[Vienna, November 13, 2024] — With artificial intelligence increasingly permeating every aspect of our lives, experts are becoming more and more concerned about its dangers. In some cases, the risks are pressing, in others they won't emerge until many months or even years from now. Scientists point out in The Royal Society's journal that a coherent approach to understanding these threats is still elusive. They call for a complex systems perspective to better assess and mitigate these risks, particularly in light of long-term uncertainties and complex interactions between A.I. and society.
"Understanding the risks of A.I. requires recognizing the intricate interplay between technology and society. It's about navigating the complex, co-evolving systems that shape our decisions and behaviors," says Fariba Karimi , co-author of the article. Karimi leads the research team on Algorithmic Fairness at the Complexity Science Hub (CSH) and is professor of Social Data Science at TU Graz.
"We should not only discuss what technologies to deploy and how, but also how to adapt the social context to capitalize on positive possibilities. A.I. possibilities and risks should likely be taken into account in debates about, for instance, economic policy," adds CSH scientist Dániel Kondor , first author of the study.
Broader and Long-Term Risks
Current risk assessment frameworks often focus on immediate, specific harms, such as bias and safety concerns, according to the authors of the article published in Philosophical Transactions A. "These frameworks often overlook broader, long-term systemic risks that could emerge from the widespread deployment of A.I. technologies and their interaction with the social context they are used," says Kondor.
"In this paper, we tried to balance the short-term perspectives on algorithms with long-term views of how these technologies affect society. It's about making sense of both the immediate and systemic consequences of A.I.," adds Kondor.
What Happens in Real Life
As a case study to illustrate the potential risks of A.I. technologies, the scientists discuss how a predictive algorithm was used during the Covid-19 pandemic in the UK for school exams. The new solution was "presumed to be more objective and thus fairer [than asking teachers to predict their students' performance], relying on a statistical analysis of students' performance in previous years," according to the study.
However, when the algorithm was put into practice, several issues emerged. "Once the grading algorithm was applied, inequities became glaringly obvious," observes Valerie Hafez, an independent researcher and study co-author. "Pupils from disadvantaged communities bore the brunt of the futile effort to counter grading inflation, but even overall, 40% of students received lower marks than they would have reasonably expected."
Hafez reports that many responses in the consultation report indicate that the risk perceived as significant by teachers—the long-term effect of grading lower than deserved—was different from the risk perceived by the designers of the algorithm. The latter were concerned about grade inflation, the resulting pressure on higher education, and a lack of trust in students' actual abilities.
The Scale and the Scope
This case demonstrates several important issues that arise when deploying large-scale algorithmic solutions, emphasize the scientists. "One thing we believe one should be attentive to is the scale—and scope—because algorithms scale: they travel well from one context to the next, even though these contexts may be vastly different. The original context of creation does not simply disappear, rather it is superimposed on all these other contexts," explains Hafez.
"Long-term risks are not the linear combination of short-term risks. They can escalate exponentially over time. However, with computational models and simulations, we can provide practical insights to better assess these dynamic risks," adds Karimi.
Computational Models – and Public Participation
This is one of the directions proposed by the scientists for understanding and evaluating risk associated with A.I. technologies, both in the short- and long-term. "Computational models—like those assessing the effect of A.I. on minority representation in social networks—can demonstrate how biases in A.I. systems lead to feedback loops that reinforce societal inequalities," explains Kondor. Such models can be used to simulate potential risks, offering insights that are difficult to glean from traditional assessment methods.
In addition, the study's authors emphasize the importance of involving laypeople and experts from various fields in the risk assessment process. Competency groups—small, heterogeneous teams that bring together varied perspectives—can be a key tool for fostering democratic participation and ensuring that risk assessments are informed by those most affected by AI technologies.
"A more general issue is the promotion of social resilience, which will help A.I.-related debates and decision-making function better and avoid pitfalls. In turn, social resilience may depend on many questions unrelated (or at least not directly related) to artificial intelligence," ponders Kondor. Increasing participatory forms of decision-making can be one important component of raising resilience.
"I think that once you begin to see A.I. systems as sociotechnical, you cannot separate the people affected by the A.I. systems from the 'technical' aspects. Separating them from the A.I. system takes away their possibility to shape the infrastructures of classification imposed on them, denying affected persons the power to share in creating worlds attenuated to their needs," says Hafez, who's an A.I. policy officer at the Austrian Federal Chancellery.
About the Study
The study "Complex systems perspective in assessing risks in A.I.," by Dániel Kondor , Valerie Hafez, Sudhang Shankar, Rania Wazir, and Fariba Karimi was published in Philosophical Transactions A and is available online.
About CSH
The Complexity Science Hub (CSH) is Europe's research center for the study of complex systems. We derive meaning from data from a range of disciplines — economics, medicine, ecology, and the social sciences — as a basis for actionable solutions for a better world. Established in 2015, we have grown to over 70 researchers, driven by the increasing demand to gain a genuine understanding of the networks that underlie society, from healthcare to supply chains. Through our complexity science approaches linking physics, mathematics, and computational modeling with data and network science, we develop the capacity to address today's and tomorrow's challenges.
