Tuesday, June 25th, 2024 | 18:15 - 19:45 p.m.
Public Lecture Series: Taming the Machines. Repairing AI for Environmental Justice
UHH, Main Building, West Wing, Edmund-Siemers-Allee 1, Room 221
Taming the Machines — Horizons of Artificial Intelligence. The Ethics in Information Technology Public Lecture Series
This summer‘s „Taming the Machine“ lecture series sheds light on the ethical, political, legal, and societal dimensions of Artificial Intelligence (AI).This lecture series brings together perspectives from ethics, politics, law, geography, and media studies to assess the potential for preserving and developing human values in the design, dissemination, and application of AI technologies. How does AI challenge our most fundamental social, political, and economic institutions? How can we bolster (or even improve) them in times of technological disruption? What regulations are needed to render AI environments fairer and more transparent? What needs to be done to make them more sustainable? In what sense could (and even should) we hold AI accountable?
To explore these and other related questions, this public lecture series invites distinguished international researchers to present and discuss their work. To get the latest updates and details how to attend the lectures, please visit http://uhh.de/inf-eit.
Prof. Dr. Aimee van Wynsberghe, Rheinische Friedrich-Wilhelms-Universität Bonn, D
Let us imagine that Artificial Intelligence (AI) is broken. Not in the physical sense in which pieces are falling apart and need to be put together; rather, in the metaphorical sense in which there are serious ethical concerns related to the design and development of AI that demand repair. In this talk I will outline a definition of Sustainable AI as an umbrella term to cover two branches with different aims and methods: AI for sustainability vs the sustainability of AI. I will show that AI for sustainability holds great promise but is lacking in one crucial aspect; it fails to account for the environmental impact from the development of AI.
Alternatively, the environmental impact of AI training (and tuning) sits at the core of the sustainability of AI, for example measuring carbon emissions and electricity consumption, water and land usage, and regulating the mining of precious minerals. All of these environmental consequences fall on the shoulders of the most marginalized and vulnerable demographics across the globe (e.g. the slave like working conditions in the mining of minerals, the coastal communities susceptible to unpredictable weather conditions). By placing environmental consequences in the centre one is forced to recognize the environmental justice concerns underpinning all AI models. The question then becomes, how can the AI space be repaired to transform current structures and practices that systemically exacerbate environmental justice issues with the consequence of further marginalizing vulnerable groups.
Institutions
- UHH
Universität Hamburg
Adeline Scharfenberg