Samantha Stone-Jovicich

As AI systems increasingly influence various aspects of human life, it is critical to ensure their development and deployment align with ethical standards and societal values. Our paper argues that integrating expertise from Humanities, Social, and Economic Sciences (HSES) into AI development is essential to achieving responsible AI. We present four compelling reasons to advocate for this integration: enhancing social legitimacy, ensuring meaningful impact, strengthening credibility and capability building. These reasons emerged from a collaborative effort involving 16 researchers from AI and HSES fields. Together, we explored the enablers and barriers to integrating our knowledge for the purpose of developing effective, responsible, and socially grounded AI products. We aim to inspire others to adopt an integrated approach to AI development, promoting innovations that are both technologically advanced and aligned with societal needs.

The potential for monitoring, evaluation and learning (MEL) to enhance innovation and impact in agricultural research and development is receiving increasing attention. New Zealand’s AgResearch Limited and Australia’s CSIRO Agriculture and Food are working with their scientists to support the organisations to achieve greater innovation and impact by embedding MEL into research programs and projects. However, both organisations have found it challenging to systematically demonstrate the value of their MEL initiatives. While there is an increasing number of case studies and anecdotes pointing towards the contribution of MEL to fostering innovation that delivers social, economic and environmental impacts, there is limited evidence, collated through systematic and rigorous methods, to substantiate this. This article presents an evaluation framework drawing on insights from complexity science (the Cynefin framework), evaluation practice and research (complexity-aware M&E and reflective practice) and innovation capacities (learning, reflection and adaptation). The framework is intended for research organisations working in agricultural innovation systems to be able to demonstrate the value of their MEL initiatives as well as carry out comparative analyses. It also supports organisational learning to better inform evaluative strategies and actions.

Stone-Jovicich, S., B. E. Goldstein, K. Brown, R. Plummer, and P. Olsson. 2018. Expanding the contribution of the social sciences to social-ecological resilience research. Ecology and Society 23(1):41. https://doi.org/10.5751/ES-10008-230141

Stone-Jovicich, S. 2015. Probing the interfaces between the social sciences and social-ecological resilience: insights from integrative and hybrid perspectives in the social sciences. Ecology and Society 20(2): 25.http://dx.doi.org/10.5751/ES-07347-200225

Moloney, G., Z. Leviston, T. Lynam, J. Price, S. Stone-Jovicich, and D. Blair. 2014. Using social representations theory to make sense of climate change: what scientists and nonscientists in Australia think. Ecology and Society 19(3): 19.http://dx.doi.org/10.5751/ES-06592-190319

Although the broad concept of mental models is gaining currency as a way to explore the link between how people think and interact with their world, this concept is limited by a theoretical and practical understanding of how it can be applied in the study of human-environment relationships. Tools and processes are needed to be able to elicit and analyze mental models. Because mental models are not directly observable, it is also important to understand how the application of any tools and processes affects what is measured. Equally important are the needs to be clear on the intent of the elicitation and to design the methods and choose the settings accordingly. Through this special edition, we explore how mental models are elicited using two approaches applied in two case-study regions. We analyze two approaches used in the Crocodile River catchment of South Africa: a graphically based approach, i.e., actors, resources, dynamics, and interactions (ARDI); and an interview- or text-based approach, i.e., consensus analysis (CA). A further experiment in the Rhone Delta (Camargue), France, enabled us to test a cross- over between these two methods using ARDI methodology to collect data and CA to analyse it. Here, we compare and explore the limitations and challenges in applying these two methods in context and conclude that they have much to offer when used singly or in combination. We first develop a conceptual framework as a synthesis of key social and cognitive psychology literature. We then use this framework to guide the enquiry into the key lessons emerging from the comparative application of these approaches to eliciting mental models in the two case regions. We identify key gaps in our knowledge and suggest important research questions that remain to be addressed