Controlling technology at the age of Artificial Intelligence: a Free Software perspective

What is Free Software?

Free Software (also known as Open Source) empowers people to control technology by granting four freedoms to each user:

1. The freedom to use software for any purpose, without geographical limitations;
2. The freedom to study software, without any non-disclosure agreement;
3. The freedom to share software and copy it at no cost;
4. The freedom to improve software and share the improvements.

These freedoms are granted by releasing software under a Free Software licence, whose terms are compatible with the aforementioned freedoms. There exist multiple Free Software licences with different goals. Software may be licensed under more than one license. Because in order to be freely modified, an AI application requires its training code and training data, both need to be released under a Free Software license to consider the AI as being Free.

Accessibility

Accessibility for AI means making it reusable, so that everyone may tinker with it, improve it and use for their own purposes. To make AI reusable, it can be released under a Free Software license. The advantages of this approach are many. By having open legal grounds, Free AI fosters innovation, because one does not have to deal with artificial restrictions that prevent people from reusing work. Making AI Free therefore saves everyone from having to reinvent the wheel, making researchers and developers alike able to focus on creating new, better AI software instead of rebuilding blocks and reproducing previous work again and again. In addition to improving efficiency, by sharing expertise, Free AI lowers the cost of development by saving time and removing license fees. All of this improves accessibility of AI, which leads to better and more democratic solutions as everyone can participate.

Making AI reusable also makes it easier to base specialised AI models upon more generic ones. If a generic AI model is released as Free Software, rather than training a new model from scratch, one can leverage the generic model as a starting point for a specific, downstream prediction task. For example, one can use a generic computer vision model1,2 as a starting point for managing public infrastructure which requires specific image treatments. Just as with accessibility in general, this approach has a key advantage: generic models with a lot of parameters and trained on large datasets may make the downstream task easier to learn. This makes AI more accessible by lowering the barrier to entry by making it easier to reuse works.

However, making both the source code used to train the AI application and the corresponding data Free is sometimes not enough to make it accessible. AI requires a huge amount of data in order to identify patterns and correlations which lead to correct predictions. On the contrary, not having enough data reduces its ability to understand the world. Furthermore, big datasets and their inherent complexity tend to make AI models large, making their training time-consuming and resource-intensive. The complexity in handling the data required to train AI models, coupled with the knowledge required to develop them and manage a huge computer capacity, demands a lot of human resources. Therefore, it may be hard to exercise the freedoms offered by Free AI, even though its training source code and data might be released as Free Software. In those cases, releasing the trained AI models as Free Software would greatly improve accessibility.

Finally, it should be noted that, just like any other technology, making AI reusable by everyone can potentially be harmful. For example, reusing a face detector released as Free Software as part of facial recognition software can cause human rights issues. However, this holds true regardless of the technology involved. If a software use case is deemed harmful, it should therefore be prohibited without an explicit ban on AI technology.

Transparency

AI transparency can be subdivided into openness and interpretability. In this context, openness is defined as the right to be informed about the AI software, and interpretability is defined as being able to understand how the input is processed so that one can identify the factors taken into account to make predictions, and their relative importance. In Europe, the right to be informed about the decision of an algorithm is granted by the Recital 71 of the General Data Protection Regulation (GDPR) 2016/679 “ In any case, such processing should be subject to suitable safeguards, which should include specific information to the data subject and the right to obtain human intervention, to express his or her point of view, to obtain an explanation of the decision reached after such assessment and to challenge the decision.”. Transparency can thus be defined as the ability to understand what led to the predictions.

AI needs to be transparent because it is used for critical matters. For example, it is used to determine credit worthiness3, in self-driving cars4, in predictive policing5 or in healthcare6. In these contexts, getting information about how the predictions are done is therefore critical and information about the data used and how it was processed by the AI should be made available. Moreover, trust and adoption of AI would consequently be higher. Furthermore, modern AI technologies such as deep learning are not meant to be transparent, because they are composed of millions or billions of individual parameters7, making them very complex and hard to understand. This calls for Free Software which can assist in analysing this complexity.

