AI fairness is an essential topic as regards its topical and social-societal implications. However, there are many challenges posed by automating AI fairness. Based on the challenges around automating fairness in texts, our study aims to create a new fairness testing paradigm that can gather disparate proposals on fairness on a single platform, test them, and develop the most effective method, thereby contributing to the general orientation on fairness. To ensure and sustain mass participation in solving the fairness problem, gamification elements are used to mobilize individuals’ motivation. In this framework, gamification in the design allows participants to see their progress and compare it with other players. It uses extrinsic motivation elements, i.e., rewarding participants by publicizing their achievements to the masses. The validity of the design is demonstrated through the example scenario. Our design represents a platform for the development of practices on fairness and can be instrumental in making contributions to this issue sustainable. We plan to further realize a plot application of this structure designed with the gamification method in future studies.

A knowledge graph is convenient for storing knowledge in artificial intelligence applications. On the other hand, it has some shortcomings that need to be improved. These shortcomings can be summarised as the inability to automatically update all the knowledge affecting a piece of knowledge when it changes, ambiguity, inability to sort the knowledge, inability to keep some knowledge immutable, and inability to make a quick comparison between knowledge. In our work, reliability, consistency, immutability, and context mechanisms are integrated into the knowledge graph to solve these deficiencies and improve the knowledge graph’s performance. Hash technology is used in the design of these mechanisms. In addition, the mechanisms we have developed are kept separate from the knowledge graph to ensure that the functionality of the knowledge graph is not impaired. The mechanisms we developed within the scope of the study were tested by comparing them with the traditional knowledge graph. It was shown graphically and with t-test methods that our proposed structures have higher performance in terms of update and comparison. It is expected that the mechanisms we have developed will contribute to improving the performance of artificial intelligence software using knowledge graphs.

As digital data circulation increases, information pollution and manipulation in journalism have become more prevalent. In this study, a new digital journalism model is designed to contribute to the solution of the main current problems, such as information pollution, manipulation, and accountability in digital journalism. The model uses blockchain technology due to its transparency, immutability, and traceability. However, it is tough to provide the mechanisms necessary for journalism, such as updating one piece of information, instantly updating all other information affected by the updated information, establishing logical relationships between news, making quick comparisons, sorting and indexing news, and keeping the changing information about the news in the system, with the blockchain data structure. For this reason, in our study, we have developed a new data structure that provides both the immutability, transparency and traceability properties of the blockchain and can support the communication mechanisms necessary for journalism. The functionality of our proposed data structure is demonstrated in terms of communication mechanisms such as mutability, context, consistency, and reliability through example scenarios. Additionally, our data structure is compared with the data structure of blockchain technology in terms of time, space, and maintenance costs. Accordingly, while the model size increases linearly in blockchain, the model’s size remains approximately constant since the structure we developed is data-independent. In this way, maintenance costs are reduced. Since our model also has an indexing mechanism, it reduces the linear time search complexity to logarithmic time. As a result, the data structure we developed is found to have higher performance than blockchain in the journalism concept. In future studies, it is planned to test all aspects of the model with a pilot application, eliminate its shortcomings, and develop a holistic approach to the root causes of the problems in the journalism focus.

Çivi yazısı gibi özel yazı biçimlerinin yapay zeka ile okunması, görüntü işleme teknolojisinin hızla gelişmesiyle son yıllarda önemli bir çalışma alanı haline gelmiştir. Çeşitli dillerde yazılmış tabletler üzerinde gerçekleştirilmiş pek çok önemli literatür bulunmaktadır. Buna karşın, her ne kadar dünyada farklı merkezlerde 3D modelleme ve dijitalleştirme çalışmaları başlamış olsa da yapay zeka kullanılarak okunan çivi yazılı diller arasında Hititçe dili bulunmamaktadır. Literatürdeki bu boşluğu doldurmak, Hititologların tablet okuma hızlarını artırarak bilimsel çalışmalarını daha derinlemesine analiz etmelerine olanak tanımak ve manuel yöntemlerle okunan tabletlerin elle temasını azaltarak binlerce yıl öncesinden günümüze ulaşan tabletlerin tahrip olma durumunu en aza indirmek amacıyla çalışmamızda, Hitit çivi yazısı üzerinde transfer öğrenme yöntemi ile geliştirilmiş derin öğrenme modelleri denenmiştir. Bunun sonucunda model topluluğu yöntemiyle %89 doğruluk elde edilmiştir. Çalışmamızın, Hitit çivi yazısı uzmanlarının tabletler üzerindeki çalışma sürelerini kısaltması ve daha verimli işleyecek bir bilimsel sürece katkı sunması beklenmektedir.