As Artificial Intelligence (AI) continues to advance rapidly, Friendly AI (FAI) has been proposed to advocate for more equitable and fair development of AI. Despite its importance, there is a lack of comprehensive reviews examining FAI from an ethical perspective, as well as limited discussion on its potential applications and future directions. This paper addresses these gaps by providing a thorough review of FAI, focusing on theoretical perspectives both for and against its development, and presenting a formal definition in a clear and accessible format. Key applications are discussed from the perspectives of eXplainable AI (XAI), privacy, fairness and affective computing (AC). Additionally, the paper identifies challenges in current technological advancements and explores future research avenues. The findings emphasise the significance of developing FAI and advocate for its continued advancement to ensure ethical and beneficial AI development.

Studies have underscored how, regardless of the recent breakthrough and swift advances in AI research, even state-of-the-art Large Language models (LLMs) continue to struggle when performing logical and mathematical reasoning. The results seem to suggest that LLMs still work as (highly advanced) data pattern identifiers, scoring poorly when attempting to generalise and solve reasoning problems the models have never previously seen or that are not close to samples presented in their training data. To address this compelling concern, this paper makes use of the notion of critical questions from the literature on argumentation theory, focusing in particular on Toulmin's model of argumentation. We show that employing these critical questions can improve the reasoning capabilities of LLMs. By probing the rationale behind the models' reasoning process, the LLM can assess whether some logical mistake is occurring and correct it before providing the final reply to the user prompt. The underlying idea is drawn from the gold standard of any valid argumentative procedure: the conclusion is valid if it is entailed by accepted premises. Or, to paraphrase such Aristotelian principle in a real-world approximation, characterised by incomplete information and presumptive logic, the conclusion is valid if not proved otherwise. This approach successfully steers the models' output through a reasoning pipeline, resulting in better performance against the baseline and its Chain-of-Thought (CoT) implementation. To this end, an extensive evaluation of the proposed approach on the MT-Bench Reasoning and Math tasks across a range of LLMs is provided.

Building safe Large Language Models (LLMs) across multiple languages is essential in ensuring both safe access and linguistic diversity. To this end, we introduce M-ALERT, a multilingual benchmark that evaluates the safety of LLMs in five languages: English, French, German, Italian, and Spanish. M-ALERT includes 15k high-quality prompts per language, totaling 75k, following the detailed ALERT taxonomy. Our extensive experiments on 10 state-of-the-art LLMs highlight the importance of language-specific safety analysis, revealing that models often exhibit significant inconsistencies in safety across languages and categories. For instance, Llama3.2 shows high unsafety in the category crime_tax for Italian but remains safe in other languages. Similar differences can be observed across all models. In contrast, certain categories, such as substance_cannabis and crime_propaganda, consistently trigger unsafe responses across models and languages. These findings underscore the need for robust multilingual safety practices in LLMs to ensure safe and responsible usage across diverse user communities.

In this paper, we propose CitaLaw, the first benchmark designed to evaluate LLMs' ability to produce legally sound responses with appropriate citations. CitaLaw features a diverse set of legal questions for both laypersons and practitioners, paired with a comprehensive corpus of law articles and precedent cases as a reference pool. This framework enables LLM-based systems to retrieve supporting citations from the reference corpus and align these citations with the corresponding sentences in their responses. Moreover, we introduce syllogism-inspired evaluation methods to assess the legal alignment between retrieved references and LLM-generated responses, as well as their consistency with user questions. Extensive experiments on 2 open-domain and 7 legal-specific LLMs demonstrate that integrating legal references substantially enhances response quality. Furthermore, our proposed syllogism-based evaluation method exhibits strong agreement with human judgments.

Prior research indicates that to be able to mediate conflict, observers of disagreements between parties must be able to reliably distinguish the sources of their disagreement as stemming from differences in beliefs about what is true (causality) vs. differences in what they value (morality). In this paper, we test if OpenAI's Large Language Models GPT 3.5 and GPT 4 can perform this task and whether one or other type of disagreement proves particularly challenging for LLM's to diagnose. We replicate study 1 in Ko\c{c}ak et al. (2003), which employes a vignette design, with OpenAI's GPT 3.5 and GPT 4. We find that both LLMs have similar semantic understanding of the distinction between causal and moral codes as humans and can reliably distinguish between them. When asked to diagnose the source of disagreement in a conversation, both LLMs, compared to humans, exhibit a tendency to overestimate the extent of causal disagreement and underestimate the extent of moral disagreement in the moral misalignment condition. This tendency is especially pronounced for GPT 4 when using a proximate scale that relies on concrete language specific to an issue. GPT 3.5 does not perform as well as GPT4 or humans when using either the proximate or the distal scale. The study provides a first test of the potential for using LLMs to mediate conflict by diagnosing the root of disagreements in causal and evaluative codes.

