The use of algorithms and automation brings about many benefits, but it also poses risks, as shown in high-profile cases of harm associated with their use. Applying AI ethics principles could have prevented these harms from occurring. Cases that highlight the risks associated with algorithms include the COMPAS tool, Amazon's scrapped resume screening tool, and Apple's algorithm used to determine credit limits. AI ethics principles could have been useful in mitigating these issues if bias assessments and checks for differential accuracy for subgroups were conducted. It stresses the importance of transparency and explainability when it comes to automated decision tools and the assurance of algorithms to reduce the harm that can result from their use.

Impact assessments, including Algorithmic Impact Assessments (AIAs) and Data Protection Impact Assessments (DPIAs), can be used to determine the potential risks and harm associated with a system. AIAs are used to identify the risks associated with the use of an automated decision system, while DPIAs are used to assess data management strategies. These assessments are increasingly being used to determine the impact of AI systems on users and stakeholders. EU AI Act requires AIAs to determine significant risks posed by high-risk systems to fundamental rights, health, or safety. A higher impact system is subject to tighter regulations and stricter requirements, and low impact systems are subject to more lenient requirements.

The rise in AI use has led to concerns about its legal, ethical, and safety implications, resulting in the emergence of AI ethics. Algorithm auditing is an approach to mitigating potential harm that can arise from high-risk AI systems such as those in healthcare, recruitment, and housing. Auditing involves assessing an algorithm's safety, ethics, and legality and consists of five stages of development: data and set up, feature pre-processing, model selection, post-processing and reporting, and production and deployment. Following the audit, assurance is the process of determining whether the AI system conforms to regulatory, governance, and ethical standards, including certification, impact assessments, and insurance. Whilst auditing contributes to assuring an algorithmic system, governance and impact assessments are also essential.

AI ethics is a new field concerned with ensuring that AI is used in an ethical way, and it draws on philosophical principles, computer science practices, and law. The main considerations of AI ethics include human agency, safety, privacy, transparency, fairness, and accountability. There are three major approaches to AI ethics: principles, processes, and ethical consciousness. These include the use of guidelines, legislative standards and norms, ethical by design, governance, and integration of codes of conduct and compliance. AI ethics aims to address concerns raised by the development and deployment of new digital technologies, such as AI, big data analytics, and blockchain technologies.

The UK government has proposed a pro-innovation framework for regulating artificial intelligence (AI) that is context-specific and based on the use and impact of the technology. The government plans to give broader direction for key principles relating to transparency, accountability, safety, security and privacy, a mechanism for redress or contestability of AI, and delegate responsibility for developing enforcement strategies to appropriate regulators. The approach aims to provide flexibility for regulators and avoid introducing unnecessary barriers to innovation, with regulators focusing on high-risk applications. The framework welcomes stakeholder views by September 26, 2022, ahead of a white paper with more granular details and implementation plans towards the end of the year.