The EU is at the front of trying to regulate and avoid the negative impacts of AI, while a specific AI Act is in the advanced process of being approved, the High-Level Expert Group on Artificial Intelligence (AI HLEG) (an independent expert group that was set up by the European Commission as part of its AI strategy) released its document on “Ethics Guidelines for Trustworthy Artificial Intelligence”.

The guidelines first released in 2018 were revised and published in April 2019. Based on these specific Ethics Guidelines for Trustworthy AI, an AI system should have three main characteristics: be lawful, ethical, and robust. Seven main requirements have been identified by the experts to allow for that:

1. Human agency and oversight – Including fundamental rights, human agency, and human oversight;
2. Technical robustness and safety – Including resilience to attack and security, fallback plan and general safety, accuracy, reliability, and reproducibility;
3. Privacy and data governance – Including respect for privacy, quality, and integrity of data, and access to data;
4. Transparency – Including traceability, explainability, and communication;
5. Diversity, non-discrimination, and fairness – Including the avoidance of unfair bias, accessibility and universal design, and stakeholder participation;
6. Societal and environmental well-being – Including sustainability and environmental friendliness, social impact, society, and democracy;
7. Accountability – Including audibility, minimization and reporting of negative impact, trade-offs, and redress.

While the document from the European Commission Expert Group is one of the most relevant in addressing ethical issues for AI, many other contributions exist.  As highlighted by Hagendorff (2020), there are some issues related to the implementation of the identified guidelines: there are no mechanisms to ensure compliance with the various codes of ethics, and there are no consequences in case of deviations. Also, there is the risk to use ethics as a marketing strategy. One of the main need is to bridge the abstract ethics values with the specific technical implementations, so that we have effective ethical AI systems. Thus, the implementation of ethical guidelines should be tailored accordingly to the specific context of AI application (Floridi, 2019) and starting from the design phase, with AI ethics that look at individual situations and specific technical assemblages (Hagendorff, 2020). And this is the approach that we took for XMANAI and to prepare the XMANAI Ethics evaluation framework.

The objective is to provide a framework specific to manufacturing since also there is a lack of a specific framework for the manufacturing sector. At the same time, several studies highlight the ethical risks of introducing AI in the industrial sector such as in the case of loss of human skills (Torresen, 2018), and automated decision-making (Mpofu & Nicolaides, 2019). Another emergent risk, particularly relevant also in the manufacturing sector, is the liability and responsibility of AI activities (Coeckelbergh, 2020), which should be clearly defined. In this context, the value of XMANAI and its approach is to work on explainability, which together with transparency, is also a way to improve and answer liability concerns.