Redirecting the cultural tradition of digital innovation in agriculture
Maaz Gardezi (Virginia Tech)
The ‘problem of many hands’ is often cited as the issue with new and emerging technologies such as artificial intelligence (AI), big data, and machine learning algorithms. This is the problem attributed to the complex and distributed nature of innovation; possessing interests and priorities of a variety of actors and organizations across the innovation chain, from development, governance, to its use and misuse. A ‘networked world’ may exaggerate the propensity of technology to disrupt existing social and political systems, not least because we are finding it difficult to answer questions such as ‘who is responsible?’ for potential social and ethical ramifications of new technology. Against this background, I would like to use this workshop as an opportunity to deliberate on this question: How is this problem of ‘many hands’ in AI-based systems different from previous instance of technological transformations, such as those related to mechanization in agriculture? To answer this question, I will focus on algorithmic architectures that are unique to AI, machine learning, big data, and natural language processing—domains that are central to foundational AI research. I will interrogate whether more-than-human ontologies of algorithmic architectures allow data and code to inherit sociopolitical forces of existing social differences, e.g. based on race, class, and gender differences. The appearance of objectivity in these algorithms may in fact make it more difficult to answer the question ‘who is responsible?’. Thus, this opens further questions about how to democratize science and technology? how to make innovation more responsible? and what role does anticipatory approaches have for stakeholders to reflect about societal disruptions of their innovations in the early stages of innovations?