Human-Centered Al (Also) for Humanistic.

Francesca Mangili, Alessandro Facchini

Abstract: Since the so-called Deep Learning revolution, recent advances in Artificial Intelligence (AI) obtained through Machine Learning (ML), that would have been impossible without the accumulation of huge quantities of data and continually improving and accelerating computer hardware, are promising to change much of what we do and how we do it in domains as different as scientific research, education, arts, and management. Those promises, which with the wide accessibility of tools stemming from generative AI have suddenly become very tangible for everyone, have sparked many hopes and expectations, such as the possibility of enhancing productivity and tackling complex problems such as climate change and poverty. At the same time, many ethical and more broadly societal concerns have emerged. In this chapter, we will try to make sense of these fears and see how we can try to counter them. First of all, we will clarify what AI and ML are, what makes ML systems and models so peculiar, and for which tasks and purposes they can be used, taking examples from domains such as management. Elucidating the nature (ontology) of these technological artefacts and the whole ML development cycle will then enable us to clarify where the critical points lie, both from an ethical but also cognitive and epistemic perspectives. In fact, among other things, what one can observe is that often, when deployed, AI systems are under-used or not used at all, one of the reasons commonly put forward being that it is difficult for humans to estimate to what extent to trust outputs coming from algorithms whose behavior is difficult, if not impossible, to understand. We will then see that one way to overcome such issues is to advocate for a human-centered perspective on the design and development of ML systems, in which general ethical principles but also the specific needs, concerns, and values (ethical, epistemic, and cognitive) of the various stakeholders should be taken into account in the design, development, and deployment of a ML model. In doing so, we align with the core tenets of humanistic management.