It's no secret that the field of Artificial Intelligence (AI) is fraught with scandals, biases and limitations. There's also no shortage of attempts to fix these issues, whether coming from tech, mathematics, ethics, or even design. What's becoming more and more clear is that there will never be a one-size-fits all solution to these problems, and instead of trying to reinvent the wheel, the field could benefit greatly from taking advantage of existing movements and trends moving towards human-centeredness and inclusivity. In a broad sense, participation means to take part in something. One of the important benefits of stakeholder participation in the field of AI is to more evenly distribute the power of decision-making and having an influential voice among the parties affected by a technology or intervention and especially those experiencing "structural oppression" or "systemic disadvantages".

Neural networks and machine learning algorithms have been used in materials science and engineering for some years now and have even yielded successes in developing new materials and novel manufacturing methods. However, the majority of this research is based upon learning data sets that try to link numerical materials property data to the manufacturing process variables. Such approaches have limited potential because the microscopic structure of the materials that actually determines the properties and its evolution during processing is not taken into account explicitly. As a result, the trained machine learning models are able to interpolate well the possibilities that fall with the domain of the training data, but often fail to make viable predictions outside of it. Thus, potentially superior novel processing methods and materials with improved properties can remain undiscovered.

I've recently published an article explaining why the field of artificial intelligence could greatly benefit from the approaches of design fields. I believe that involving different stakeholders early on in AI-based projects is the most effective technique to battle the various kinds of biases and shortcomings embedded within AI systems. By starting at the very beginning, involved stakeholders and their insights can help shed light on inequitable processes of design, on systemic biases buried in data-sets and how they can disadvantage different groups of people, on use-cases and experiences that might otherwise be overlooked, and on potential consequences and implications that even the most rigorous testing might not capture. Instead of creating overly-specific, bespoke solutions tailored to a specific project, or trying to focus on completely eradicating one of those problems, my approach is to bring the voices that matter into the design process and let them help the expert team navigate all these challenges. By providing a more generalized toolkit and methodology for supporting these stakeholders, different problems in different projects and during different phases can all be addressed in whatever way is needed.

In collaboration with the European Institute of Innovation and Technology (EIT) and Delta Capita Ltd, the School of Mathematics, Computer Science & Engineering is offering a PhD studentship. The studentship falls under the new EIT-Digital Industrial Artificial Intelligence Doctoral Programme at City, University of London. The studentship consists of a full fee waiver and a stipend of £18K per year, for four years. As part of their studies and training, students will spend time at City, University of London, EIT-Digital London Co-Location Centre and Delta Capita Ltd premises. In addition, over the 4 years of study, the PhD will spend between 3 and 6 months abroad to enrich their research experience, for which a supplementary budget is available.