Large language models (LLMs) have gained significant attention over the past few years, particularly following the emergence of ChatGPT, both from researchers Movva et al. [2024] and in terms of their adoption in the private sector. This hype has revolutionized how we use AI in different fields and helped redefine how various domains utilize AI, such as in medicine Alghamdi and Mostafa [2024], Yuan et al. [2024], finance Xie et al. [2024], Malaysha et al. [2024], and even agriculture Gupta et al. [2024]. These advancements demonstrate the profound potential of AI to transform industries, driving innovation and efficiency in ways previously unimaginable. However, one domain that still has room for growth and the potential to bring about significant change is the legal domain Martin et al. [2024], Maree et al. [2024]. Although sectors such as healthcare and finance have rapidly adopted AI to address their unique challenges, the legal industry has been relatively slow to embrace these technologies Legg and Bell [2020]. The complex nature of legal language, coupled with jurisdictional variations and the high stakes involved, and the lack of AI regulations de Almeida et al. [2021], Nadjia [2024], has presented significant obstacles to developing effective AI-powered legal

Specializing large language models (LLMs) for local deployment in domain-specific use cases is necessary for strong performance while meeting latency and privacy constraints. However, conventional task-specific adaptation approaches do not show simultaneous memory saving and inference speedup at deployment time. Practical compression techniques like quantization and pruning require dedicated hardware or kernel support to achieve measured inference speedup. We develop TrimLLM based on the layer-wise specialization phenomenon we empirically observed and verified on contemporary LLMs. TrimLLM reduces the depth of LLMs via progressive layer dropping. We show it retains LLMs' capacity in specific domains and achieves inference speedup irrespective of hardware and deep learning frameworks. We evaluated TrimLLM on LLMs of various sizes for inference; models adapted on medical, legal, and financial datasets all demonstrate $2.1-5.7\times$ inference speedup on consumer GPUs and up to $3.1\times$ speedup on A100 when compared to state-of-the-art model compression algorithms, with no loss in accuracy at 50$\sim$60\% model compression ratio.

AI-generated counterspeech offers a promising and scalable strategy to curb online toxicity through direct replies that promote civil discourse. However, current counterspeech is one-size-fits-all, lacking adaptation to the moderation context and the users involved. We propose and evaluate multiple strategies for generating tailored counterspeech that is adapted to the moderation context and personalized for the moderated user. We instruct an LLaMA2-13B model to generate counterspeech, experimenting with various configurations based on different contextual information and fine-tuning strategies. We identify the configurations that generate persuasive counterspeech through a combination of quantitative indicators and human evaluations collected via a pre-registered mixed-design crowdsourcing experiment. Results show that contextualized counterspeech can significantly outperform state-of-the-art generic counterspeech in adequacy and persuasiveness, without compromising other characteristics. Our findings also reveal a poor correlation between quantitative indicators and human evaluations, suggesting that these methods assess different aspects and highlighting the need for nuanced evaluation methodologies. The effectiveness of contextualized AI-generated counterspeech and the divergence between human and algorithmic evaluations underscore the importance of increased human-AI collaboration in content moderation.

The recently published EU Artificial Intelligence Act (AI Act) is a landmark regulation that regulates the use of AI technologies. One of its novel requirements is the obligation to conduct a Fundamental Rights Impact Assessment (FRIA), where organisations in the role of deployers must assess the risks of their AI system regarding health, safety, and fundamental rights. Another novelty in the AI Act is the requirement to create a questionnaire and an automated tool to support organisations in their FRIA obligations. Such automated tools will require a machine-readable form of information involved within the FRIA process, and additionally also require machine-readable documentation to enable further compliance tools to be created. In this article, we present our novel representation of the FRIA as an ontology based on semantic web standards. Our work builds upon the existing state of the art, notably the Data Privacy Vocabulary (DPV), where similar works have been established to create tools for GDPR's Data Protection Impact Assessments (DPIA) and other obligations. Through our ontology, we enable the creation and management of FRIA, and the use of automated tool in its various steps.

In recent years, much research has been dedicated to uncovering the environmental impact of Artificial Intelligence (AI), showing that training and deploying AI systems require large amounts of energy and resources, and the outcomes of AI may lead to decisions and actions that may negatively impact the environment. This new knowledge raises new ethical questions, such as: When is it (un)justifiable to develop an AI system, and how to make design choices, considering its environmental impact? However, so far, the environmental impact of AI has largely escaped ethical scrutiny, as AI ethics tends to focus strongly on themes such as transparency, privacy, safety, responsibility, and bias. Considering the environmental impact of AI from an ethical perspective expands the scope of AI ethics beyond an anthropocentric focus towards including more-than-human actors such as animals and ecosystems. This paper explores the ethical implications of the environmental impact of AI for designing AI systems by drawing on environmental justice literature, in which three categories of justice are distinguished, referring to three elements that can be unjust: the distribution of benefits and burdens (distributive justice), decision-making procedures (procedural justice), and institutionalized social norms (justice as recognition). Based on these tenets of justice, we outline criteria for developing environmentally just AI systems, given their ecological impact.