Given the success of ChatGPT, LaMDA and other large language models (LLMs), there has been an increase in development and usage of LLMs within the technology sector and other sectors. While the level in which LLMs has not reached a level where it has surpassed human intelligence, there will be a time when it will. Such LLMs can be referred to as advanced LLMs. Currently, there are limited usage of ethical artificial intelligence (AI) principles and guidelines addressing advanced LLMs due to the fact that we have not reached that point yet. However, this is a problem as once we do reach that point, we will not be adequately prepared to deal with the aftermath of it in an ethical and optimal way, which will lead to undesired and unexpected consequences. This paper addresses this issue by discussing what ethical AI principles and guidelines can be used to address highly advanced LLMs.

A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms that tackle social navigation remains hard because it involves not just robotic agents moving in static environments but also dynamic human agents and their perceptions of the appropriateness of robot behavior. In contrast, clear, repeatable, and accessible benchmarks have accelerated progress in fields like computer vision, natural language processing and traditional robot navigation by enabling researchers to fairly compare algorithms, revealing limitations of existing solutions and illuminating promising new directions. We believe the same approach can benefit social navigation. In this paper, we pave the road towards common, widely accessible, and repeatable benchmarking criteria to evaluate social robot navigation. Our contributions include (a) a definition of a socially navigating robot as one that respects the principles of safety, comfort, legibility, politeness, social competency, agent understanding, proactivity, and responsiveness to context, (b) guidelines for the use of metrics, development of scenarios, benchmarks, datasets, and simulators to evaluate social navigation, and (c) a design of a social navigation metrics framework to make it easier to compare results from different simulators, robots and datasets.

In thinking about how artificial intelligence works, it is not difficult to arrive at the analogy of a human brain, learning over time from the information it is provided, seeking patterns in that information to optimize its ability to apply those learnings to similar or never-before-seen problems. However, the power of AI lies in its ability to process infinitely greater volumes of information, including streaming data, to detect patterns that may otherwise never be detectible to the human brain. This kind of superpower can be useful when processing over one hundred billion transactions per year and seeking, in real time, to detect costly fraud. This is how, using artificial intelligence technologies such as smart agents, neural networks, and case-based reasoning, Brighterion has been able to transform how fraud is detected and prevented across payment, healthcare and credit risk lifecycle ecosystems. As AI continues to enable, improve and automate a growing number of tasks and processes across different industries, it is not only shifting how companies conduct business, it is also increasingly curating our daily experiences and shaping how we as individuals interact with our world.

Much of the existing research on the social and ethical impact of Artificial Intelligence has been focused on defining ethical principles and guidelines surrounding Machine Learning (ML) and other Artificial Intelligence (AI) algorithms [IEEE, 2017, Jobin et al., 2019]. While this is extremely useful for helping define the appropriate social norms of AI, we believe that it is equally important to discuss both the potential and risks of ML and to inspire the community to use ML for beneficial objectives. In the present article, which is specifically aimed at ML practitioners, we thus focus more on the latter, carrying out an overview of existing high-level ethical frameworks and guidelines, but above all proposing both conceptual and practical principles and guidelines for ML research and deployment, insisting on concrete actions that can be taken by practitioners to pursue a more ethical and moral practice of ML aimed at using AI for social good.

In January of 2019 Smart Dubai launched the city's official principles and guidelines for the ethical implementation of AI. What truly makes Dubai's approach to AI unique is our city-government launched AI Ethics Self-Assessment Toolkit – which allows anyone implementing AI to self-assess their performance against a set of criteria which when taken together assure an ethical approach. The process uses the data from the toolkit to create a positive feedback loop with those using and developing AI. Express Computer spoke to H.E. Younus Al Nasser, Assistant Director General, Smart Dubai and CEO, Smart Dubai Data. What potential do you see in AI for governance and happiness?

In the past five years, private companies, research institutions and public sector organizations have issued principles and guidelines for ethical artificial intelligence (AI). However, despite an apparent agreement that AI should be'ethical', there is debate about both what constitutes'ethical AI' and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analysed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted, why they are deemed important, what issue, domain or actors they pertain to, and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

Adding cognitive abilities to a machine may appear to many as the background plot of every science-fiction movie, however, the debate around the future and limitations of Artificial Intelligence (AI) has unquestionably existed for decades in the world of computer automation systems, especially with AI being a rapidly growing trend in emergent technologies. AI algorithms are already in use in modern society and making human life easier; technologies such as voice recognition, car navigation systems, chatbots, social networking, purchase suggestions, robotics in healthcare, and many more, rely on these algorithms to perform the task they were specifically designed to accomplish. So far, these technologies are considered positive by various smart tech enthusiasts who believe that AI can be even further developed for the greater good. However, the question many of the wary poses is: will researchers and scientists proceed in developing artificial intelligence technologies to the point where humans lose the ability to understand and control the functioning of a super-intelligent machine? Although we are still too far from creating an AI technology that surpasses the capacity of the human brain, the current discussion mostly focuses on ethics, morality, and limitations.