Many influencer marketing platforms use artificial intelligence to match brands with content creators, yet offer ambiguous answers as to why certain candidates are not chosen. This lack of transparency hurts both parties, as companies lose relevant talent, and creators do not know how to improve (Abdollahi & Nasraoui, 2016).
That is, firms may lose relevant talent due to failures in the matching system, while creators are frustrated when they do not get useful feedback. According to a study by Influencer Marketing Hub (2023), 68% of influencers leave platforms that do not clearly explain their recruitment processes, which reduces the number of available candidates.
Benefits of Explainable AI
Traditional recommendation systems prioritise accuracy over explainability. However, as the European Commission (2019) points out, ‘transparency is a pillar of trusted AI’.
Applied to affiliate marketing, this would translate into content creators understanding the factors that led to their exclusion, such as low audience engagement, audience volume or segment, etc. For brands, transparent AI that explains the criteria for the inclusion or exclusion of candidates can help them understand why results may not be as expected and avoid missing out on relevant talent. Once this is understood, it is easier to refine the selection criteria.
For example, the platform Upfluence implemented a detailed feedback system that shows influencers how their metrics compare to the requirements of each campaign. As a result, successful collaborations increased by 40% in six months (Social Media Today, 2023).
Explainable AI Features
Selecting a platform with explainable artificial intelligence (XAI) can make the difference between strategic partnerships and opaque decisions that lead to frustration. In assessing the transparency of a system, there are several key aspects that determine its practicality for brands and content creators.
Firstly, the system’s ability to provide clear justifications and detailed information on the factors considered in the decision making process. This allows creators to understand what aspects of their performance need to be improved, while allowing brands to verify the consistency between results and objectives.
Setting flexibility is another essential element. Solutions that allow adjusting recruitment parameters according to each campaign’s needs are particularly valuable for correcting possible deviations and ensuring alignment with business objectives.
Additionally, the most reliable platforms provide comprehensive documentation algorithms’ operation, including methodologies for identifying and correcting potential biases. This not only builds trust, but also complies with the regulations on automated data processing.
Finally, continuous feedback mechanisms set the most effective solutions apart. Systems with regular assessments of their recommendations ensure a constant cycle of improvement according to the real needs of their users.
Explainable AI and algorithmic transparency are not just a technical issue, but a competitive advantage, so selecting tools with these characteristics
of understandability, both in terms of answers and operation documentation, flexible settings and feedback mechanisms allows for more effective and longer-lasting collaborations. Those platforms that implement clear systems will be the ones to gain influencers’ and brands’ trust.
References
Abdollahi, B., & Nasraoui, O. (2016). Explainable matrix factorization for collaborative filtering. arXiv. https://arxiv.org/abs/1609.04368
European Commission. (2019). Ethics guidelines for trustworthy AI. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
Influencer Marketing Hub. (2023). The impact of algorithmic transparency on creator retention. https://influencermarketinghub.com
Social Media Today. (2023). How Upfluence increased matches with explainable AI. https://www.socialmediatoday.com
