Data is the foundation of any scientific, industrial, or commercial process. Its journey flows from collection to transport, storage, and processing. While best practices and regulations guide its management and protection, recent events have underscored their vulnerabilities. Academic research and commercial data handling have been marred by scandals, revealing the brittleness of data management. Data is susceptible to undue disclosures, leaks, losses, manipulation, or fabrication. These incidents often occur without visibility or accountability, necessitating a systematic structure for safe, honest, and auditable data management. We introduce the concept of Honest Computing as the practice and approach that emphasizes transparency, integrity, and ethical behaviour within the realm of computing and technology. It ensures that computer systems and software operate honestly and reliably without hidden agendas, biases, or unethical practices. It enables privacy and confidentiality of data and code by design and default. We also introduce a reference framework to achieve demonstrable data lineage and provenance, contrasting it with Secure Computing, a related but differently orientated form of computing. At its core, Honest Computing leverages Trustless Computing, Confidential Computing, Distributed Computing, Cryptography, and AAA security concepts. Honest Computing opens new ways of creating technology-based processes and workflows which permit the migration of regulatory frameworks for data protection from principle-based approaches to rule-based ones. Addressing use cases in many fields, from AI model protection and ethical layering to digital currency formation for finance and banking, trading, and healthcare, this foundational layer approach can help define new standards for appropriate data custody and processing.

Several African countries are developing artificial intelligence (AI) strategies and ethics frameworks with the goal of accelerating responsible AI development and adoption. However, many of these governance actions are emerging without consideration for their suitability to local contexts, including whether the proposed policies are feasible to implement and what their impact may be on regulatory outcomes. In response, we suggest that there is a need for more explicit policy learning, by looking at existing governance capabilities and experiences related to algorithms, automation, data, and digital technology in other countries and in adjacent sectors. From such learning, it will be possible to identify where existing capabilities may be adapted or strengthened to address current AI-related opportunities and risks. This paper explores the potential for learning by analysing existing policy and legislation in twelve African countries across three main areas: strategy and multi-stakeholder engagement, human dignity and autonomy, and sector-specific governance. The findings point to a variety of existing capabilities that could be relevant to responsible AI; from existing model management procedures used in banking and air quality assessment to efforts aimed at enhancing public sector skills and transparency around public–private partnerships, and the way in which existing electronic transactions legislation addresses accountability and human oversight. All of these point to the benefit of wider engagement on how existing governance mechanisms are working, and on where AI-specific adjustments or new instruments may be needed.

The use of artificial intelligence and algorithmic decision-making in public policy processes is influenced by a range of diverse drivers. This article provides a comprehensive view of 13 drivers and their interrelationships, identified through empirical findings from the taxation and social security domains in Belgium. These drivers are organized into five hierarchical layers that policy designers need to focus on when introducing advanced analytics in fraud detection: (a) trust layer, (b) interoperability layer, (c) perceived benefits layer, (d) data governance layer, and (e) digital governance layer. The layered approach enables a holistic view of assessing adoption challenges concerning new digital technologies. The research uses thematic analysis and interpretive structural modeling.

June 2018 was an intense time in Vietnam when one saw the role of social media in revealing and facilitating the (dis)congruity between connectivism, collectivism, and conservatism. On June 12, the National Assembly of Vietnam passed the controversial Cybersecurity Law by a landslide of 86 percent agreement despite widespread public dissent, including an online petition signed by more than sixty-five thousand people. The new Cybersecurity Law was said to further restrict the already limited freedom of expression in Vietnam and grant too much power to the police in surveying and punishing online citizens.