{"id":6470,"date":"2022-06-09T17:59:27","date_gmt":"2022-06-09T15:59:27","guid":{"rendered":"https:\/\/ethicstech.eu\/?p=6470"},"modified":"2022-12-08T14:26:45","modified_gmt":"2022-12-08T13:26:45","slug":"anais-resseguier-the-critical-approach-to-ai-needs-to-be-continuously-restarted","status":"publish","type":"post","link":"http:\/\/ethicstech.eu\/en\/anais-resseguier-the-critical-approach-to-ai-needs-to-be-continuously-restarted\/","title":{"rendered":"Ana\u00efs Ress\u00e9guier: The critical approach to AI needs to be continuously restarted"},"content":{"rendered":"\t\t
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Ana\u00efs Ress\u00e9guier:
The critical approach to AI needs to be continuously restarted<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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9 June, 2022
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There is a tendency to consider AI as a highly sophisticated technology that would be almost virtual, intangible. This is very problematic because it means that we cannot see all the impacts of AI. So if we want to fully assess this technology looking at the whole AI lifecycle is very much needed, says Dr. Ana\u00efs Ress\u00e9guier in conversation with Maciej Chojnowski.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Maciej Chojnowski: In an article<\/a> co-written with Rowena Rodrigues, you argue that today\u2019s AI ethics is mostly based on high-level prinicples and remains quite abstract. You also give two reasons why AI ethics as we know it is rather ineffective. Firstly, because it has limited means to ensure compliance. And secondly, because AI ethics should not be treated as a soft version of law. Quite contrary, it should be seen as attention to context: a constantly renewed ability to see the new, which is \u201ea powerful tool against cognitive and perceptive inertia\u201d. What are the most striking examples of this inertia in the AI field today?
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Ana\u00efs Ress\u00e9guier<\/strong>*: I would like to highlight two points here. The first one is inertia in acknowledging the disparate impacts of AI on different communities. We often fail to recognise that if an AI system doesn\u2019t work for a particular community it is a failure of this system. And there are many systems that work poorly for specific groups of people. This should be considered a really significant problem, not just a marginal issue, and be properly addressed. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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There is a study called \u201eGender shades\u201d<\/a> by Joy Buolamwini and Timnit Gebru about facial recognition systems which exemplifies this point very well. It shows that the standard evaluation of facial recognition systems that look at error rates over the whole population fails to account for major disparities across different communities. As this study showed, error rates for black females are much higher than those for white males.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This illustrates the fact that we need more fine-tuned, more precise lenses to assess this technology, one that look more closely at the characteristics of particular communities. I think that despite the evidence that this is highly needed there is still a lot of blindness towards it. This is the first point of this cognitive and perceptive inertia that I want to highlight.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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And the second one?<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The second one is the failure to account for the full lifecycle of AI systems, including the raw material they rely on, their environmental impact, the costs of producing and disposing of the systems components and so on.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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There is a tendency to consider AI as a highly sophisticated technology that would be almost virtual, intangible. In another article<\/a>, we call this the \u201eAI immaterial narrative\u201d. And this is very problematic because it means that we cannot see all the impacts of AI. For instance, a study on the environmental impacts of AI shows that training a single AI system produces as much carbon dioxide as five cars in their whole lifetime! So that\u2019s quite a major impact. This is why looking at the whole AI lifecycle, not only at the algorithms that are being developed in laboratories, is very much needed if we want to fully assess this technology.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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In her book \u201eAtlas of AI\u201d<\/a>, Kate Crawford does a great job in redefining what AI is in all its different dimensions: the rare earth minerals it relies on, the infrastructure, the logistics, the data collection, and the impact of this data collection. So that\u2019s a really broad system that needs to be taken into consideration when we look at AI. But I\u2019m afraid we are still a bit blind to that. And this should definitely change.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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According to Luciano Floridi, digital ethics is one of the three components of the normative system concerning technology (the other two being digital regulation and digital governance). And whereas regulation tells us what is legal and illegal, it has nothing to say about what we can do to live in a better society. This sort of moral evaluation is only possible within the ethics domain. But it seems that this is exactly the sort of capability that AI ethics is lacking today because of this widespread legalistic approach. What sort of actions and tools do we need to make AI ethics effective?<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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To answer this question I would make a distinction within the field of ethics that helps address a confusion I see quite often.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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On the one hand, we can distinguish ethics as embedded within particular institutional frameworks. For instance, with bioethics there are well-established structures and frameworks that make it work and implement it in practice for medical research. At Trilateral Research<\/a>, we work a lot with this approach to ethics, in particular with research ethics applied to new and emerging technologies. We provide guidance to research projects developing technological solutions to make sure they follow ethical obligations. The European Commission has a very specific research ethics framework that requires compliance from grantees. While working on projects funded by the EU we use our ethical expertise to implement this ethical framework. And this form of ethics is indeed a bit like a softer version of law.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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In our article we were critical of this approach to ethics as soft law when it is not embedded within institutional frameworks. This is when it becomes indeed \u201etoothless\u201d. But it can be very valuable when embedded in institutions with mechanisms to ensure compliance. For instance if an EU-funded project does not comply with research ethics obligations, funding might be withheld or the project suspended. So there are ways to ensure compliance to ethical requirements.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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In 2020-2021, the European Commission developed a research ethics framework for EU-funded projects using and\/or developing AI. The SIENNA<\/a> project had the chance to contribute to this effort and suggested ways to identify and mitigate ethics issues raised by projects using and\/or developing AI systems or techniques. This framework<\/a> is now in place and has become a requirement for all projects funded under the new Horizon Europe Framework Program<\/a> that will be in place for the next seven years. So it\u2019s going be interesting to see how it works and we already look forward to suggesting ways to further enhance it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This is the first approach to ethics, ethics as embedded in institutional frameworks and there is assuredly the need to develop further this approach for the ethics of AI.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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And this seems to be the dominant way of thinking about AI ethics today. <\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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But there is also another aspect of ethics \u2013 very important although quite neglected. And it is actually the source of the first one that I have just discussed. This is what we developed in the Ethics as Attention to Context<\/a> piece in the SIENNA project where ethics is more fundamentally seen as an open and continuously renewed attention to the world and to the new as it emerges.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This is where we come back more specifically to ethics as a way to reduce the cognitive and perceptive inertia. Here ethics is not about saying what we should or should not do as opposed to the first approach. It\u2019s rather an attitude towards the world characterized by the attention and ability to determine the norms. Because only when we see the new that is happening, we are in a position to determine how to deal with it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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