The ALTAI checklist as a Tool to Assess Ethical and Legal Implications for a Trustworthy AI in education

Date: April 18, 2024

Presenter: Stefano Tramacere

Location: Officine Garibaldi - il Cantiere delle Idee, Via Vincenzo Gioberti, 39, 56124 Pisa PI, Italy

The rapid proliferation of Artificial Intelligence (AI) applications in various domains of our lives has prompted a need for a shift towards a human-centered and trustworthy approach to AI. In this study we employ the Assessment List for Trustworthy Artificial Intelligence (ALTAI) checklist to evaluate the trustworthiness of Artificial Intelligence for Student Performance Prediction (AI4SPP), an AI-powered system designed to detect students at risk of school failure. We strongly support the ethical and legal development of AI and propose an implementation design where the user can choose to have access to each level of a three-tier outcome bundle: the AI prediction alone, the prediction along with its confidence level, and, lastly, local explanations for each grade prediction together with the previous two information. AI4SPP aims to raise awareness among educators and students regarding the factors contributing to low school performance, thereby facilitating the implementation of interventions not only to help students, but also to address biases within the school community. However, we also emphasize the ethical and legal concerns that could arise from a misuse of the AI4SPP tool. First of all, the collection and analysis of data, which is essential for the development of AI models, may lead to breaches of privacy, thus causing particularly adverse consequences in the case of vulnerable individuals. Furthermore, the system’s predictions may be influenced by unacceptable discrimination based on gender, ethnicity, or socio-economic background, leading to unfair actions. The ALTAI checklist serves as a valuable self-assessment tool during the design phase of AI systems, by means of which commonly overlooked weaknesses can be highlighted and addressed. In addition, the same checklist plays a crucial role throughout the AI system life cycle. Continuous monitoring of sensitive features within the dataset, alongside survey assessments to gauge users’ responses to the systems, is essential for gathering insights and intervening accordingly. We argue that adopting a critical approach to AI development is essential for societal progress, believing that it can evolve and accelerate over time without impeding openness to new technologies. By aligning with ethical principles and legal requirements, AI systems can make significant contributions to education while mitigating potential risks and ensuring a fair and inclusive learning environment.