On the 27th of April, RAND Europe carried out a virtual roundtable to present and discuss the findings of their research “Labelling initiatives, codes of conduct and other self-regulatory mechanisms for artificial intelligence applications: From principles to practice and considerations for the future”. The event brought together several stakeholders, including experts who were interviewed for the study, policymakers, researchers, and industry representatives. The Digital Trust Label was featured in this research as an example of an operational labelling initiative to denote the trustworthiness of a digital service.
During the introductory remarks at the RAND event, Isabel Flanagan, one of the main researchers, highlighted that building trust in AI products and services is vital to building support and motivating wide spread uptake of these technologies. Also, she mentioned that the increasing need to build trustworthy AI is also being recognized under policy frameworks within the European Union (EU), such as the 2019 Trustworthy AI Whitepaper and the 2021 AI Act.
In this context, commissioned by Microsoft, RAND Europe carried out a research project aiming at exploring what are the relevant examples of self-regulatory policy mechanisms for low-risk AI technologies, as well as the opportunities and challenges related to them. As part of this research, the RAND team reached out to the Swiss Digital Initiative to get a deeper insight into our Digital Trust Label, and better understand how this contributes to the trustworthiness of a digital service.
In total, the RAND team identified 36 different initiatives. However, these initiatives are widely diverse, some are still in the early stages of conceptualization, while others are already being implemented. The examples of self-regulatory mechanisms come from a wide range of countries across the globe, some with the intent of local implementation, and others aiming for a more global reach. Also, some initiatives are designed and targeted for particular sectors such as healthcare and manufacturing, or just championed certain causes, such as gender equality or environmentalism.
Moreover, the results of the research show that most self-regulatory mechanisms fall within two main categories: labels and certifications, like the Digital Trust Label, and codes of conduct. On the one hand, certifications and labels are mechanisms that define certain standards for AI algorithms and are assessed against a set of criteria, generally using an audit. These resemble the energy or food labels, as they are meant to communicate to consumers that the AI product they are using is reliable and safe. On the other hand, codes of conduct do not involve an assessment against measurable criteria and instead are composed of a set of principles and standards that an organisation should uphold when developing AI applications. Nevertheless, the two categories share some common aims, such as increasing the number of users, elevating trust by signalling reliability and quality, promoting transparency and comparability of AI applications, and helping organisations understand emerging standards and good practices in the field.
As part of the research results, The RAND team identified several opportunities within the design, development and implementation of self-regulating mechanisms:
However, as with any other technology, the opportunities always come along with new challenges. Through the research, RAND team identified and concluded that the main challenges for the regulation of AI are:
Finally, the presentation finished with some of the key learnings that the RAND team considers should be key in the future when designing, developing, or incentivising self-regulatory mechanisms for AI systems.
After the presentation of the results, the event followed with a series of roundtables and discussions on the future of AI regulations with smaller groups. Here are some of our main takeaways:
Through our experience and learnings in developing the Digital Trust Label, we can support many of the identified challenges. The Digital Trust Label is one of the first of its kind. By using a clear, visual, plain, and non-technical language, the Digital Trust Label denotes the trustworthiness of digital services in a way that everyone can understand. By combining the dimensions of security, data protection, reliability and fair user interaction, the DTL takes a holistic approach when it comes to addressing the complex question of digital trust. Recognising the fast pace of technological innovation and evolution, the DTL was developed to constantly be adapted and confront the challenges of the digital transformations.