Fact-checking is the process (human, automated, or hybrid) by which claims (i.e., purported facts) are evaluated for veracity. In this article, we raise an issue that has received little attention in prior work - that some claims are far more difficult to fact-check than others. We discuss the implications this has for both practical fact-checking and research on automated fact-checking, including task formulation and dataset design. We report a manual analysis undertaken to explore factors underlying varying claim difficulty and categorize several distinct types of difficulty. We argue that prediction of claim difficulty is a missing component of today's automated fact-checking architectures, and we describe how this difficulty prediction task might be split into a set of distinct subtasks.

The widespread use of artificial intelligence (AI) in many domains has revealed numerous ethical issues from data and design to deployment. In response, countless broad principles and guidelines for ethical AI have been published, and following those, specific approaches have been proposed for how to encourage ethical outcomes of AI. Meanwhile, library and information services too are seeing an increase in the use of AI-powered and machine learning-powered information systems, but no practical guidance currently exists for libraries to plan for, evaluate, or audit the ethics of intended or deployed AI. We therefore report on several promising approaches for promoting ethical AI that can be adapted from other contexts to AI-powered information services and in different stages of the software lifecycle.

Fake images and videos have engulfed mass communication media. This is not something recent, manipulations and forgeries have occurred since the advent of photography itself. These alterations can go from innocent retouches in an attempt to make an image visually attractive to the spread of misleading information or even the use of false media in legal instances. Accordingly, the creation of methods that can help us assure the authenticity of an image presented as non-modified is of paramount importance. In this thesis, we aim at detecting image manipulation operations using deep learning techniques. We present three methods showing the progression of our work under one common objective, i.e, the design and test of Convolutional Neural Network (CNN) initialization methods for image forensic problems with a variance stability focus for the output of a CNN layer.First, we carry out an extensive review of the state of the art in deep-learning-based methods for image forensics. From this review we can confirm that the first layer of a CNN has big impact on the final performance. Specifically, the initialization used on the first-layer filters plays an important role that should be in line with the image forensic task in hand.As our first attempt to address this research problem, we propose a low-complexity initialization method for CNNs. Taking advantage of previous methods designed for the computer vision field, we extend the popular Xavier method to design a filter that would provide variance stability after a convolution operation. This method generates a set of random high-pass filters for the initialization of a CNN's first layer. These filters allow us to better identify forensic traces which usually lie towards the high-frequency part of the image.This first approach constitutes a good staring point of our work. However, a wrong assumption, largely utilized in the research community, was made. This is corrected in our second method where we follow a different data-dependent approach and take into consideration the real statistical properties of natural images. Accordingly, we propose a scaling method for first-layer filters which can cope well with different CNN initialization algorithms. The objective remains in keeping the stability of the variance of data flow in a CNN. We also present theoretical and experimental studies on the output variance for convolutional filter, which are the basis of our proposed data-dependent scaling.Next we describe a revisited version of our first proposal now with a corrected assumption on the statistics of natural images. More precisely, we propose an improved random high-pass initialization method which does not explicitly compute the statistics of input data. We believe that such a ``data-independent'' approach has higher flexibility and broader application range than our second method in situations where the computation of input statistics is not possible.Our proposed methods are tested over several image forensic problems and different CNN architectures.Finally, during all this thesis work we took part in a challenge competition of image forgery detection organized by the French National Research Agency and the French Directorate General of Armaments. We explain in the Appendix the objectives of the challenge along with a brief description of our work conducted for the competition.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Artificial intelligence (AI) technologies offer profoundly important strategic benefits and hazards for global businesses and government agencies. One of AI's greatest strengths is its ability to engage in behavior typically associated with human intelligence -- such as learning, planning, and problem solving. AI, however, also brings new risks to organizations and individuals, and manifests those risks in perplexing ways. It is inevitable that AI will soon face increased regulation.

The use of artificial intelligence in the hiring process has increased in recent years with companies turning to automated assessments, digital interviews, and data analytics to parse through resumes and screen candidates. But as IT strives for better diversity, equity, and inclusion (DEI), it turns out AI can do more harm than help if companies aren't strategic and thoughtful about how they implement the technology. "The bias usually comes from the data. If you don't have a representative data set, or any number of characteristics that you decide on, then of course you're not going to be properly, finding and evaluating applicants," says Jelena Kovačević, IEEE Fellow, William R. Berkley Professor, and Dean of the NYU Tandon School of Engineering. The chief issue with AI's use in hiring is that, in an industry that has been predominantly male and white for decades, the historical data on which AI hiring systems are built will ultimately have an inherent bias.

The European Commission believes that its proposed Artificial Intelligence Act should become the global standard if it is to be fully effective. The upcoming AI treaty that is being drafted by the Council of Europe might help the EU achieve just that. In April the European Commission launched its proposal for an Artificial Intelligence Act (AIA). Structured around a risk-based approach, the regulation introduces tighter obligations in proportion to the potential impact of AI applications. Commissioner Thierry Breton argued that "one should not underestimate the advantage of the EU being the first mover" and emphasised that the EU is the main "pacemaker" in regulating the use of AI on a global scale. In a similar vein, the Commission's director-general for communications networks, content and technology, Roberto Viola said that "equilibrium is key to have a horizontal risk-based approach in which many voices are heard to avoid extremism and create rules that last.

Urgent action is needed as it can take time to assess and address the serious risks this technology poses to human rights, warned the High Commissioner: "The higher the risk for human rights, the stricter the legal requirements for the use of AI technology should be". Ms. Bachelet also called for AI applications that cannot be used in compliance with international human rights law, to be banned. "Artificial intelligence can be a force for good, helping societies overcome some of the great challenges of our times. But AI technologies can have negative, even catastrophic, effects if they are used without sufficient regard to how they affect people's human rights". On Tuesday, the UN rights chief expressed concern about the « unprecedented level of surveillance across the globe by state and private actors », which she insisted was « incompatible » with human rights.

The JCU Institute of Future and Innovation Studies organized a roundtable discussion called "Democracy, Technology, Artificial Intelligence, and the Future," on September 10, 2021. The discussion was the first event in the "Democracy Watch" series, which aims to provide a public forum for maintaining a critical and open dialogue about the complex relationships that exist between technology and democracy. JCU President Franco Pavoncello welcomed the participants and gave the opening remarks. Francesco Lapenta, Director of the Institute of Future and Innovation Studies, explained that with technology continually evolving, it is essential to understand its relationship with democracy. According to Lapenta, technological advances have a direct impact on democratic practices and values.

Artificial intelligence (AI) systems are becoming critical components of today's IT landscapes. Their resilience against attacks and other environmental influences needs to be ensured just like for other IT assets. Considering the particular nature of AI, and machine learning (ML) in particular, this paper provides an overview of the emerging field of resilient AI and presents research issues the authors identify as potential future work.

The United Nations' (UN) head of human rights has called for all member states to put a moratorium on the sale and use of artificial intelligence systems. UN high commissioner for human rights Michelle Bachelet acknowledged that AI can be a "force for good" but that it could also have "negative, even catastrophic, effects" if the risks It poses are not addressed. Bachelet's comments come alongside a new report from the Office of the High Commissioner for Human Rights (OHCHR). The report analyses how AI affects people's rights to privacy, health, education, freedom of movement, amongst other things. "Artificial intelligence now reaches into almost every corner of our physical and mental lives and even emotional states. AI systems are used to determine who gets public services, decide who has a chance to be recruited for a job, and of course they affect what information people see and can share online," Bachelet said.