The cosmetics industry has experienced significant transformations in recent years, largely influenced by evolving international regulations. As governments and consumers have become increasingly concerned about the safety, sustainability, and ethical aspects of cosmetic products, the industry has been forced to adapt. This opinion explores the emerging role of artificial intelligence (AI) in potentially disrupting the challenges of transparency, and the impact on brands, especially as regards claims development and compliance.

Regulations and Formulations

International regulations profoundly shape the cosmetics industry, reflecting a global trend towards stricter standards on product safety, environmental impact, and ethics. Growing concerns over potentially harmful ingredients further fuel this shift towards safer and more sustainable options. These regulations also promote transparency in labelling, compelling cosmetic companies to share more information. Consequently, a transparency trend has emerged, increasing the demand for sustainable, natural, and organic ingredients. The industry's response includes a slow transformation in cosmetic formulations to align with evolving international standards, fostering a commitment to safety, environmental responsibility, and ethical practices.

Obstacles in Product Testing

The cosmetics industry also faces several obstacles in product testing, with two of the most significant challenges being the ban on animal testing, and the requirement for robust claims evidence. Ethical concerns and a growing awareness of animal welfare prompted regulatory bodies to prohibit the use of animals in cosmetic testing (except REACH). Poor study designs and data management especially in consumer and clinical and consumer studies are also presenting obstacles. Flawed methodologies compromise the reliability and validity of test results, hindering accurate assessments of a product's safety and efficacy, and this fact has been highlighted by advertising authority judgements.

Artificial intelligence is however emerging in cosmetic product testing, i.e., in silico. Machine learning algorithms can in principle analyse vast amounts of data quickly and accurately, improving the efficiency of safety assessments. AI-based models can predict the potential toxicity of ingredients, analysing their molecular structures. It could be used to identify patterns in adverse reactions, and even perhaps suggest alternative formulations? This would enable companies to identify potential risks early in the development process. Additionally, with human input, AI might be able to enhance the design and execution of clinical trials, ensuring more robust and reliable results.

AI holds potential in transforming cosmetic product testing, yet its adoption presents challenges for brands. Small companies may find the initial investment and expertise required as barriers. Interpretability of AI models also raises transparency and accountability concerns in decision-making, compounded by the challenge of navigating misinformation online. Brands must delicately balance technological advancement with consumer trust, as skepticism exists about algorithms guiding product decisions. Establishing trust through transparent communication about AI's role in testing processes becomes crucial. Brands aiming to leverage these technologies need a genuine human touch to bridge the gap between AI's capabilities and consumer confidence, emphasising the importance of authenticity in navigating the evolving landscape of cosmetic testing. This will be difficult given the poor record of brand communication to consumers generally.

Claims Compliance

AI might be able to play a role in supporting the likes of myself, in encouraging cosmetic brands to be compliant with the cosmetic claims regulations, by addressing some aspects of the various claims criteria:

• Evidence - In terms of data analysis, AI could analyse vast amounts of data related to cosmetic ingredients, formulations, and their effects. It could assess scientific literature, clinical studies, and consumer feedback to provide evidence supporting or refuting specific claims. This however will depend on the quality of the data it is analysing in the first place. In addition, AI could also build predictive models based on historical data, which may help companies anticipate potential issues or areas, where evidence for claims might be lacking. This type of approach might enable brands to strengthen their claims with robust supporting data, e.g., running a human consumer study to support bio-instrumental findings.
• Fairness - It might be bit far fetched but AI algorithms could be designed to ensure fairness in evaluating claims. This involves considering a diverse range of factors, including different skin types, ethnicities, and ages. By avoiding biased algorithms, brands can ensure that their claims are applicable and fair across various demographic groups.
• Honesty - Let’s face it, many cosmetic claims are just not fair. AI could be harnessed to analyse marketing content, product descriptions and advertisements, to ensure that claims are accurate and honest. Natural language processing (NLP) algorithms could identify misleading language and exaggerations in marketing materials, helping brands maintain honesty in their communication. AI systems, e.g., Sei, can be programmed to cross-reference claims against existing cosmetic regulations. This ensures that brands are honest in their communication and align with the legal requirements set by regulatory bodies and inspecting authorities.
• Truthfulness - Since AI can monitor social media and online platforms for consumer reviews and feedback, analysing real-time sentiments and experiences shared by users, brands can gauge the truthfulness of their claims and make necessary adjustments based on authentic customer experiences. Furthermore, implementing blockchain in the supply chain should enhance transparency. AI could be integrated with blockchain to track and verify the authenticity of specific claims throughout the production and distribution process, e.g., ‘green’ claims and packaging.

Challenges & Ethical Considerations

Encouraging claims compliance is always difficult, and so automated auditing for example might be employed for product claims and marketing materials. By continuously monitoring and evaluating claims against regulatory standards, brands should identify and rectify compliance issues in real-time. AI could also predict potential compliance issues by analysing emerging regulatory trends and updates, enabling brands to proactively adjust their claims to align with evolving regulations. Brands will also need to ensure the ethical use of AI in compliance efforts. Transparency in how AI models make decisions is crucial to build trust among consumers, regulatory bodies, and stakeholders. Moreover, wearing ignorance as a badge of honour will no longer be tolerated, since the interpretability of AI models will be of extreme importance for brands to understand how decisions are reached! This is particularly critical in regulatory environments where clear explanations for claim assessments by brands are required, meaning the difference between authorities/judges accepting claims or not.

Concluding Remarks

AI's potential in the cosmetics industry relies on a harmonious integration of innovation, regulatory adherence, and consumer trust. Brands, employing data analytics, predictive modelling, and automated auditing, will need to meet regulatory standards but also foster transparency and trust through evidence-based communication. As AI continues to advance, it is poised to become a promising tool for ensuring compliance in the dynamic yet tightly regulated cosmetics sector. However, the human factor remains a bottleneck, requiring budget negotiations for robust claims substantiation. Some brands may still resist compliance efforts due to the challenges of the internet landscape. Thus, whether AI or human-driven, ensuring compliance remains a gradual process, at least for the present.