In the era of Artificial Intelligence (AI), we face a crucial question: how can we ensure that this technology evolves in an ethical and responsible direction? AI offers incredible benefits, but without adequate oversight, it can also pose serious risks to society, ranging from job losses to privacy violations and even the perpetuation of discrimination based on race or gender.
In a study conducted by IBM, it was found that generative AI poses different entry barriers compared to traditional AI models: data privacy concerns (57%) and trust and transparency issues (43%) are the main barriers to generative AI according to IT professionals in the surveyed organizations. However, it is essential to recognize that solutions already exist to promote ethical AI.
First, transparency is fundamental. The algorithms used in AI systems must be understandable and explainable. Too often, decisions made by algorithms remain mysterious, creating a gap between AI designers and the public. By making algorithmic decision-making processes transparent, we can increase public trust and ensure greater accountability among users. For instance, the insurance sector seeks transparency in its decision-making criteria for its customers. The explainability of models also serves as a defense against the risk of a model being usurped by a cyber-attacker; it allows for the detection of any behavior deviating from a company’s charter and better identification and countering of exploitation or manipulation attempts.
Second, diversity and inclusivity must be integrated from the design phase of AI systems. Too often, cultural and social biases are amplified by the choice of data used to train algorithms, leading to discriminatory outcomes. By promoting diversity within development teams and adopting inclusive practices throughout the process, we can mitigate these biases and create more balanced systems. For example, we can question the data sets used by LLM (Large Language Model) providers and their specific biases (OpenAI vs. Mistral). By broadening the diversity of the data used to train these models and ensuring they faithfully represent the plurality of human perspectives and experiences, we can help reduce distortions and biases in generated results. This proactive approach is essential to ensure that AI technologies reflect and respect the diversity of the societies in which they are deployed.
Finally, education and awareness are essential to creating a culture of responsibility around AI. Users must be informed about the ethical implications of this technology and encouraged to participate in the dialogue on how to guide it responsibly. Likewise, AI professionals need to be trained on ethical issues and equipped with the skills necessary to design and implement AI systems that consider moral and social concerns. In practice, more and more software companies integrating AI now have ethical committees tasked with ensuring the alignment of future developments with strict ethical standards. These initiatives are crucial, especially in sensitive industries like cyber defense, to ensure that every technological advance is guided by fundamental ethical values, not just commercial objectives or technical considerations.