Technologies released as Free Software to make AI more transparent already exist. For example, Local Interpretable Model-Agnostic Explanations (LIME)8 is a software package which simplifies a complex prediction model by simulating it with a simpler, more interpretable version, thus enabling users of the AI to understand the parameters that played a role in the prediction. Figure 1 illustrates this process by comparing predictions made by two different models. Captum9 is a library released as Free Software providing an attribution mechanism allowing one to understand the relative importance of each input variable and each parameter of a deep learning model. Making AI more transparent is therefore possible.

Although a proprietary AI model can be transparent, Free Software facilitates transparency by making auditing and inspection easier. While some data might be too sensitive to be released under a Free Software license, statistical properties of the data can still be published. With Free Software, everyone is able to run the AI to understand how it is made, and look up the data that went through it. However, it should be noted that the AI model itself, being composed of millions or billions of parameters, is not meant to be transparent. But simulating the AI model with a much simpler one would make it easy to inspect it.

Another benefit of Free Software in this context is that by granting the right to improve the AI software and share improvements with others, it allows everybody to improve transparency, thereby preventing vendor lock-in where one has to wait until the software provider makes the AI software more transparent.

Fairness

In Artificial Intelligence (AI), fairness is defined as making it free of harmful discrimination based on one’s sensitive characteristics such as gender, ethnicity, religion, disabilities, or *****ual orientation. Because AI models are trained on datasets containing human behaviors and activities that can be unfair, and AI models are designed to recognise and reproduce existing patterns, they can create harmful discrimination and human rights violations. For example, (COMPAS)10, an algorithm attributing scores which indicate how likely one would recidivate, was found to be unfair towards African Americans11 because for them 44.9% of cases were false positives. The algorithm attributed a high chance of recidivism despite the defendants not re-offending. Conversely, 47.7% of the cases for white people were labeled as low risk of recidivism despite them re-offending. Suspected unfairness has also been found in healthcare12, where an algorithm was used to attribute risks scores to patients, thereby identifying those needing additional care resources. To have the same risks scores as white people, black people needed to be in a worse health situation, in term of severity in hypertension, diabetes, anemia, bad cholesterol, or renal failure. Therefore, real fairness issues may exist in AI algorithms. Moreover, from a legal perspective, checking for fairness issues is required by Recital 71 of the GDPR, which requires to “prevent, inter alia, discriminatory effects on natural persons on the basis of racial or ethnic origin, political opinion, religion or beliefs, trade union membership, genetic or health status or *****ual orientation, or processing that results in measures having such an effect.”. We thus need solutions to detect potential fairness issues in datasets on which AI is trained and correct them when they occur.

To detect fairness, one needs to quantify it. There are lots of ways to define fairness for AI, based on two categories of approaches. The first one verifies that people grouped according to some sensitive characteristic are treated similarly by the algorithm, e.g. in term of accuracy, true positive rate and false positive rate. The second approach measures fairness at the individual level by ensuring that similar individuals are treated similarly by the algorithm13. More formally, a distance measure between samples of the dataset and a distance measure between the predictions of the algorithm are compared to ensure their ratio is consistent. However, satisfying group fairness and individual fairness at the same time might be impossible14. There are three commonly used methods to mitigate unfairness, if detected:

1. Remove the sensitive attribute (e.g. gender, ethnicity, religion, etc.) from the dataset. This approach may not work in real-world scenarios. When the sensitive attribute is correlated with other attributes of the dataset, removing the sensitive attribute is not be enough to completely mask it. Removing it may therefore not be sufficient, and removing all attributes correlated with it may lead to a lot of information loss;
2. Ensure that the dataset has an equal representation of people if grouped by a sensitive characteristic;
3. Optimise the AI model for accuracy and fairness at the same time. While the algorithm is trained on an existing dataset that contains unfair discrimination, both consider its accuracy and its fairness15. In other words, add fairness to the goal of the algorithm.

