With the ever-growing use of digital technology, especially the use of big data technologies and machine learning, questions related to the proper use of data collected from social networks led to the introduction of data transparency. Hence, the authors open the discussion on crucial problems of data use “in many application areas ... dealing with personally identifiable information.” There are many cases where personal data theft has led to privacy breaches and the possibility of identity theft. This fact asks for appropriate solutions, and the authors successfully aim their study at data transparency, knowing that it is not easy to achieve. Many cybersecurity techniques have been implemented, but our almost fully networked business, social, and personal lives need a robust and decentralized approach. This study recognizes blockchain’s potential for a variety of applications, as it can “provide real-time data visibility and transparency while keeping private information secure” .
Many of us have seen the slogan “we value your privacy” (or something similar). The blockchain technology appears to make data transparent:
Network participants have the ability to access holdings and transactions of public addresses using a block explorer, used to search the blocks of a blockchain, their contents, and their relevant details. 
Additionally, trust and privacy are secured by national and international regulations. This technology is recognized as the right tool for storing personal information since it eliminates the need to give consent to use personal data. In this way, user identities will not be duplicated and distributed to service providers, social media companies, or online stores.
In the work on finding suitable frameworks and approaches for data transparency, the authors introduce, in an excellent way, the term “data transparency dimensions.” It is explained with brief descriptions of several key data transparency dimensions. For example, they explore how artificial intelligence (AI) and machine learning ethics influence data transparency in accordance with commonly accepted ethical principles. The introduction of data ethics through AI ethics-driven transparency is a really successful approach, giving business and social communities new possibilities for data transparency. In the networked decentralized infrastructure there is another infrastructure, that is, blockchain technology as an infrastructure for transparency. Hence, blockchain, a decentralized technology, “does not rely on a central point of control” and instead “relies on consensus protocols across a network of nodes to confirm any transaction performed on the network” . Thus, “participants on the network all must agree unanimously to add a new block and must do it while ensuring its integrity” .
It is amazing how the authors present their ideas about data transparency--a crucial data quality dimension based on agreed-upon data ethics principles, where blockchain technology is used as an infrastructure for efficient transparency--in an easy-to-follow way. Besides the blockchain technology issues, the authors provide a novel approach for the research community: including data ethics as a key part of AI ethics. Data ethics “refers to assessing whether data have been gathered and used according to some ethical principles”; trust and transparency are important elements. They clearly state that “transparency is critical” and “data transparency should not violate privacy and security requirements.”
This work is promotional research on data ethics that helps in building digital trust and developing adequate regulatory requirements for data transparency. It is also strongly connected to identifying universal data ethics principles for the data science community.