Paper: Are machines automating morality? (ICLR-2024)

Reviewer comment (Writing):
The biggest flaw, in my opinion, is how the article is written on a fine-grained level.

Author response:
You are right, we have tried to make the writing more coherent and to improve the accuracy of grammar and expression.

Reviewer comment (Writing):
The quality of the writing in general needs to be upgraded. For example, some sentences aren’t grammatically valid: « The problem that stems today from machine learning [...] », or « Such was even named the study conducted by researchers […] ».

Author response:
You are right, we have tried to make the writing more coherent and to improve the accuracy of grammar and expression.

Reviewer comment (Writing):
No rigorous definition of the terms at hand is given. For example, a definition of morality VS ethics was necessary. Or machine learning vs AI vs algorithm, which was only briefly discussed on page 4, after many references to these concepts. Also, there seems to be a lack of understanding of the concepts at hand: « In machine learning, artificial intelligence learns [...] » or «In the banking sector, algorithms have largely replaced traditional methods of determining loan eligibility ».

Author response:
You are right, there was a lack of definitions, which made the argumentation unclear. We briefly introduced some distinctions in the introduction. However, while these three terms cover different realities, they share the objective of automation (to a greater or lesser extent). It is this automation in general that we are dealing with here, even if we realize that there are differences at various points. We prefer to speak of "automated systems" or "models".

Reviewer comment (Writing):
Some sentences lack clarity. For example: « in the banking sector, algorithms have largely replaced traditional methods of determining loan eligibility, leading to moral dilemmas akin to the ”trolley problem” »; what is the link between those two situations? Another example would be « Silicon Valley’s new mantra is WYSIWYG: ”what you see is what you get”. And yet, by brandishing WYSIWYG as its sole political horizon, Silicon Valley delegitimizes in advance any critical reflection on technological innovations and the meaning attached to them (Alloa & Soufron, 2019). » There is a lack of explanation and discussion.

Author response:
As mentioned above, we have decided to remove the sentence on the banking sector.
We have extended and integrated into a "Discussion" section the questions surrounding the ideology of Silicon Valley.

Reviewer comment (Theory):
The core of the article is centered around AI being utilitarian. A single citation is used to assess this point, along with a single argument, which is far from being convincing. Plus, the reasoning is doubtful: « consequentialist morality […] has dominated debates and infused current technological developments (Salvat, 2020). As a result, utilitarianism has become the dominant morality in Silicon Valley »: it might have infused technologies, but why assume that because of that it is **dominant** in Silicon Valley? Plus, Silicon Valley is responsible for many technologies that we manipulate in our daily lives, but the example of the loan approval is a good example of something that has huge consequences on the lives of many people, yet does not stem from Silicon Valley. Finally, this last example illustrates how consequentialist vision is not that dominant in AI: we want the models in insurance to be fair and not racist, this criterion being $\textit{a priori}$.

Author response:
Thank you for your detailed and constructive comment. With regard to the utilitarian aspect of Silicon Valley, we are of course aware that many innovations do not originate in Silicon Valley. Our purpose here is to focus on this particular and crucial place when talking about technological innovations.
We have also briefly compared the Chinese technological ecosystem, in which the ideological aspect plays an important role.
Finally, from our point of view, insurance models are not designed not to be racist, but to provide the best outcome. They are utilitarian in their functioning. We try to discuss this view in our paperr

Reviewer comment (Theory):
It is unacceptable, in such work, not to discuss « interpretability » or « explainability » in AI, for such topics exactly aim at understanding the decision process of the predictor and currently generate tons and tons of research. Statements such as « [w]ith each moral question addressed by machine learning, we delegate our moral authority. » are not true, for it only (and questionably) holds when it comes to black boxes (some other term that would definitely need to be discussed in such work) in machine learning. Machine learning yielding simple predictors, understandable by human beings could potentially be approved by them.

Author response:
This discussion was indeed missing from our paper. We have therefore introduced this issue first in the introduction and then at various points. We are not experts in the various types of models, and in this paper we are mainly concerned with models that cannot be interpreted or explained. We are aware, though, that the question of interpretability does not apply to simpler models.

Reviewer comment (Novelty):
It is hard to understand the original contribution of the work with regards to the current literature, for most of the ideas in this work are supported by works from the literature; I don’t feel like new or novel thoughts have been brought, or thoroughly defended and argued, therefore questioning the overall contribution of the work.

Author response:
We have reorganized our paper to dedicate part of our work to a discussion in which we seek to further defend a point of view.