# Widen the scope!

Laure Vieu

## ► To cite this version:

Laure Vieu. Widen the scope!. International Workshop on Integrating the Semantics of Data, On-tologies, Moral and cultural values and their Societal impact (WISDOMS), May 2024, Hersonissos (Crète), Greece. hal-04854242

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Submitted on 23 Dec 2024

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# Widen the scope! *

Laure Vieu

IRIT, CNRS, Université de Toulouse, Toulouse, France

**Abstract**

This is a position paper with no abstract.

**Keywords**

AI impacts, AI ethics

## 1. Introduction

Seeing the community of applied ontology and NL processing, to which I still believe to belong, address the topic of AI ethics as in the present workshop is encouraging. But which ethical issues are we talking about?

I am no specialist of ethics in general nor of AI ethics, so I will not discuss the human, moral and cultural values to be considered. Further, I will not address how these values can be represented in ontologies and other knowledge representation systems, although this constitutes the core of this workshop. I will argue here that, when taking into account AI ethics issues underlying this workshop, whose focus is on “ensuring that AI systems not only meet human needs but also embody and respect human values”, the scope of AI impacts considered should be as broad as possible to include AI’s impacts in terms of all sorts of society transformations and in terms of environmental damages.

## 2. Individual-level impacts

What is often focused on when discussing ethics of what is nowadays called AI is how to keep deep learning algorithms from inheriting discriminating bias from the data. For instance, how to avoid developing racist applications just because there is a lot of racist speech on internet. In this sense, “respecting human values” means avoiding impacts that are harmful at the individual level. It is obviously a significant task and, given the black box nature of Large Language Models, not an easy one. Yet, AI ethics cover much more (see, e.g., the Montréal Declaration¹ and Unesco’s recommendations²).

## 3. Social-level impacts

This workshop rightly widens the scope to AI’s “societal impact”, that is, ethical issues regarding AI’s impact on society structures, for instance, on democratic institutions and on employment. How to guarantee that our scientific results feed technologies that induce society

WISDOMS: International Workshop on Integrating the Semantics of Data, Ontologies, Moral and cultural values and their Societal impact

¹ https://montrealdeclaration-responsibleai.com/the-declaration/

² https://www.unesco.org/en/artificial-intelligence/recommendation-ethics
evolution for the better? More importantly, how to guarantee that they do not pave the way to
worse societies? Even letting aside the infamous “existential risk” (emergence of rogue AI
systems wanting to get rid of humans), this question is crucial. Interdisciplinary efforts certainly
are the best hope to tackle it, as advocated in this workshop.

Still, I cannot help doubting that our tools, ontologies, knowledge graphs and knowledge representation as a whole, can live up to this challenge — I sincerely wish to be wrong.

In just a decade, the deployment of the smartphone technology has built a world in which
smartphones have become compulsory for basic administrative needs and have multiplied
“bullshit jobs”. Such technology-induced social transformations are questionable. They have
been planned by a few, if planned at all. But we are now locked-in into this new world, and
stepping back has been made structurally unthinkable. Indeed, there is no scientific nor
democratic arena where a decision to quit the smartphone world could be discussed, adopted
and implemented.

So, how are we to control which society is being shaped by AI? Generative AI is still young
and we are already struggling to prevent deepfakes impacting democracy. There is no doubt that
most of academia wants to develop the common good, to meet human needs. But can we
collectively assess which AI developments are really addressing human needs? Then, given the
capitalistic structure of the world's digital industry, can we restrict AI use to applications we
would deem to meet human needs?

Can we even be sure there are any human needs requiring AI? Perhaps one could think of
medical applications such as improved cancer detection. But is it not doubtful that they can
apply to humanity as a whole, beyond the rich world, and contribute to reduce inequalities? Is it
not doubtful that they can be developed alone, without being sustained by large developments of
applications with less humanistic purposes and with higher profits?

Already at this point, one can doubt that academia as a whole is currently poised to properly
address such ethical questions with the hope that conclusions be effective. The difficult question
of how the AI that we are helping develop shapes our societies is hardly being addressed by our
community, even though similar questions were already being asked in the 70s for other fields
of research [1].

4. Biosphere-level impacts

There is an even bigger elephant in the room, ignored altogether by the State of AI Report<fnref n="3" /> cited
in this workshop's CFP: AI's impacts at the biosphere level. To have a chance to keep a livable
earth for our species, and all the others, in the near future, AI development has to be compatible
with planet boundaries and in particular with IPCC's 1.5 scenario. Unesco indeed recommends:
"Member States and business enterprises should assess the direct and indirect environmental
impact throughout the AI system life cycle, including, but not limited to, its carbon footprint,
energy consumption and the environmental impact of raw material extraction for supporting the
manufacturing of AI technologies, and reduce the environmental impact of AI systems and data
infrastructures. Member States should ensure compliance of all AI actors with environmental
law, policies and practices".

