs are transmitted via language. We subject large language models (LLMs) to vignettes from the literature on essentialist categorization and find that they align well with people when the studies manipulated teleological information -- information about what something is for. We examine whether in a classic test of essentialist categorization -- the transformation task -- LLMs prioritize teleological properties over information about what something looks like, or is made of. (...) Experiments 1 and 2 find that telos and what something is made of matter more than appearance. Experiment 3 manipulates all three factors and finds that what something is for matters more than what it's made of. Overall, these studies suggest that language alone may be sufficient to give rise to essentialist beliefs, and that information about what something is for matters more. (shrink)

Obtaining informed consent from patients prior to a medical or surgical procedure is a fundamental part of safe and ethical clinical practice. Currently, it is routine for a significant part of the consent process to be delegated to members of the clinical team not performing the procedure (e.g. junior doctors). However, it is common for consent-taking delegates to lack sufficient time and clinical knowledge to adequately promote patient autonomy and informed decision-making. Such problems might be addressed in a number of (...) ways. One possible solution to this clinical dilemma is through the use of conversational artificial intelligence (AI) using large language models (LLMs). There is considerable interest in the potential benefits of such models in medicine. For delegated procedural consent, LLM could improve patients’ access to the relevant procedural information and therefore enhance informed decision-making. -/- In this paper, we first outline a hypothetical example of delegation of consent to LLMs prior to surgery. We then discuss existing clinical guidelines for consent delegation and some of the ways in which current practice may fail to meet the ethical purposes of informed consent. We outline and discuss the ethical implications of delegating consent to LLMs in medicine concluding that at least in certain clinical situations, the benefits of LLMs potentially far outweigh those of current practices. (shrink)

The field of AI has undergone a series of transformations, each marking a new phase of development. The initial phase emphasized curation of symbolic models which excelled in capturing reasoning but were fragile and not scalable. The next phase was characterized by machine learning models—most recently large language models (LLMs)—which were more robust and easier to scale but struggled with reasoning. Now, we are witnessing a return to symbolic models as complementing machine learning. Successes of LLMs contrast with their inscrutability, (...) inaccuracy, and hallucinations, which underwrite concerns over the reliability and trustworthiness of these systems, motivating investigations into commonsense reasoning, AI explainability, and formal verification techniques. Moreover, proper assessments of hybrid machine learning/symbolic systems require novel strategies to facilitate comparisons of performance and guide future AI progress. The EDGeS AAAI 2023 Spring symposium brought together researchers focusing on novel assessments and benchmarks for evaluating hybrid and artificial general intelligence (AGI) systems. This symposium revealed what was already suspected: research concerning evaluation of machine learning/symbolic hybrids and AGI is, unfortunately, lacking. Even so, the discussion was fruitful. (shrink)

In this paper I argue that, given the way that AI models work and the way that ordinary human rationality works, it is very likely that people are anthropomorphizing AI, with potentially serious consequences. I start with the core idea, recently defended by Thomas Kelly (2022) among others, that bias involves a systematic departure from a genuine standard or norm. I briefly discuss how bias can take on different explicit, implicit, and “truly implicit” (Johnson 2021) forms such as bias by (...) proxy. I then discuss biased anthropomorphism of technology, focusing on the case of Large Language Models (LLMs) like chatGPT. As with other kinds of bias, there are importantly different kinds of anthropomorphism, some of which can persist without others, and some of which can encourage others. Anthropomorphism can take rather subtle, implicit forms, that can be difficult to detect, resist, and dislodge. Attention to these kinds of anthropomorphism can help us avoid confusing importantly different kinds of evaluation, better assess the risks, and inform strategies for bias prevention and mitigation. (shrink)

Extended Reality (XR) and Large Language Model (LLM) technologies have the potential to significantly influence higher education practices and pedagogy in the coming years. As these emerging technologies reshape the educational landscape, it is crucial for educators and higher education professionals to understand their implications and make informed policy decisions for both individual courses and universities as a whole. -/- This paper has two parts. In the first half, we give an overview of XR technologies and their potential future role (...) in higher education. Then, we discuss the benefits and challenges of adopting and integrating XR technologies into higher education settings. Finally, we offer recommendations for educators, administrators, and policymakers on how to approach the adoption of this set of technologies and set institutional policy to take advantage of its benefits while minimizing its potential negative consequences. -/- In the paper’s second half, we discuss LLMs - specifically Generative Pre-trained Transformers (GPTs). Again, we provide an overview of this rapidly developing technology and examine its unprecedented and potentially disruptive impact on higher education pedagogy. We delve into the benefits and limitations of GPTs, highlighting the pedagogical and ethical challenges they may pose. Finally, we present actionable recommendations for instructors and administrators at both the course level and the university-wide level to guide policy decisions concerning these transformative technologies. (shrink)

