Artificial Intelligence, Human Evolution, and the Speed of Learning

Abstract

Accountability is an important dimension of decision-making in human and artificial intelligence (AI). We argue that it is of fundamental importance to inclusion, diversity and fairness of both the AI-based and human-controlled interactions and any human-facing interventions aiming to change human development, behaviour and learning. Less debated, however, is the nature and role of biases that emerge from theoretical or empirical models that underpin AI algorithms and the interventions driven by such algorithms. Biases emerging from the theoretical and empirical models also affect human-controlled educational systems and interventions. However, the key mitigating difference between AI and human decision-making is that human decisions involve individual flexibility, context-relevant judgements, empathy, as well as complex moral judgements, missing from AI. In this chapter, we argue that our fascination with AI, which predates the current craze by centuries, resides in its ability to act as a ‘mirror’ reflecting our current understandings of human intelligence. Such understandings also inevitably encapsulate biases emerging from our intellectual and empirical limitations. We make a case for the need for diversity to mitigate against biases becoming inbuilt into human and machine systems, and with reference to specific examples, we outline one compelling future for inclusive and accountable AI and educational research and practice.