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</h1>
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<div id="introduction">
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<p>
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Technical improvements, the accumulation of large, detailed datasets and
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advancement in computer hardware have led to an Artificial Intelligence
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(AI) revolution. For example, breakthroughs in computer vision have
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enabled automated decision making based on images and videos, the building
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of large datasets and amelioration in text analysis coupled with the
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gathering of personal data have given birth to countless AI applications.
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These new AI applications have given lot of benefits to European Union
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(EU) citizens. However, because of its inherent complexity and
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requirements in technical resources and knowledge, AI may undermine our
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ability to control technology and put fundamental freedoms at risk.
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Therefore, introducing new legislation on AI is a worthwhile objective.
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Technical improvements, the accumulation of large, detailed datasets and
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advancement in computer hardware have led to an Artificial Intelligence
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(AI) revolution. For example, breakthroughs in computer vision as well
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as the building of large datasets and amelioration in text analysis
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coupled with the gathering of personal data have given birth to
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countless AI applications. These new AI applications have given many
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benefits to European Union citizens. However, because of its inherent
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complexity and requirements in technical resources and knowledge, AI may
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undermine our ability to control technology and put fundamental freedoms
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at risk. Therefore, introducing new legislation on AI is a worthwhile
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objective.
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</p>
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<p>
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In the context of a new legislation, this article explains how releasing
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AI applications under Free Software licenses pave the way for more
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accessibility, transparency and fairness.
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AI applications under Free Software licences paves the way for more
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accessibility, transparency, and fairness.
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</p>
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</div>
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@ -54,74 +54,76 @@
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<p>
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These freedoms are granted by releasing software under a Free Software
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license, whose terms are compatible with the aforementioned freedoms. There
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exists multiple Free Software licenses with different goals. A software may
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licence, whose terms are compatible with the aforementioned freedoms. There
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exist multiple Free Software licences with different goals. Software may
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be licensed under more than one license. Because in order to be freely
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modified, an AI requires its training code and the data, both needs to be
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released under a Free Software license to consider the AI as being Free.
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modified, an AI application requires its training code and training data,
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both need to be released under a Free Software license to consider the AI
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as being Free.
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</p>
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<h2 id="accessibility">Accessibility</h2>
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<p>
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Accessibility for AI means making it reusable, so that everyone may tinker
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with it, improve it and use for their own means. To make AI reusable, it can
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with it, improve it and use for their own purposes. To make AI reusable, it can
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be released under a Free Software license. The advantages of this approach
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are plenty. By having open legal grounds, a Free AI fosters innovation,
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are many. By having open legal grounds, Free AI fosters innovation,
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because one does not have to deal with artificial restrictions that prevent
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people from reusing work. Making AI Free therefore saves everyone from
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having to reinvent the wheel, making researchers and developers alike able
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to focus on creating new, better AI software instead of rebuilding blocks
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and reproducing previous work again and again. In addition to improving
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efficiency, by sharing expertise, Free AI also lowers the cost of
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efficiency, by sharing expertise, Free AI lowers the cost of
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development by saving time and removing license fees. All of this improves
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accessibility of AI, which leads to better and more democratic solutions as
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everyone can participate.
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</p>
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<p>
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Making AI reusable also makes it easier to build specialized AI model upon
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Making AI reusable also makes it easier to base specialised AI models upon
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more generic ones. If a generic AI model is released as Free Software,
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rather than training a new model from scratch, one could leverage the
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rather than training a new model from scratch, one can leverage the
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generic model as a starting point for a specific, downstream prediction
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task. For example, one could use a generic computer vision model<a
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task. For example, one can use a generic computer vision model<a
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href="#fn-1" id="ref-1" class="fn">1</a><span class="fn">,</span><a
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href="#fn-2" id="ref-2" class="fn">2</a> as a starting point for managing
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public infrastructure which requires specific image treatments. Just like
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public infrastructure which requires specific image treatments. Just as
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with accessibility in general, this approach has a key advantage: generic
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models with a lot of parameters and trained on large datasets may make the
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downstream task easier to learn. This makes AI more accessible by lowering
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the barrier to entry by making it easier to reuse works.
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</p>
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<p>
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However, making both the source code used to train the AI and the
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corresponding data Free is sometimes not enough to make it accessible. AI
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requires a huge amount of data in order to identify patterns and
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correlations which lead to correct predictions. In contrary, not having
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enough data reduces its ability to understand the world. Furthermore, big
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datasets and their inherent complexity tend to make AI models large, making
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their training time-consuming and resources intensive. The complexity in
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handling the data required to train AI models, coupled with the knowledge
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required to develop them and manage computer resources demand a lot of human
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resources. Therefore, it may be hard to exercise the freedoms offered by a
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Free AI, even though its training source code and data might be released as
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Free Software. In those cases, releasing the trained AI models as Free
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Software would greatly improve accessibility.
