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In recent years, artificial intelligence (AI) has made significant progress which has made it possible to implement ever more efficient recommendation systems. Are these tools, supposed to make our daily lives easier and help us in our choices, infallible? This article looks at major technological advances in AI and explores their limitations, particularly in terms of bias and ethics.
The feats of artificial intelligence
Artificial intelligence has continued to advance in recent years thanks to innovative techniques such as machine learning and deep neural networks. Examples such as ChatGPT or Midjourney perfectly illustrate this evolution, allowing the democratization of technology among the general public. The areas of application are vast: health, education, commerce, marketing, etc.
Personalized recommendation systems
AI-based recommender systems leverage available user data to provide them with choices tailored to their preferences and needs. Several online platforms, such as Goodreads, Amazon or Tertulia, thus offer book suggestions based on users’ tastes and previous readings. Likewise, social networks and streaming platforms use algorithms to recommend content that may be of interest to their members.
The limits of AI recommendation systems
Despite these promising advances, AI-based recommender systems have certain limitations and can sometimes generate errors or biases. Several experts agree that it is essential to be aware of these pitfalls to better anticipate and correct them.
Prediction errors
Like any machine learning algorithm, recommender systems are prone to errors. While some results may prove relevant, others may seem inappropriate or even completely meaningless. This may be due to insufficient, erroneous or biased data, or even imperfections in the learning models used.
- Insufficient data: if the system does not have enough information about a user, it will have difficulty providing relevant recommendations.
- Incorrect data: an error in data collection or processing may lead to inappropriate recommendations.
- Biased data: if training data reflects existing biases, recommendations will also be biased.
Algorithmic and ethical biases
AI recommender systems may also be subject to algorithmic and ethical biases. For example, a recent study showed that users tend to follow advice from a chatbot like ChatGPT even if it is supposed to remain neutral. Researchers question the impact of these tools on the formation of public opinion and decision-making, and warn against the risks of excessive influence or manipulation.
Towards a regulation of artificial intelligence
In order to prevent potential abuses linked to AI recommendation systems and to guarantee their ethics, several authorities are looking into the question of their regulation. The European Parliament is working in particular on legislation aimed at regulating the use of artificial intelligence. Once adopted, this law will better protect citizens against discrimination and invasions of privacy.
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AI recommender systems represent a major advance in various fields, but it is crucial to carefully consider their limitations and ethical challenges. By working on their continuous improvement and establishing an appropriate legal framework, it should be possible to take full advantage of their potential while minimizing the risks for society.
Sources
- https://www.jeuxvideo.com/news/1751021/ia-y-a-t-il-des-limites-a-l-avancee-technologique.htm
- https://actualnewsmagazine.com/pourquoi-les-algorithms-sont-ils-toujours-aussi-bad-pour-recommander-des-livres/
- https://www.ladn.eu/tech-a-suivre/chatgpt-peut-vous-faire-changer-davis-et-vous-ny-verrez-que-du-feu/
- https://www.euractiv.fr/section/economie/news/reglementation-de-lia-lue-franchit-une-nouvelle-etape-apres-un-vote-cle-au-parlement-europeen/