If those methods are used, having a perfectly accurate and fair algorithm is impossible14, but if the accuracy is defined on a dataset known to contain unfair treatment of a particular group, having a less than perfect accuracy may be deemed acceptable.

Because as AI application released as Free Software may be used and inspected by everyone, verification of whether it is free of potentially harmful discrimination is easier than if it were proprietary. Moreover, this synergises with AI transparency (see Section Transparency), as a transparent AI applicationfacilitates the understanding of the factors considered for making predictions. While necessary, releasing an AI application as Free Software does not make it fair. However, it makes fairness easier to evaluate and enforce.

Conclusions

In this article, potential issues around the democratisation of artificial intelligence (AI) and implications for human rights are highlighted, and potential Free Software solutions are presented to tackle them. In particular, it is shown that AI needs to be accessible, transparent and fair in order to be usable. While not a sufficient solution, releasing AI under Free Software licences is necessary for its widespread use throughout our information systems by making it more scrutable, trustworthy and safe for everyone.

References

1. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” arXiv:1512.03385 [cs], Dec. 2015. ↩
2. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv:1409.1556 [cs], Apr. 2015. ↩
3. X. Dastile, T. Celik, and M. Potsane, “Statistical and machine learning models in credit scoring: A systematic literature survey,” Applied Soft Computing, vol. 91, p. 106263, 2020, doi: 10.1016/j.asoc.2020.106263. ↩
4. C. Badue et al., “Self-Driving Cars: A Survey,” arXiv:1901.04407 [cs], Oct. 2019. ↩
5. D. Ensign, S. A. Friedler, S. Neville, C. Scheidegger, and S. Venkatasubramanian, “Runaway Feedback Loops in Predictive Policing,” in Conference on Fairness, Accountability and Transparency, Jan. 2018, pp. 160–171. ↩
6. N. Schwalbe and B. Wahl, “Artificial intelligence and the future of global health,” The Lancet, vol. 395, no. 10236, pp. 1579–1586, May 2020, doi: 10.1016/S0140-6736(20)30226-9. ↩
7. A. Canziani, A. Paszke, and E. Culurciello, “An Analysis of Deep Neural Network Models for Practical Applications,” arXiv:1605.07678 [cs], Apr. 2017. ↩
8. M. T. Ribeiro, S. Singh, and C. Guestrin, “"Why Should I Trust You?": Explaining the Predictions of Any Classifier,” arXiv:1602.04938 [cs, stat], Aug. 2016. ↩
9. N. Kokhlikyan et al., Captum: A unified and generic model interpretability library for PyTorch. 2020. ↩
10. “Practitioners Guide to COMPAS.” Northpointe, Mar. 2015. ↩
11. L. K. Mattu Jeff Larson, “Machine Bias,” ProPublica. Mar. 2015. ↩
12. Z. Obermeyer, B. Powers, C. Vogeli, and S. Mullainathan, “Dissecting racial bias in an algorithm used to manage the health of populations,” Science (New York, N.Y.), vol. 366, no. 6464, pp. 447–453, Oct. 2019, doi: 10.1126/science.aax2342. ↩
13. C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, “Fairness Through Awareness,” arXiv:1104.3913 [cs], Nov. 2011. ↩
14. J. Kleinberg, S. Mullainathan, and M. Raghavan, “Inherent Trade-Offs in the Fair Determination of Risk Scores,” arXiv:1609.05807 [cs, stat], Nov. 2016. ↩
15. M. B. Zafar, I. Valera, M. G. Rodriguez, and K. P. Gummadi, “Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment,” Proceedings of the 26th International Conference on World Wide Web, pp. 1171–1180, Apr. 2017, doi: 10.1145/3038912.3052660. ↩