Titled “AI Ethics at the Intersection of Nutrition and Behavior Change,” the new seven-step guidelines aims to address security and privacy concerns, while highlighting the potential of AI to increase access and affordability of personalized nutrition solutions.
It also highlights the potential of AI to efficiently analyze large data sets and identify hidden patterns that could enable a transition to preventative health solutions using nutrition.
The report concludes that developing ethical AI solutions requires training systems using data representative of diverse cultures and preferences, using continuous human oversight and stakeholder collaboration, while ensuring compliance with AI regulations.
“The reason I’m proud of this article is because of all the published frameworks on AI ethics, none have focused specifically on nutrition,” said Mariette Abrahams, CEO and founder of Qina. “Consumers prioritizing their health. , and we all need to eat, we must ensure that entrepreneurs creating nutrition and wellness solutions do not underestimate the impact of these digital tools on our health, our behaviors and our society.
“We hope businesses will use this framework to internally assess where they stand against the 7 pillars and where they need to improve or seek external help.”
AI challenges and opportunities
The personalized nutrition market could reach $64 billion by 2040 (UBS, 2020).
Coupled with the recent increase in adoption of technologies such as wearable devices and the growing implementation of AI in healthcare, there is renewed potential for the widespread application of personalized nutrition, according to Qina’s report.
However, significant costs, limited scientific support, large resources and data sets required, and lack of consumer confidence represent major challenges for the sector. In fact, 75% of customers reported being seriously concerned about their ability to protect the privacy of their personal health information, according to a 2022 report from the American Medical Association.
The framework
Qina’s ethical framework includes the seven interrelated principles of data: AI system, human-centered, organization, education and training, people and planet, and regulation.
Under the data pillar, the report highlights the need for AI systems to be trained using a comprehensive and diverse dataset aligned with the nutritional guidelines of the target population, while continually integrating research most recent academics.
“The AI solution should also leverage existing behavioral data, such as information on individuals’ eating habits, preferences and lifestyle choices, to better understand and predict user behavior in real-world contexts “, he says.
The paper highlights the need for continuous human intervention and monitoring for personalized and precise AI nutrition that involves nutritionists, data scientists and behavioral scientists.
Abrahams noted that few companies share AI knowledge and data among stakeholders, emphasizing their “social responsibility” to do so to improve future solutions.
The report highlights the importance of users’ freedom in making final decisions consistent with their individual values and preferences.
“A user may receive a range of meal suggestions but ultimately decides what to eat, ensuring that the AI supports rather than dictates dietary decisions, an approach taken by ZOE,” the authors explained, highlighting the need to take into account differences in cultures, dietary needs and preferences and health conditions.
Furthermore, compliance with legal and regulatory standards such as the AI Act (introduced in December 2023) is essential for the transparency and security of these systems, as well as for compliance with the General Data Protection Regulation ( GDPR) and the Data Protection Act 2018. (DPA 2018) to ensure data protection.
For the future
The authors noted that future AI systems will require increased transparency, inclusion, and security to ensure successful implementation as health improvement tools. They emphasized that nutrition education and training must evolve to include design thinking, creative problem solving, and behavioral innovation.
“They should equip the next generation with the skills, tools and mindset to solve real-world problems,” Abrahams said. “Practitioners are trained in a very scientific way, which is great, but we should also learn other skills that not only make practitioners more marketable, but also help them contribute more widely to new product development.”
“We are also trained in cognitive behavioral therapy, but that is only one aspect of behavioral science,” she added. “We need practitioners to understand much better and more deeply what drives behavior and engagement, particularly from a digital perspective.”