We are not on the right track at all to keep down global warming. Despite decades of
scientific evidence, despite tangible climate disruption, global greenhouse gas emissions keep

<footnote n="3">³ https://www.stateof.ai/</footnote>
growing. We are rushing into the unknown, and to the best of our knowledge, this unknown looks like hell. As evidenced in the 6th IPCC report, we need to drastically reduce our impact in all areas; we will not achieve this drastic reduction with technological improvements only, we must commit to sufficiency. Some colleagues in Green IT and Sustainable AI work hard on algorithm design, hardware design, data center design, or network design to make the whole system lighter and improve energy consumption, water consumption and materials use. Yet, the Jevons paradox applies in digital technologies as elsewhere: make X lighter, cheaper, faster, and you just end up with much more of X so that its overall weight grows. The digital footprint keeps growing exponentially [2,3].

Digital technologies and AI development is pervaded with claims of being part of the solution, and this percolates even into IPCC reports. Unfortunately, most such claims are not substantiated enough: more often than not, no environmental assessment is made, thus preventing a careful and honest assessment of the benefit-risk balance. More often than not, such claims amount to wishful thinking if not downright greenwashing. For instance, even the expected benefits of carpooling apps on which the French national plan (Stratégie Nationale Bas Carbone) counts come out as unrealistic when submitted to a careful assessment [4]. Precision agriculture to reduce chemical inputs? This fits with a scenario of an increasingly industrial agriculture, while IPCC reports show that only scenarios based on small-scale agro-ecology are able to meet the Paris Accord's climate objectives. Beyond their direct impact and their indirect impacts in many application domains, it is hard to make sure AI technologies will not lock us into social transformations that imply exponential growth of energy consumption and greenhouse gases emissions, water consumption, material extraction, and waste.

So I believe the most pressing ethical question we should ask is: can AI development be made compatible with a desirable future on a livable planet?

This is an extremely difficult question that only few of us are trying to address, and I personally have only doubts to offer. Still, I strongly believe we cannot ignore it.

As all scholars, or perhaps even more than others, computer scientists have a responsibility in shaping our collective future. Taking into account our research's direct and all of its indirect impacts on climate and the environment is our ethical responsibility [5].

# 5. Conclusion

Digital technologies change the world at a very quick pace. Even keeping distant from fossil fuel or weapon industry applications, we will be hard pressed to show we're not contributing to accelerating our current race to hell. Some of us are quitting as a result.

I wish that those who stay be able to empower themselves enough to steer this enormous ship in the right direction, if they can identify a right direction, or else just stop it.

I wish that the scope of our collective ethical thinking regarding AI be not restricted to issues similar to “what of lethal injection or electrocution is the most ethical way to apply death penalty?”

I wish that we can collectively be bold, think ahead and think bigger.

So, how about including a session in (this or a future edition of) this workshop to discuss how ontologies and other knowledge representation tools could help ensuring the compatibility of AI with desirable and sustainable futures?
## Acknowledgements

Thanks to Adrien Barton and Anne-Laure Ligozat for comments on a previous version, although this position paper does not necessarily reflect their views. Thanks to the French collectives Atecopol, EcoInfo and Labos1point5 for discussions and texts that inspired much of the contents.

## References

[1] A. Grothendieck, "Allons-nous continuer la recherche scientifique ?" (1972) Écologie & politique 52 (2016): 159-169. doi:10.3917/ecopo1.052.0159

[2] C. Freitag, M. Berners-Lee, K. Widdicks, B. Knowles, G. S. Blair, A. Friday, "The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations" Patterns 2.9 (2021). doi:10.1016/j.patter.2021.100340

[3] K. Crawford. "Generative AI's environmental costs are soaring — and mostly secret" Nature 626.693 (2024). doi:10.1038/d41586-024-00478-x

[4] A. Rasoldier, A. Girault, S. Quinton, J. Combaz, K. Marquet, Assessing the Potential of Carpooling for Reducing Vehicle Kilometers Traveled, in: Proceedings of the 9th International Conference on Information and Communications Technology for Sustainability (ICT4S 2023), Rennes, France, Jun 2023, pp.120-131, doi:10.1109/ICT4S58814.2023.00021.

[5] Comité d'éthique du CNRS (COMETS). Intégrer les enjeux environnementaux à la conduite de la recherche – Une responsabilité éthique, Avis n°2022-43 (2022). URL: https://comite-ethique.cnrs.fr/avis-du-comets-integrer-les-enjeux-environnementaux-a-la-conduite-de-la-recherche-une-responsabilite-ethique/