The emergence and consecutive entrance of Generative AI (particularly ChatGPT) into the mainstream has provoked all kinds of reactions, from excitement to apprehension, but it has not been definitely decided whether it is a boon or a bane yet. We wish to voice the still unmentioned relation between AI accessibility and social injustice. So far, the initial access to tools such as ChatGPT has been free or low-cost. This is predicated on the availability of open-source or inexpensively sourced data. As the value of models hinges upon high quality, diverse data, the demand for it will increase, resulting in the rising costs of its procuration. We worry that the free models will then turn into expensive commodities, limiting their use only to the privileged entities. This potential shift causes major concerns about ethics and social equity, with the concept of unbundling being one of the potential solutions.

It is important to realize that false news is more than just a deception. Sadly, it is impossible to confirm every bit of information we come across. A normal human impulse is to accept any information that looks sufficiently convincing, relevant, or exciting. In doing so, we often do not realize that we have just contributed to the misinformation of the community to which we belong. As a result, fake news happens to be our collective error. In this paper, we propose an architecture for combating the disinformation problem using a hybrid-based approach. We demonstrate our preliminary results on the health-related fake news dataset.

Nowadays, we can see how social media networks are developing. We must accept the fact that the opinion of an expert is frequently just as valuable and crucial as that of a non-expert. It is feasible to see how traditional media is undergoing changes and processes that diminish the importance of the traditional ”editing office” and place a growing focus on journalists’ remote labour.As a result, social media has evolved into a component of national security since fake news and disinformation spread by nefarious individuals can influence readers and spark pointless debates on social issues that are inherently unimportant. This has a domino effect, instils dread in the populace, and eventually puts the security of the state in jeopardy.Recently, deep machine learning techniques have proven to be one of the technologies thought to be an effective way to combat the false news problem. However, due to shortages of labelled data, these methods often have poor model generalization capabilities when applied in real-world cases.In this paper, we address this problem by utilizing lightweight model stitching, which serves as a foundation for a hybrid method for fake news detection. Six distinct benchmark datasets have been used in our varied experiments. The outcomes are promising and pave the way for additional studies.

The domain of Artificial Intelligence has become ubiquitous across a wide plethora of domains and is now an integral part of the daily life of the ordinary citizen. While the need for increased transparency of the highly accurate black-box model is an important and very active area of research, the produced explanations themselves might not always be accurate. The measures to assess the quality of explanations are an important research topic. In this paper, a set of extensive experiments is performed to evaluate the stability of SHAP explanations under conditions of different noise types and different noise intensities as an effort to build a formal way of assessing the quality of explanations provided by the SHAP algorithm. The experiments are performed on four different datasets, with three different noise types at four different strength levels. The impact of the scenarios on SHAP explanations is reported, the implications for the evaluation of explainability methods are elaborated upon, along with the significance of the results for the SHAP method of explanations. The future directions are laid out thereafter.

Explainability, Transparency, and Fairness are now recognized foundations that have altered the landscape of the artificial intelligence domain. Excellent performance is no longer enough if the decisions of a system can upset the life of a regular citizen. The stakeholders of a utilised system must know whether they can trust the results the system provides, for both ethical and legal reasons. This can be made possible with Explainable Artificial Intelligence (xAI). It helps detect errors that could compromise the effectiveness of an intelligent system. It can discover biases present in the data that could lead to unfair treatment. It also provides novel insights regarding the data and the investigated domain. The applicability and benefits of Explainable Artificial Intelligence are explicit; however, scalable and simple-to-use solutions are scarce. Therefore, this paper proposes a new approach to explainability called "Fast Hybrid Oracle-Explainer". This method is a significant extension and improvement over previous approaches utilizing Comprehensible Decision Trees, expanded with the Nearest Neighbors Search algorithm. Compared to its predecessors, the proposed method maintains a similar level of agreement while drastically reducing the time necessary to provide an explanation. Furthermore, it still offers concise and intuitive decision-tree-based explanations. This paper presents details of this new approach in the context of an Intrusion Detection System. The soundness, clarity, and performance of the proposed method are proven experimentally.