The various potential policy levers described in Part IV have their own strengths and weaknesses. Promoting disclosure to experts in the field will likely require a combination of these levers, depending on the context. For example, disclosure negotiated by the regulator, while potentially very powerful in the life sciences field, will not be available in areas without strong regulatory gatekeepers. For life sciences, one area where we proposed that algorithmic disclosure stands out in particular, all the levers we mentioned are available, and the right combination depends on a mix of political economy, technocratic efficiency, and redundancy to ensure effectiveness. Our goal is to start this conversation, not to end it. Beyond this previous work, we argue here that developer secrecy regarding tools significantly hinders machine learning`s ability to elucidate system transparency. Our argument is more than a general argument in favor of open science, or even a call to attention to the exceptionally strong competitive power of secrecy in machine learning.17 On the contrary, openness to the non-intuitive results generated by machine learning is particularly important because it sheds light on one of the areas where such light is most needed. [85]. See, for example, Ruha Benjamin, Assessing Risk, Automating Racism, 366 Science 421, 421–22 (2019); Effy Vayena, Alessandro Blasimme & I.

Glenn Cohen, Machine Learning in Medicine: Addressing Ethical Challenges, 15 PLoS Med. 1, 1-4 (2018) (description of biases, among other challenges); Price, note 57 above, at 66 (describes the potential for population-based bias in medical big data and artificial intelligence); Han Liu & Mihaela Cocea, Granular Computing-Based Approach for Classification Towards Reduction of Bias in Ensemble Learning, 2 Granular Computing 131, 131 (2017). Especially in its more complex forms (for example, convolutional deep learning neural networks), machine learning works very differently from standard software used to perform analysis and predictions. While standard software applies explicit, human-made decision rules to data — rules that humans have (hopefully) designed with some understanding of the underlying systems — machine learning algorithms don`t embody predictive rules before they are exposed to data. Ferster, B.C. & Skinner, B. F. (1957). Reinforcement schedules.

New York: Appleton Century Crofts. What is potentially more important is that to the extent that collectors of large datasets from different populations and with different collection strategies do so, different datasets are likely to have different limitations and biases.85 When these datasets are combined as a result of disclosure, these differences in representativeness can at least partially counteract each other, resulting in improved performance. In terms of understanding, tensions and discrepancies between models formed in different ways and with similar goals can also indicate fruitful paths for future exploratory work. [89]. If an independent researcher can know that she obtained the same results as the original researcher, the research is also verifiable. A particular challenge arises when implicit knowledge – often know-how – is also necessary for reproducibility. See laura G. Pedraza-Fariña, Spill Your (Trade) Secrets: Knowledge Networks as Innovation Drivers, 92 Notre Dame L. Rev. 1561 (2017) (describes the importance of know-how for innovation and the emergence of informal networks that share this know-how). Beyond the issues of market entry, competition and price, it is about disclosure outside the development companies themselves. Even if companies compete fiercely for data and can even innovate gradually on the basis of this data – and therefore there are no problems from a competition policy point of view in the short term – the pace of disclosure outside these companies can be quite slow.

Below we discuss the benefits of disclosure, especially for local experts. Some of these benefits are benefits of open science in general. However, robust disclosure can be especially useful for machine learning. Ordinary reverse engineering processes may be less likely to generate competitive and public domain training data at scale. More importantly, robust disclosure (and challenging that disclosure) provides a relatively inexpensive mechanism for an initial review of the interesting but non-intuitive results of machine learning. Positive reinforcement is a term described by B. F. Skinner in his theory of operant conditioning.

With positive reinforcement, a reaction or behavior is amplified by rewards, resulting in the repetition of the desired behavior. The reward is a reinforcement stimulus. [29]. Voir en général FDA, Developing a Software Precertification Program: A Working Model (2019) [ci-après FDA, Software Precertification Program], www.fda.gov/ media/119722/download [perma.cc/5Q5F-MQFY] (description du programme pilote « Pre-Cert » pour les développeurs ayant une culture d`excellence éprouvée) ; FDA, Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) (2019), www.fda.gov/media/122535/ Download [perma.cc/7MA4-L4QN] (Beschreibung eines Modells für die Verwaltung von Änderungen in der künstlichen Intelligenz zur Senkung der Anforderungen für ergänzende Zulassungsanträge).