If you've ever taken a computer security class, you've probably learned about the three legs of computer security--confidentiality, integrity, and availability--known as the CIA triad.a When we talk about a system being secure, that's what we're referring to. All are important, but to different degrees in different contexts. In a world populated by artificial intelligence (AI) systems and artificial intelligent agents, integrity will be paramount. It's ensuring that no one can modify data--that's the security angle--but it's much more than that.

The integration of Artificial Intelligence (AI) systems into technologies used by young digital citizens raises significant privacy concerns. This study investigates these concerns through a comparative analysis of stakeholder perspectives. A total of 252 participants were surveyed, with the analysis focusing on 110 valid responses from parents/educators and 100 from AI professionals after data cleaning. Quantitative methods, including descriptive statistics and Partial Least Squares Structural Equation Modeling, examined five validated constructs: Data Ownership and Control, Parental Data Sharing, Perceived Risks and Benefits, Transparency and Trust, and Education and Awareness. Results showed Education and Awareness significantly influenced data ownership and risk assessment, while Data Ownership and Control strongly impacted Transparency and Trust. Transparency and Trust, along with Perceived Risks and Benefits, showed minimal influence on Parental Data Sharing, suggesting other factors may play a larger role. The study underscores the need for user-centric privacy controls, tailored transparency strategies, and targeted educational initiatives. Incorporating diverse stakeholder perspectives offers actionable insights into ethical AI design and governance, balancing innovation with robust privacy protections to foster trust in a digital age.

With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designed to keep up with the scale and scope of data and analytics use cases. In fact, existing architectures often fail to deliver the promised value associated with them. Data mesh is a socio-technical, decentralized, distributed concept for enterprise data management. As the concept of data mesh is still novel, it lacks empirical insights from the field. Specifically, an understanding of the motivational factors for introducing data mesh, the associated challenges, implementation strategies, its business impact, and potential archetypes is missing. To address this gap, we conduct 15 semi-structured interviews with industry experts. Our results show, among other insights, that organizations have difficulties with the transition toward federated governance associated with the data mesh concept, the shift of responsibility for the development, provision, and maintenance of data products, and the comprehension of the overall concept. In our work, we derive multiple implementation strategies and suggest organizations introduce a cross-domain steering unit, observe the data product usage, create quick wins in the early phases, and favor small dedicated teams that prioritize data products. While we acknowledge that organizations need to apply implementation strategies according to their individual needs, we also deduct two archetypes that provide suggestions in more detail. Our findings synthesize insights from industry experts and provide researchers and professionals with preliminary guidelines for the successful adoption of data mesh.

Companies hire data and machine-learning professionals to help them with cutting-edge ML models. They spend often 80% of their time cleaning or dealing with data that is riddled with missing values, outliers, large load times, and a constantly changing schema. It is not uncommon for people to be far from their expectations. Data scientists may initially be enthusiastic to work on advanced models and insights, but this enthusiasm quickly fades amid daily schema changes, tables that stop updating, and other surprises that silently ruin models and dashboards. Although "data science" can be applied to many roles, such as product analytics or putting statistical models into production, there is one thing that is always true: data scientists, ML engineers, and data analysts often sit at the tail of the data pipeline.

The recent flooding of social media feeds with AI-generated "portraits" derived from databases of artists' work has renewed conversation over data ownership and the potential power AI has to supplant livelihoods in the future. The 22 million individuals and counting who have already handed over their images to the Lensa application might be fine to receive the myriad of AI-illustrated images in exchange for their data. But the fundamental rights, principles, and freedoms users are giving up during this exchange remains largely unchecked. In Web3 technology circles, much promises have been made of decentralized technologies to open up the possibility for individual ownership and monetization of data, returning power to "creators." This reflects the political ethos held by Blockchain proponents like Etherum co-founder Joe Lubin, who ostensibly seek to supplant the existing power structures of finance through "permissionless" consensus-based transaction data structures.

The COVID-19 pandemic has transformed our lives in more ways than one. It has not just alerted us to the vulnerabilities of our health systems but also how ill-equipped our education systems are to cope with disruptions of this scale. When the pandemic forced schools to shut down and learners had to completely switch to online learning systems, the transition was anything but smooth. As part of the coordinated global education response to the COVID-19 pandemic, UNESCO, UNICEF and the World Bank conducted a Survey on National Education Responses to COVID-19 school closures. According to this joint report, 108 countries reported missing an average of 47 days of in-person instruction due to school closures - the equivalent to approximately one quarter of a regular school year – a long gap in the life of a student.

The trick of Refik Anadol's Machine Hallucinations, a three-day public art installation at The Shed in New York City, is to transform the processing of data into surreal hypnosis. The immersive audiovisual exhibit towers over a cavernous 17,000 sq ft gallery in Hudson Yards, an outer ring of screens features a shimmering and chameleonic display of what looks like pixelated sand. But each square is a narrative of data: a familiar image – tree, building, lamppost, over 130m publicly available images of New York City searched and collected by Anadol and his team's algorithms – morphed into a single-colored square and then silenced by a single question: what would you do if you owned your data? The free exhibit, part of a $250m project to shift data ownership from private mega-corporations to individual users called Project Liberty, makes a tactile, sensory, emotional argument for data dignity and decentralization of internet power – concepts often so bogged down in technicality, abstraction and vagueness as to be inaccessible. The overarching aim of Project Liberty is to imagine an internet future not governed by tech CEOs, the forfeit of your data for participation, surveillance capitalism and the whims of social media companies aiming for infinite scale.

Artificial Intelligence (AI) and space are both popular subjects in the current policy climate. AI techniques are being applied to space datasets and accelerating progress in the satellite and space industry through natural language processing, machine vision and advanced analytics. The combination of AI and space could play an integral role in increasing global connectivity and closing the digital divide. AI space services face the same problems as terrestrial AI services. They are exposed to the same policy challenges when delivered through a fibre network as they are when transmitted wirelessly from a satellite.

The automation of measurement and data collection procedures, coupled with the development of vast capacity for data storage and the creation of highly sophisticated tools for analyzing and processing data, often in real time, is radically changing the world in which we live. This has prompted considerable debate, both philosophical and legal, about the right, legitimate, and proper ways to use such data. Also, since there is no absolute authority to whom we can appeal for guidance, it is important that we, the data creators, suppliers, and users, should engage with these ethical considerations. Emphasis naturally tends to be on the side of risk and protection, but we must always bear in mind the need for a proper balance between risk and benefit. Zero risk can be attained only at the cost of zero benefit, but the potential benefit from new data technologies is vast. Or, as leading data ethicists Floridi and Taddeo1 put it: "On the one hand, overlooking ethical issues may prompt ...

Blockchain, artificial intelligence and IoT are the future, but are companies ready for the legal issues relating to the new model of business? The Internet of Things obliges companies to change their models of business since one-off contractual relationships where they were selling a product are replaced by long term relationships for the provision of services with continous exchanges of data, potential liabilities and contractual issues. This is enhanced by artificial intelligence and blockchain technologies which increase the potential benefits, but at the same time also with higher reliance on the proper functioning of technologies. The shift that is happening in any business can be quite well represented in the image below of a "pizza as a service". Even the more traditional businesses might turn into a service if for instance, products are no longer purchased as part of specific order, but on the basis of a monthly fee which is calculated on the needs of the purchaser through sensors that collect information on the actual consumption of each product.