We are pleased to share insights from Burkhard Schafer, Professor of Computational Legal Theory at the University of Edinburgh, which emerged during discussions at the latest AI4People Summit. The session, titled ‘Creating a Trustworthy AI-Enabled Future,’ brought together speakers dedicated to ensuring that AI development remains fair and transparent.
This included David Danks, Professor of Data Science, Philosophy, and Policy at UCSD; Andreas Kaminski, Professor of Philosophy of Science and Technology at Technische Universität Darmstadt; Jo Pierson, Full Professor of Responsible Digitalisation at Hasselt University; Ann Gregg Skeet, Senior Director of Leadership Ethics at the Markkula Center for Applied Ethics at Santa Clara University; and Suresh Venkatasubramanian, Computer Scientist and Professor of Data Sciences and Humanities at Brown University.
To watch this discussion please click here
Trustworthiness as a Contextual, Second-Order Value
A key theme of the discussion was the need to move “trustworthiness” beyond mere technical reliability towards a richer, more layered concept. “Trustworthiness” is not a simple measurable attribute like “weight”; rather, it is a highly context- and sector-dependent “second level” value that “quantifies over” other values. We trust someone if they are honest, fair, courageous, careful, and protective—WHEN the situation requires it. Or if they display transparency, fairness, privacy, reliability, and explainability. We can also call an AI or any other system trustworthy if it displays transparency, fairness, privacy, reliability, and explainability—again, when these values are important for us in a given context.
Trust is created when we recognise that the other side is guided by the values that matter to us. This can be extended to the recognised values that guided the development of AI – we trust the AI if we recognise that its developers share the same set of values that we have.
This recognition entails vulnerability. Once we trust someone to share our values, we expect them to behave in appropriate ways either towards us or to the people we care about. This context sensitivity (sometimes we care about privacy, sometimes we don’t, etc) also means that regulatory tools like sandboxes will fall short, and only concrete real-life examples that look at everybody affected by an AI, not just its deployer and the directly affected party, are heard. Also, within company-led ethics principles, what is often still missing is environmental harm, investment in humanity, and other third-party concerns, dimensions that extend beyond those contracted in a commercial exchange.
Public vs. Private Trust and the Importance of Institutional Context
This raises the issue of context: we may not trust a private company with our personal data and only share it with their AI if there are safeguards protecting it, but we might also be happy to have this data shared, again responsibly, with a regulator who controls and oversees the company as a public sector organisation.
This “public trust” is confidence we can have in AI even when we are not directly affected by its decisions. It underpins all values – privacy safeguards an individual on social media, but also protects wider society and their interest in a functioning democracy, where people can express their opinions without the fear of immediate profiling.
Measurement, Evidence, and Scientific Framing
Developing these systems also requires a proper frame of reference, discussing them in ways that are scientific, replicable, testable, and overall sound. The ability to distinguish between reality, aspirational thriving, and “fever dreams” is important to ensure the AI ethics debate targets the right issues. This then leads to precise measurements of what the system does and, crucially, what it does not do once integrated into a real-world application. What makes AI trustworthy is not that its reliability was tested in a sandbox, but how every stage of its development and deployment prioritises the needs of the community it is built for and how they use it. This also requires complex measurement infrastructures, not controlled solely by the company that develops the AI. And whenever the measurements identify the problem, we need the right tools to address it. Trustworthiness is therefore not just an assurance that the system works reliably, it is also a recognition that systems can and will fail, and that in such cases, there are efficient tools and interventions that will be used to rectify the problem. These interventions can and often will come from third parties, including the state. Trustworthiness can only be understood correctly if we see it as a network of responsibility, with checks and balances that ensure the unity of human values is always protected, even if the AI has failed.
Making an AI fully trustworthy is beyond the company’s power, even though they can and should work towards that goal. However, trust ultimately also depends on public institutions that oversee and regulate AI beyond its developers and deployers.
Conclusions
Ethical principles and frameworks remain vital though; many organisations (including numerous universities!) either lack them entirely or fail to implement them effectively. They need to be more detailed and precise, yet also genuinely adopted by the developer’s company and the broader environment, so that their normative values align with the production process. The key here is also to emphasise the human element; we utilise technology, but we manage humans, not the other way around.
