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The science of complexity – Expert Q&A

International researchers specialising in complex systems like disease spread, creativity, and AI ethics are visiting New Zealand.

The Capital City Complex Systems Symposium takes place on 13-14 February in Wellington.

The SMC asked the keynote speakers to preview their talks. 

Associate Professor Juyong Park, Graduate School of Culture Technology, KAIST (Korea Advanced Institute of Science & Technology), comments:

The science of creativity:

“What do Einstein’s theory of relativity, Beethoven’s sonatas, and Katherine Mansfield’s stories have in common? They are products of human creativity. Cherished across generations as one of the most unique and important human abilities, creativity allows us to overcome many challenges and advance civilisation.

“What is creativity? David Bohm, a 20th-century giant in quantum physics, said it is the ability to see beyond the often messy outside looks of the everyday world and find higher order. In science, this is in the elegant scientific theories that teach us how things move; in art, this is in the tasteful artworks that express human emotions and experiences. And such higher order gives off harmony and totality, letting us even sense beauty.

“Can we understand creativity in the scientific way? If we could, the benefits could be huge — just as the science of energy let harness its power and solve great problems, the science of creativity could let us solve even greater and more complex problems.

“Here we will introduce network creativity as the starting point, which allows us to quantify two of the most notable features of creativity — being new (that is, different from the past) yet impactful (shaping the future) — and show how it can be applied to study human creative enterprises, in this case classical piano music.”

No conflict of interest.

Dr Juniper Lovato, Research Assistant professor in Computer Science, University of Vermont (USA), comments:

Consent and privacy in social media:

“In online social networks, your personal data is not yours alone; it is intertwined with that of your friends and family. When you consent to allow a website or application to access your profile, it exposes not only your data but also that of others—without their explicit consent. This raises concerns about the effectiveness of the traditional consent model, especially in social networks where obtaining informed consent from all affected users is not currently possible.

“My presentation explores an alternative approach called “distributed consent.” Instead of relying solely on individual consent, this model considers the consent and security settings of your social contacts. We also introduce a “consent passport” that coordinates your security settings across platforms. Our simulations of these new settings indicate that if widely adopted, these measures can thwart unwanted data exposure through our contacts, allowing privacy-conscious individuals to maintain connectivity while safeguarding their personal information. This study is a step towards redefining how we navigate privacy in the interconnected landscape of online social networks.”

AI-generated art:

“Generative AI tools are used to create art-like outputs and sometimes aid in the creative process. These tools have potential benefits for artists, but they also have the potential to harm the art workforce and infringe upon artistic and intellectual property rights. Without explicit consent from artists, Generative AI creators scrape artists’ digital work to train Generative AI models and produce art-like outputs at scale. These outputs are now being used to compete with human artists in the marketplace as well as being used by some artists in their generative processes to create art.

“We surveyed 459 artists to investigate the tension between artists’ opinions on Generative AI art’s potential utility and harm. This study surveys artists’ opinions on the utility and threat of Generative AI art models, fair practices in the disclosure of artistic works in AI art training models, ownership and rights of AI art derivatives, and fair compensation. Results show a majority of artists believe creators should disclose what art is being used in AI training, that AI outputs should not belong to model creators, and express concerns about AI’s impact on the art workforce and who profits from their art. This highlights the need for ethical discussions to balance the benefits and risks of emerging AI tools.”

No conflict of interest.

Dr Joel Miller, Associate Professor of Mathematics, La Trobe University (Australia), comments:

Infectious disease modelling and ethical dilemmas:

“We were all impacted by the COVID-19 pandemic. In early stages, the impact in New Zealand and Australia was mostly through restrictions on what ordinary people could do. The elimination of COVID-19 and the delay in reintroduction had significant benefits for many people, with many lives saved. However, the restrictions caused significant harm to many people, many of whom were not in a high risk group.

“This raises an important ethical dilemma – it’s widely accepted in society to put restrictions on some people in order to protect others (e.g., alcohol limits on drivers). The appropriateness of a restriction and the associated penalties depends on the damage that an individual would cause by violating the restriction.

“During the early stages of the COVID-19 pandemic, mathematical modelling played a large role in guiding policy design. These decisions were often made with great urgency and with limited information. Researchers are now improving mathematical modelling methods to provide decision-makers with better information about the ethical implications of their policies — helping them better understand the tradeoffs in their decisions.”

Conflict of interest statement: “I received some funding from the Australian government for modeling work in early stages of the pandemic.”

Dr Ciro Cattuto, Director, ISI Foundation (Italy), comments:

Wearable proximity sensors:

“Data on human mobility and proximity have proven valuable to help us get ahead of epidemics and pandemics. Mobility data inform mathematical models of infectious disease spread and enable advanced capabilities for forecasting public health crises and designing interventions to mitigate their impact.

“The experience of deploying digital contact tracing for COVID-19 has exposed the complexity of carrying out a public health intervention that leverages the citizens’ personal devices and has exposed the complex nexus between public health, private digital platforms, citizens’ trust, and public discourse.

“On the one hand, technical means are available to extract value from data without compromising privacy and data protection; on the other hand, the non-technical, non-digital aspects of digital contact tracing have critically limited its impact in many countries. Moving forward requires a better understanding of data governance for the public interest and new policies that can support transparent, responsible, and sustainable data collaboration.”

No conflict of interest.