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</p>
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<p>
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<p>
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However, making both the source code used to train the AI application and
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the corresponding data Free is sometimes not enough to make it
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accessible. AI requires a huge amount of data in order to identify
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patterns and correlations which lead to correct predictions. On the
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contrary, not having enough data reduces its ability to understand the
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world. Furthermore, big datasets and their inherent complexity tend to
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make AI models large, making their training time-consuming and
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resource-intensive. The complexity in handling the data required to train AI
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models, coupled with the knowledge required to develop them and manage
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a huge computer capacity, demands a lot of human resources. Therefore, it may be
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hard to exercise the freedoms offered by Free AI, even though its
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training source code and data might be released as Free Software. In
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those cases, releasing the trained AI models as Free Software would
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greatly improve accessibility.
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</p>
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<p>
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Finally, it should be noted that, just like any other technology, making AI
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reusable by everyone can potentially be harmful. For example, reusing a face
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detector released as Free Software as part of a facial recognition software
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can cause human right issues. However, this holds true regardless of the
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detector released as Free Software as part of facial recognition software
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can cause human rights issues. However, this holds true regardless of the
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technology involved. If a software use case is deemed harmful, it should
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therefore be prohibited without an explicit ban on AI technology.
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</p>
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</p>
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<h2 id="transparency">Transparency</h2>
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<p>
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AI transparency can be subdivided in openness and interpretability. In this
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AI transparency can be subdivided into openness and interpretability. In this
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context, openness is defined as the right to be informed about the AI
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software, and interpretability is defined as being able to understand how
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the input is processed so that one can identify the factors taken into
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@ -147,20 +149,20 @@
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used and how it was processed by the AI should be made available. Moreover,
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trust and adoption of AI would consequently be higher. Furthermore, modern
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AI technologies such as deep learning are not meant to be transparent,
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because are composed of millions or billions of individual parameters<a
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because they are composed of millions or billions of individual parameters<a
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href="#fn-7" id="ref-7" class="fn">7</a>, making them very complex and hard
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to understand. This calls for Free Software which seeks to analyze this
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to understand. This calls for Free Software which can assist in analysing this
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complexity.
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</p>
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<p>
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Technologies released as Free Software to make AI more transparent already
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exists. For example, Local Interpretable Model-Agnostic Explanations
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exist. For example, Local Interpretable Model-Agnostic Explanations
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(LIME)<a href="#fn-8" id="ref-8" class="fn">8</a> is a software package
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which simplifies a complex prediction model by simulating it with a simpler,
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more interpretable version, thus enabling users of the AI to understand the
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parameters that played a role in the prediction. Figure 1 illustrates this
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process by comparing predictions made by two different models. Captum<a
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href="#fn-9" id="ref-9" class="fn">9</a> is library released as Free
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href="#fn-9" id="ref-9" class="fn">9</a> is a library released as Free
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Software providing an attribution mechanism allowing one to understand the
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relative importance of each input variable and each parameter of a deep
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learning model. Making AI more transparent is therefore possible.
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@ -170,8 +172,8 @@
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<figcaption>Figure 1: example of prediction explanations by LIME<a href="#fn-8" id="ref-8" class="fn">8</a></figcaption>
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</figure>
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<p>
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Although a proprietary AI can be transparent, Free Software facilitates this
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process by making auditing and inspection easier. While some data might be
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Although a proprietary AI model can be transparent, Free Software facilitates
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transparency by making auditing and inspection easier. While some data might be
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too sensitive to be released under a Free Software license, statistical
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properties of the data can still be published. With Free Software, everyone
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is able to run the AI to understand how it is made, and look up the data
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@ -184,40 +186,41 @@
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Another benefit of Free Software in this context is that by granting the
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right to improve the AI software and share improvements with others, it
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allows everybody to improve transparency, thereby preventing vendor lock-in
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where one has to wait until the software provider makes AI more transparent.
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where one has to wait until the software provider makes the AI software more
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transparent.
