# AI Ethics--A Bird's Eye View
## Moving on from Principles
### Technical Approaches to Specific Issues  
Since the beginning of the \"Big Data\" era, data collection, maintenance, and sharing have received specific attention regarding governance [78] and consent [79], while according to the \"garbage in-garbage out\" maxim, much attention has been given to detecting and mitigating bias in algorithms and datasets [80; 81]. Indicatively, the proposed solutions include: data documentation via the introduction of datasheets for datasets in analogy with what exists in the electronics industry; documenting the creation motivation, composition, collection processes, preprocessing/cleaning/labeling, potential uses, distribution, and maintenance of a dataset [82]; using trusted third parties to hold sensitive data, identify data bias beforehand, or act as a pre-processor and giving access accordingly, e.g., by enforcing anonymization [83]; and the use of mechanisms such as data trusts, i.e., legal structures that provide mechanisms \"for individuals to pool their data rights (or data) into an organisation--the trust--which is then tasked with governing its use according to conditions stipulated at its establishment\" [84] (p. 8). All the above aim at ensuring principles such as safety and security, privacy, fairness, and accountability, which are among the most commonly mentioned in the guidelines [58; 59].  
Another commonly mentioned principle, namely explainability/transparency (also linked with accountability), is addressed by a substantial body of work in eXplainable AI (XAI). XAI aims to produce explainable models or effective individual explanations, thus enabling users to understand and trust the system (for a comprehensive overview, see [85]). While XAI is often connected to EU legislation -GDPR and \"the right to explanation\" [86], whether this includes a right to explanation from automated procedures is a debated issue [87].  
Security and privacy are two issues that have been extensively researched since the \"Big Data\" data era with techniques such as anonymization and encryption routinely applied; a glossary of relevant terms as well as an overview of current and next-generation methods for federated, secure, and privacy-preserving artificial intelligence can be found in [88]. The authors define secure AI as the combinations of methods that are concerned with safeguarding algorithms, while privacy-preserving AI is when the system allows the processing of data without revealing the data itself. The two of them ensure data and algorithm sovereignty, while enabling trustworthiness and transparency, as well as integral computational processes and results. While the authors focus on medical imaging applications, the methods that are described can be widely applied.  
A comprehensive review of the state of the art in privacy issues and solutions for machine learning is presented by [PERSON] et al. [89]. AI is examined here as either the object of privacy concerns or the tool to avert or instigate privacy attacks. The authors offer a comprehensive survey on AI/ML and privacy, addressing all three cases and identifying the key challenges. In the first case, where the objective is to ensure the privacy of either the training data or the algorithm, they classify the attacks and protection schemes and assess the existing solutions. Regarding the other two cases, namely AI aided privacy protection and AI-based privacy attack, they conclude that while the first one is gaining momentum, the research about the latter is in infancy but it is expected to have great future development.