Introduction
Recent developments in Artificial Intelligence, particularly in Generative AI, have created new opportunities in multiple sectors, from finance to the pharmaceutical industry, etc. At the same time, many doubts and questions have been raised regarding how to build AI systems that adhere to principles of Responsible AI, such as equity, inclusivity, safety, and privacy. Indeed, it is increasingly crucial to understand and define the social consequences of using AI systems and how they can impact people’s lives.
Human-Centric AI
The Human-Centric AI approach prioritizes the ethical and social impact on the well-being of individuals and communities influenced by Artificial Intelligence technologies, with the goal of developing AI systems that maintain responsible and correct behavior towards them through continuous Impact Assessment. AI systems are created to support human work rather than replace it, to enhance human capabilities by promoting inclusivity and respect for rights.
The Human-Centric AI approach represents a significant shift in the development of AI systems because it views these systems as a means to serve society and aims to use them to improve people’s lives. Therefore, development is guided not only by technological advancements but also and above all by the desire to align it with the values of society.
Principles
In recent years, giants like Microsoft and Google have introduced a series of principles aligned with Responsible AI:
Accuracy & Factuality
These are fundamental aspects for evaluating the performance and reliability of AI models. For traditional models, high accuracy translates into the ability to make correct and reliable predictions or decisions based on available data. With the recent expansion of Generative AI, factuality has become crucial: the content generated by models (text, images, audio, etc.) must be verified to correspond to real information, avoiding the proliferation of fake news. Generative AI models are subject to errors called “Hallucinations,” where they can generate inconsistent or false content.
Fairness & Inclusiveness
AI models offer significant opportunities for prediction, recommendations, and decision-making, ranging from book/film recommendation systems to complex algorithms predicting medical conditions. Since they are based on real data, these models can inadvertently perpetuate and amplify all biases contained in the training data. For example, we can imagine the negative impact of an unfair AI system that automatically analyzes candidates’ resumes to assign them the most suitable role. Developing AI systems that adhere to this principle poses many challenges and requires consideration of cultural, social, historical, political, and legal factors, as the definition of “fairness” is not the same for all cases.
Interpretability & Transparency
As AI systems become more prevalent in our lives, it becomes increasingly important for these systems to justify their decision-making process. This need for Explainable AI translates into a continuous effort by developers to thoroughly understand the data and training process, as well as the creation of new technologies to overcome the “Black Box” model of Deep Learning models like neural networks. Unlike traditional software that follows precise if-else rules, these models often follow mechanisms that remain incomprehensible even to developers. The goal of the Interpretability principle is to provide explanations that are understandable even to non-experts.
Privacy
Although there are cases where using sensitive data for training AI models is advantageous (for example: disease diagnosis systems trained on biopsy images of various patients), the implications of privacy and intellectual property violations must still be considered, both from a regulatory and social norms perspective. An example is facial recognition systems for security, which often use images captured without consent. Another case is ChatGPT, which states that it stores all information provided by users and can use it to improve the model, and for this reason, it was initially blocked in Italy by the Privacy Guarantor.
Safety & Security
The security and proper behavior of AI systems are extremely important, especially in contexts where they are used to make critical decisions. Ensuring safety involves a series of challenges, such as the difficulty of creating systems that are protected from restrictions but at the same time flexible enough to adapt to unexpected inputs. Some typical threats include “training data poisoning,” where the dataset used for training is manipulated, “model stealing,” where a model trained on sensitive data is copied and used by outsiders, and the use of “adversarial examples,” i.e., using specific inputs to cause an incorrect response from the model.
Applying Responsible AI
Implementing the principles of Responsible AI during the development of AI systems is an ongoing process, evolving daily and adapting to technological innovations and regulations. It’s important to keep in mind that governmental regulations, such as the EU’s GDPR, tend to lag behind new technologies, so it’s the developers’ responsibility to fill this gap between the potential of Artificial Intelligence and legislation.
The impact of AI systems on people’s well-being must be continuously evaluated using a risk-based approach, considering the worst-case scenario, where AI decisions have the most severe impact on an individual’s life. It’s also important to consider who (institutions or companies) has access to a particular AI system: a facial recognition model for security might be suitable for a governmental institution but not for a private one.
Finally, it’s essential to remember that implementing Responsible AI is a multidisciplinary process, involving engineering, mathematics, social sciences, and politics, which requires a combined effort from technical teams and human resources.
At Blue BI, we prioritize the principles of Responsible AI in all our projects, placing privacy and data security at the forefront, in line with the guidelines of the European GDPR. Our solutions, especially our Chatbots, leverage Artificial Intelligence to complement human work and enhance business efficiency.
We realize Business Intelligence & Advanced Analytics solutions to transform simple data into information of freat strategic value.