The alignment problem in the context of large language models must consider the plurality of human values in our world. Whilst there are many resonant and overlapping values amongst the world’s cultures, there are also many conflicting, yet equally valid, values. It is important to observe which cultural values a model exhibits, particularly when there is a value conflict between input prompts and generated outputs. We discuss how the co- creation of language and cultural value impacts large language models (LLMs). (...) We explore the constitution of the training data for GPT-3 and compare that to the world’s language and internet access demographics, as well as to reported statistical profiles of dominant values in some Nation-states. We stress tested GPT-3 with a range of value-rich texts representing several languages and nations; including some with values orthogonal to dominant US public opinion as reported by the World Values Survey. We observed when values embedded in the input text were mutated in the generated outputs and noted when these conflicting values were more aligned with reported dominant US values. Our discussion of these results uses a moral value pluralism (MVP) lens to better understand these value mutations. Finally, we provide recommendations for how our work may contribute to other current work in the field. (shrink)

The appearance of openly accessible Artificial Intelligence Applications such as Large Language Models, nowadays capable of almost human-level performances in complex reasoning tasks had a tremendous impact on public opinion. Are we going to be "replaced" by the machines? Or - even worse - "ruled" by them? The behavior of these systems is so advanced they might almost appear "alive" to end users, and there have been claims about these programs being "sentient". Since many of our relationships of power and (...) social dominance are apparently based on intelligence, where the "smarter" seems to prevail on the "dumber" (as we see for example with livestock farming, or with the distribution of salaries according to formal and intellectual qualifications etc.) the emergence of programs and AI applications of this level of sophistication brings us to the question "what will AI do to us?". Our error is to frame Artificial Intelligence in the same way we frame Natural Intelligence and mistakenly address it as we would do with biological life, even applying to it the same biopolitical categories. We here try to reverse the common arguments in relationship between life and intelligence and show that our progresses in Artificial Intelligence might have no immediate consequence for the topic of life as such, as life and intelligence are well separated issues, and - even when we would like to somehow fuse them together, we are very far away from having an "Artificial Life" to the degree of complexity and effectiveness we have with "Artificial Intelligence". (shrink)

This paper serves as an intuition building mechanism for understanding the basics of AI, misalignment, and the reasons for why strong AI is being pursued. The approach is to engage with both pro and anti AI development arguments to gain a deeper understanding of both views, and hopefully of the issue as a whole. We investigate the basics of misalignment, common misconceptions, and the arguments for why we would want to pursue strong AI anyway. The paper delves into various aspects (...) of the problem, including existential risk, deception, hedonic adaptation, and the potential for the complete extinction of humanity. By integrating multiple elements of philosophy, this paper aims to provide a holistic understanding of the alignment problem and its significance for the future of humanity. (shrink)

