OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence (AGI). Since its official release in November 2022, ChatGPT has quickly attracted numerous users with extensive media coverage. Such unprecedented attention has also motivated numerous researchers to investigate ChatGPT from various aspects. According to Google scholar, there are more than 500 articles with ChatGPT in their titles or mentioning it in their abstracts. Considering this, a review is urgently needed, and our work fills this gap. Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges. Moreover, we present an outlook on how ChatGPT might evolve to realize general-purpose AIGC (a.k.a. AI-generated content), which will be a significant milestone for the development of AGI.

One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era

Organizations are increasingly employing complex black-box machine learning models in high-stakes decision-making. A popular approach to addressing the problem of opacity of black-box machine learning models is the use of post-hoc explainability methods. These methods approximate the logic of underlying machine learning models with the aim of explaining their internal workings, so that human examiners can understand them. In turn, it has been alluded that the insights from post-hoc explainability methods can be used to help regulate black-box machine learning. This article examines the validity of these claims. By examining whether the insights derived from post-hoc explainability methods in post-model deployment can prima facie meet legal definitions in European (read European Union) non-discrimination law, we argue that machine learning post-hoc explanation methods cannot guarantee the insights they generate. Ultimately, we argue that the use of post-hoc explanatory methods is useful in many cases, but that these methods have limitations that prohibit reliance as the sole mechanism to guarantee fairness of model outcomes in high-stakes decision-making. By way of an ancillary function, the inadequacy of European Non-Discrimination Law for algorithmic decision-making is demonstrated too. 

https://link.springer.com/content/pdf/10.1007/s43681-022-00142-y.pdf

Explainable artificial intelligence (XAI) post-hoc explainability methods: risks and limitations in non-discrimination law

The aim of this article is to reflect upon the future of pre-contractual information duties as a regulatory tool in EU consumer law. Until now information duties or mandated disclosures are among the most popular regulatory instruments in EU consumer legislation. However, a growing body of research from behavioural economics, psychology and neuroscience has questioned the information paradigm that constitutes a hallmark of EU consumer law. While there seems to be a broad consensus that the old information model dating back to the 1970s is no longer viable, it is less clear which regulatory approach shall replace it. This article discusses different alternative approaches that could revive or replace the information-based approach. It is argued that information duties as a regulatory tool can have a future if they adapt to the insights from behavioural research and embrace the possibilities of modern technology.Following a brief introduction, section 2 outlines the conceptual foundations of the information paradigm and shows how duties of disclosure have become a key feature of EU consumer legislation. Section 3 addresses the limitations of the information-based approach which have been identified by psychological and behavioural research. In section 4 several regulatory alternatives to the current information model are being discussed. Section 5 looks at how in the near future a more technological approach to disclosure based on Big Data analytics could lead to a transformation of the information model from standardized to personalized disclosures.

The Future of Pre-Contractual Information Duties: From Behavioural Insights to Big Data

/pdf/the-right-to-contest-automated-decisions-under-the-general-htppf6d9yk.pdf

The right to contest automated decisions under the General Data Protection Regulation: Beyond the so-called “right to explanation”

In recent years, artificial intelligence has had a tremendous impact on every field, and several definitions of its different types have been provided. In the literature, most articles focus on the extraordinary capabilities of artificial intelligence. Recently, some challenges such as security, safety, fairness, robustness, and energy consumption have been reported during the development of intelligent systems. As the usage of intelligent systems increases, the number of new challenges increases. Obviously, during the evolution of artificial narrow intelligence to artificial super intelligence, the viewpoint on the challenges such as security will be changed. In addition, the recent development of human-level intelligence cannot appropriately happen without considering whole challenges in designing intelligent systems. Considering the mentioned situation, no study in the literature summarizes the challenges in designing artificial intelligence. In this paper, a review of the challenges is presented. Then, some important research questions about the future dynamism of challenges and their relationships are answered.

https://www.mdpi.com/2076-3417/12/8/4054/pdf?version=1650189152

A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions

The alleged opacity of AI has become a major political issue over the past few years. Opening the black box, so it is argued, is indispensable to identify encroachments on user privacy, to detect biases and to prevent other potential harms. However, what is less clear is how the call for AI transparency can be translated into reasonable regulation. This Chapter argues that designing AI transparency regulation is less difficult than oftentimes assumed. Regulators profit from the fact that the legal system has already gained considerable experience with the question of how to shed light on partially opaque decision-making systems—human decisions. This experience provides lawyers with a realistic perspective of the functions of potential AI transparency legislation as well as with a set of legal instruments which can be employed to this end.

Thomas Wischmeyer

Papers

Artificial Intelligence and Transparency: Opening the Black Box