Venue: B1.470 and MS Teams

Venue: TU Delft, Faculteit Technology, Policy and Management (TPM), Hall A – Henk Sol, 31.A0.800

Ben je bezig met energie, en wil je je verdiepen in de ethiek ervan? Of ben je een filosoof en wil je meer weten over energie-ethiek?
TPM en de Vereniging voor Ethici Nederland organiseren een evenement met boeiende sprekers en een kunstworkshop waarin we samen visueel energietoekomsten bouwen!

Hoe energie wordt geproduceerd, en hoeveel, is verwikkeld met hoe onze samenleving in elkaar zit. Maar hoe willen we dat onze maatschappij eruitziet? Energietechnologie en infrastructuur zijn niet neutraal; zij worden gemaakt door mensen. Daarnaast kunnen energiesystemen ongewenste en onrechtvaardige gevolgen hebben. Daarom is het essentieel om de energietransitie niet alleen te bekijken vanuit economisch, politiek en technisch perspectief, maar ook vanuit ethisch oogpunt.

Met deze geweldige sprekers:
Sophie Kwizera komt spreken over waterstof en Zuid-Afrika;
Arianne J. van der Wal weet alles over energiearmoede;
Behnam Taebi is expert over nucleaire energie en ethiek;
Aafke Fraaije zorgt voor een inspirerende collaboratieve kunstworkshop;
Maria Kaufmann reflecteert op onze ideeën over de toekomst.

Venue: TPM A1.370/Teams

Can we praise AI for novel outputs?

There is a growing number of examples of artists training neural networks and integrating artificial intelligence into their creative processes. While the artist's role generally remains central, the rapid development of AI raises compelling questions: Could a neural network ever be recognized as the author of an artwork? Can AI be attributed intellectual merit for novel outcomes? These questions point to a deeper philosophical issue: under what conditions, if any, can AI be held intellectually responsible for novel outcomes? To address this, I propose to draw a parallel with spontaneous creative thinking, a mental process which typically leads to the appearance of unexpected insights. Both spontaneous creative thinking and AI-driven processes share key similarities—specifically, their unpredictability and the lack of direct control during their unfolding. In my previous work, I argued that what motivates the correct attribution of intellectual responsibility to an agent for the outcome of creative thinking is that the production of these new ideas is biased by the agent’s knowledge and experiences. I suggest a similar approach for AI, particularly for systems that involve trained neural networks. By examining how biases in a neural network are established, I argue that we can better understand the conditions under which, if ever, AI might be held intellectually responsible for its outputs

Abstract
This paper investigates the gender-based employment implications of structural transformation in Africa, exploring how policies tailored to gender considerations can foster gender employment balance on the continent. We consider two distinct patterns of structural transformation. First, we consider structural transformation through the lens of economy-wide shifts in sectoral structures. Employing a panel econometrics method, we examine how gender-based employment gaps are affected by these broad shifts in sectoral structures and the role of policy options that could attenuate the observed effect. We found that these shifts have generally worsened gender-based employment gaps across African countries. However, countries implementing policies to enhance women's economic opportunities and social status have successfully mitigated this trend. Second, we explore structural transformation through the lens of green energy systems and transition technologies (GETT). Through a comprehensive literature review, we found that while GETT presents numerous opportunities for achieving gender-balanced employment, entrenched gendered social inequalities pose a significant challenge. As this calls for policies that subvert these social inequalities, we further examine the alignment of existing GETT-related policies in tackling this challenge, with Ghana and South Africa serving as case studies. We found that GETT-related policies in these countries simply follow the status quo, lacking the ability to generate the much-desired outcome. Accordingly, we propose a Gendered-Transformative (G-TRIP) framework to guide policy shifts necessary for achieving gender-inclusive structural transformation amid the evolving industrial landscape.

Venue: C1.060/hybrid

TBA

Venue: TPM A1.370/Teams