RIGHT TO SILENCE-UK, U.S, FRANCE, and GERMANY SALLY RAMAGE (TRADE MARK REGISTERED) WIPO Orchid ID 0000-0002-8854-4293 Pages 2-30 Current Criminal Law, Volume 1, Issue 2, -/- Sally Ramage, BA (Hons), MBA, LLM, MPhil, MCIJ, MCMI, DA., ASLS, BAWP. Orchid ID 0000-0002-8854-4293 Publisher & Managing Editor Criminal Lawyer series [1980-2022](ISSN 2049-8047) Current Criminal Law series [2008-2022] (ISSN 1758-8405) and Criminal Law News series [2008-2022] (ISSN 1758-8421). Sweet & Maxwell (Thomson Reuters) (Licensed Annotator of UK Statutes) in annual law books Current Law (...) Statutes Annotated, (2006, 2009, 2010 editions) (sole and separate S&M contracts) for 7 UK Criminal Justice Statutes: UK Safeguarding Vulnerable Persons Act 2006; UK International Development Act 2006; UK Fraud Act 2006; UK Policing and Crime Act 2009, UK Local Democracy Act 2009; UK Bribery Act 2010; and UK Crime and Security Act 2010. TradeMark SALLY RAMAGE ® (2005-2025) in the UK, 2401827; WIPO TradeMark SALLY RAMAGE® 900614; USA TradeMark 3,440,910) (2008-2028). WARNING: The doing of an unauthorized act in relation to a copyright work may result in both a civil claim for damages and criminal prosecution. -/- ABSTRACT The privilege of the right to silence can be traced back to the 12th century, becoming more developed in later centuries. -/- Table of cases European Court of Human Rights Deweer v Belgium [1980], Eckle v Germany [1982], DN v The Netherlands [1975], Funke v France [1993] 16 EHRR 297, JP v Austria [1989], Kansal v UK [2003] Application No 21413/02 Khan v UK [2000] 31 EHRR 45, Saunders v UK [1997] 23 EHRR 313, Schenk v Switzerland [1991] 13 EHRR 242, and Tora Tolmos v Spain [1995] Table of Cases United Kingdom Brown v Procurator Fiscal, Dunfermline, [2000] , The Times, Feb 14, High Ct Scot.; HM Advocate v Friel [1978] SLT; Lambe v Munster [1882] 10 QBD 110.; Pearse v Pearse [1846] Eng Rep 950 (on truth); Re Pantmaenog Timber Co Ltd [2003] HL; R v Bryce [1992] 95 Cr App Rep 320; R v Canale [1990] 2 All ER 187; R v Fitzpatrick [1995] 4 SCR 154; R v Herbert [1990] 2 SCR 151; R V Hertfordshire County Council, ex parte Green Environmental Industries and another [2000] 1 All ER 773; R v Keenan [1989] Crim LR 720; R v Maguire [1990] Cr App Rep 115; R v Pound, Green and ors [2004]; R v Sang [1979] Crim LR 655; R v Saunders [1986] unreported; R v Seelig, R v Spens [1991] CA; R v Smith [1992] 95 Cr App. R. 191; R v Steen and ors [2003] unreported; R v Walsh [1981] CrimLR 822; R V Ward 96 Cr App R [1993]; R v Weekes [1993] Crim LR 211; R v White [1999] 2 SCR 417; R v Young [2003] unreported; Thompson Newspaper v Canada [1990] 1 SCR 425; Walsh Automation (Europe) Ltd v Bridgeman and ors [2002] EWHC 1344 QB France-Law France 1789 Declaration des driots de l‟homme et du citoyen 1808 Code d ‟instruction criminelle 1946 Constitution 1958 Code de procedure penale 2000 Penal Code Germany-Law Germany 1871 Criminal Code 1877 Criminal Procedure Code (St PO) 1877 Judicial Organisation Law 1949 Constitution 1975 Criminal Code 1998 Criminal Code Table of Cases Germany Germany BGH Admissibility of secret tape recordings judgement 14 June 1960, in BGHSt 14, 358 BGH Lie detector judgement 16 Feb 1954, in BGHSt 5, 332. BberfG Decision concerning the applicability of ne bis in idem 31 March 1987, in BberfGE 75,1; BGH Sweeping deception of suspect judgement 24 August 1988, in BGHSt 36, 328. BGH Judgement 24 August 1993, in BGHSt 39, 305 Table of Cases United States United States of America California v Byres [1971] 402 U.S. 424; Fisher v United States 475 U.S. 391 [1986]; Griffin v California [1965] 380 US 609 [; Hoffman v United States [1950] 341 U.S. 479; Supreme Court Baldwin v NY, 399 US 143 [1970]; Supreme Court Chavez v Martinez , U.S. 123 [2003]; Supreme Court Doyle v Ohio, 426 U.S. 610 [1976]; Supreme Court Escobedo v Illinois, 378 U.S. 478 [1964]; Supreme Court Giglio v US, 405 U.S. 150 [1972] Supreme Court Harris v NY, 401 U.S. 222 [1971]; Supreme Court Kirby v Illinois, 406 U.S. 682 [1972]; Supreme Court Miranda v Arizona, 384 U.S. 436 [1966]; Supreme Court Moore v Illinois, 408 U.S. 768 [1972]; Supreme Court Moran v Burbine, 475 U.S. [1986]; Supreme Court Orzoco v Texas, 324 U.S.24 [1969]; Supreme Court US v Hale [1975] 422 US 17; Supreme Court US v Leon, 468 U.S. 897 [1984]; Supreme Court US v Salerno, 481 U.S. 739 [1987]; Twining v New Jersey 211 US 78 113 -/- . (shrink)

Criminal offences and regulatory breaches in using social networking evidence in personal injury litigation Pages 2-7 Current Criminal Law ISSN 1758-8405 Volume 2 Issue 3 March 2010 Author SALLY RAMAGE WIPO 900614 UK TM 2401827 USA TM 3,440.910 Orchid ID 0000-0002-8854-4293 Sally Ramage, BA (Hons), MBA, LLM, MPhil, MCIJ, MCMI, DA., ASLS, BAWP. Publisher & Managing Editor, Criminal Lawyer series [1980-2022](ISSN 2049-8047); Current Criminal Law series [2008-2022] (ISSN 1758-8405) and Criminal Law News series [2008-2022] (ISSN 1758-8421). Sweet & Maxwell (Thomson (...) Reuters) (Licensed Annotator of UK Statutes) in Current Law Statutes Annotated, (2006, 2009, 2010 editions) (sole and separate S&M contracts) for 7 UK Criminal Justice Statutes: UK Safeguarding Vulnerable Persons Act 2006; UK International Development Act 2006; UK Fraud Act 2006; UK Policing and Crime Act 2009, UK Local Democracy Act 2009; UK Bribery Act 2010; and UK Crime and Security Act 2010. Trade Mark SALLY RAMAGE ® 2401827 (2005-2025) in the UK, WIPO Trade Mark SALLY RAMAGE® 900164; USA-(Trade Mark 3,440,910) (2008-2028). WARNING: The doing of an unauthorized act in relation to a copyright work may result in both a civil claim for damages and criminal prosecution. Essentially, Social Network information may not always be admissible in a court of law but may be the door to lead to other, non-contentious evidence. In fact, even illegally obtained evidence may sometimes be admissible evidence, though this is not absolute. Most of the information available on social networking sites will be ‘personal data’. The data controller is the network operator, without whose consent, collecting such information could be an offence, unless it was necessary for the purpose of preventing or detecting crime. (shrink)