Artificial intelligence (AI) plays an increasingly prominent role in our daily lives by reshaping the way we consume, work, and interact. While AI feeds on our constantly evolving data, a crucial question arises: who are the true artisans of its learning? This article explores the relationship between data, AI design, and the impact of this technology on our society.
AI collects data, but who selects it?
Data is the cornerstone of artificial intelligence learning. It allows these systems to perform analyses, make predictions, and learn through advanced statistical methods. However, the question of data selection is often overlooked. Who decides which data is relevant or representative?
It is essential to understand that AI algorithms do not operate autonomously. They are the result of considerable human attention. Engineers and researchers decide which data to collect and how to structure it, thereby influencing the final outcomes. This raises the issue of potential bias that may arise from data selection made according to internal criteria, often unconscious.
The illusion of technological objectivity
It is common to present artificial intelligence as a neutral and objective technology. However, this assumption is misleading. AI models are not perfect mirrors of reality. On the contrary, they are shaped by values, priorities, and human choices. AI cannot claim to understand humanity if it only reflects a part of it.
The consequences of bias in AI learning
The consequences of the choices made during the design of algorithms are already visible in several areas. For example, some facial recognition systems show reduced effectiveness in identifying individuals from various ethnic groups. Similarly, AI-based recruitment tools can reproduce historical biases by disadvantaging certain candidates, particularly women.
Even more alarming, some algorithms have been known to associate specific stereotypes by linking scientific professions to men while reserving traditional roles for women, such as domestic tasks. This incomplete representation of the world not only skews results but also leads to decisions that profoundly impact human lives.
The forgotten domains of AI
The legacy of various scientific fields reminds us of the importance of inclusive study. Historically, medical research has often neglected female specificities, addressing pathologies globally, which has led to delays in diagnosis and treatment. It is not about opposing a male medicine to a female medicine, but about understanding that the advancement of research involves a broader view of realities.
Towards a more inclusive and universal AI
Currently, only 22% of AI professionals are women, and this figure drops to 12% for researchers. Such underrepresentation calls into question the diversity of human experiences that contribute to the design of AI intended to interact with varied populations. For AI to be truly effective in understanding humanity, it must draw from a multitude of perspectives.
It is no longer sufficient to simply balance genders in the technology sector. It is also crucial to spark interest among young girls in mathematics and sciences from an early age. Shedding light on historical figures like Ada Lovelace or Marie Curie can also enhance this perception.
Fostering interdisciplinary teams
To overcome current limitations, it is fundamental to promote interdisciplinary teams where engineers, doctors, psychologists, and other professionals work together. This would encourage dialogue between different specialties and ensure that AI is designed with a perspective encompassing human complexity.
A question of diversity and humanity
Like cinema, which has enriched our view of the world through a multitude of stories and varied narratives, artificial intelligence must also meet the challenge of diversity. The power of this technology does not lie solely in its computing capabilities or the amount of data processed but also in the quality of the information it learns. AI must feed on a diversity of viewpoints and experiences to approach genuinely universal understanding.
