Machine Learning (ML) is increasingly used to implement advanced applications with non-deterministic behavior, which operate on the cloud-edge continuum. The pervasive adoption of ML is urgently calling for assurance solutions assessing applications non-functional properties (e.g., fairness, robustness, privacy) with the aim to improve their trustworthiness. Certification has been clearly identified by policymakers, regulators, and industrial stakeholders as the preferred assurance technique to address this pressing need. Unfortunately, existing certification schemes are not immediately applicable to non-deterministic applications built on ML models. This article analyzes the challenges and deficiencies of current certification schemes, discusses open research issues, and proposes a first certification scheme for ML-based applications.

How to call the wider group of companies which bring to market innovations that could replace existing technologies with more environmental-friendly ones, even if their primary goal doesn't strictly align with the definition of Green Tech? Green Tech has been around for the past twenty years but has only gained traction recently due to the rising concerns about global warming. The green tech and sustainability market was valued at $11.2 billion in 2020, and it is expected to reach $36.6 billion by 2025. Strictly speaking, green technology or "Green Tech" is a "technology whose use is intended to mitigate or reverse the effects of human activity on the environment" explains the Oxford English Dictionary. For the Greentech alliance, Green Tech companies are founded with the purpose of protecting the environment, have a science-based, measurable impact and do not engage in greenwashing. This definition mostly encompasses companies involved in recycling, producing clean water, or using alternative energy sources like solar or wind power.

The European Commission's (EC) proposed Artificial Intelligence (AI) regulation – a much-awaited piece of legislation – is out. While this text must still go through consultations within the EU before its adoption, the proposal already provides a good sense of how the EU considers the development of AI within the years to come: by following a risk-based approach to regulation. Other use-cases such as FRT for authentication processes are not part of the list of high-level risks and thus should require a lighter level of regulation. While technology providers have to maintain the highest level of performance and accuracy of their systems, this necessary step isn't the most critical to prevent harm. The EC doesn't detail any threshold of accuracy to meet, but rather requires a robust and documented risk-mitigation process designed to prevent harm.