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</p>
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<h2 id="fairness">Fairness</h2>
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<p>
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In artificial intelligence (AI), fairness is defined as making it free of
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In Artificial Intelligence (AI), fairness is defined as making it free of
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harmful discrimination based on one’s sensitive characteristics such as
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gender, ethnicity, religion, disabilities or sexual orientation. Because AI
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gender, ethnicity, religion, disabilities, or sexual orientation. Because AI
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models are trained on datasets containing human behaviors and activities
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that can be unfair, and AI models are designed to recognize and reproduce
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existing patterns, they can create harmful discrimination and human right
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that can be unfair, and AI models are designed to recognise and reproduce
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existing patterns, they can create harmful discrimination and human rights
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violations. For example, (COMPAS)<a href="#fn-10" id="ref-10"
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class="fn">10</a>, an algorithm attributing scores which indicates how
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likely one is going to recidivate their crime, was found to be unfair
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towards African American<a href="#fn-11" id="ref-11" class="fn">11</a>,
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because for them, 44.9% of cases were false positives. The algorithm
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attributed a high change of recidivism despite the defendants not
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class="fn">10</a>, an algorithm attributing scores which indicate how
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likely one would recidivate, was found to be unfair
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towards African Americans<a href="#fn-11" id="ref-11" class="fn">11</a>
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because for them 44.9% of cases were false positives. The algorithm
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attributed a high chance of recidivism despite the defendants not
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re-offending. Conversely, 47.7% of the cases for white people were labeled
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as low risk of recidivism despite them re-offending. Suspected unfairness
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has also been found in healthcare<a href="#fn-12" id="ref-12"
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class="fn">12</a>, where an algorithm was used to attribute risks scores to
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patients, thereby identifying those needing additional care resources. To
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have the same risks scores as white people, black people needed to be in an
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worst health situation, in term of severity in hypertension, diabetes,
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have the same risks scores as white people, black people needed to be in a
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worse health situation, in term of severity in hypertension, diabetes,
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anemia, bad cholesterol, or renal failure. Therefore, real fairness issues
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exist in AI algorithm. Moreover, from a legal perspective, checking for
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fairness issues is required by the Recital 71 of the GDPR, which requires to
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“<em> prevent, inter alia, discriminatory effects on natural persons on the
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may exist in AI algorithms. Moreover, from a legal perspective, checking for
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fairness issues is required by Recital 71 of the GDPR, which requires to
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“<em>prevent, inter alia, discriminatory effects on natural persons on the
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basis of racial or ethnic origin, political opinion, religion or beliefs,
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trade union membership, genetic or health status or sexual orientation, or
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processing that results in measures having such an effect.</em>” We thus
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processing that results in measures having such an effect.</em>”. We thus
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need solutions to detect potential fairness issues in datasets on which AI
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is trained and correct it when it occurs.
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is trained and correct them when they occur.
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</p>
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<p>
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To detect fairness, one needs to quantify it. There are lots of ways to
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<ol>
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<li>
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Remove the sensitive attribute (e.g. gender, ethnicity, religion, etc.)
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from the dataset. This approach does not work in real-world scenario
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because removing the sensitive attribute is not enough to completely
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from the dataset. This approach may not work in real-world scenarios
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because removing the sensitive attribute might not be enough to completely
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mask it, as the sensitive attribute is often correlated with other
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attributes of the dataset. Removing it is therefore not sufficient, and
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removing all attributes correlated with it leads to a lot of information
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attributes of the dataset. Removing may therefore not be sufficient, and
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removing all attributes correlated with it may lead to a lot of information
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loss;
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</li>
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<li>
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by a sensitive characteristic;
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</li>
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<li>
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Optimize the AI model for accuracy and fairness at the same time. While
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Optimise the AI model for accuracy and fairness at the same time. While
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the algorithm is trained on an existing dataset that contains unfair
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discrimination, both consider its accuracy and its fairness<a
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href="#fn-15" id="ref-15" class="fn">15</a>. In other words, add fairness
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</ol>
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<p>
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If those methods are used, having a perfectly accurate and fair algorithm is
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impossible, but if the accuracy is defined on a dataset that is known to
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contain unfair treatment of a particular group, having a less than perfect
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accuracy may be deemed acceptable.
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impossible<a href="#fn-14" id="ref-14" class="fn">14</a>, but if the accuracy
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is defined on a dataset known to contain unfair treatment of a particular
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group, having a less than perfect accuracy may be deemed acceptable.
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</p>
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<p>
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Because a AI released as Free Software may be used and inspected by
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everyone, verify if it is free of potentially harmful discrimination is
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easier than if it were proprietary. Moreover, this synergizes with AI
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Because as AI application released as Free Software may be used and inspected
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by everyone, verification of whether it is free of potentially harmful discrimination
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is easier than if it were proprietary. Moreover, this synergises with AI
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transparency (see Section <a href="#transparency">Transparency</a>), as a
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transparent AI facilitates the understanding of the factors considered for
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making predictions. While necessary, releasing AI as Free Software does not
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make fair, but make fairness easier to evaluate and enforce.
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transparent AI applicationfacilitates the understanding of the factors considered for
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making predictions. While necessary, releasing an AI application as Free Software
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does not make it fair. However, it makes fairness easier to evaluate and enforce.
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</p>
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<h2 id="conclusions">Conclusions</h2>
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<p>
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In this article, we highlighted potential issues around the democratization
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of artificial intelligence (AI) and implications for human rights. Possible
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Free Software solutions are presented to tackle these issues. In particular,
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we showed that AI needs to be accessible, transparent and fair in order to
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be usable. While not a sufficient solution, releasing AI under Free Software
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licenses is necessary for its widespread use throughout our information
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systems by making it more scrutable, trustworthy and safe for everyone.
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In this article, potential issues around the democratisation
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of artificial intelligence (AI) and implications for human rights are
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highlighted, and potential Free Software solutions are presented to tackle
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them. In particular, it is shown that AI needs to be accessible, transparent
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and fair in order to be usable. While not a sufficient solution, releasing
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AI under Free Software licences is necessary for its widespread use
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throughout our information systems by making it more scrutable, trustworthy
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and safe for everyone.
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</p>
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<h2 id="fn">References</h2>
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Loading…
Reference in New Issue
Block a user