Our former colleague Irene Unceta has presented her thesis “Adapting by copying. Towards a sustainable Machine Learning“, which she has carried out with the University of Barcelona. During this fruitful journey, Irene worked with us thanks to an industrial doctoral programme, which allows to develop a research project guided by an academic institution, but with the knowledge and application to real problems offered by an institution like BBVA.
For our part, it has been a great pleasure to have Irene as a colleague and to accompany her in the steps she has taken. We are convinced of the value of these research projects and their application to the real needs in the business world. These types of projects allow us to continue to innovate in the AI-based solutions and products that we propose to our clients and other teams in the bank.
Finally, we would like to reflect here the original abstract of the work carried out by Irene, together with Oriol Pujol and Jordi Nin:
Despite the rapid growth of machine learning in the past decades, deploying automated decision making systems in practice remains a challenge for most companies. On an average day, data scientists face substantial barriers to serving models into production. Production environments are complex ecosystems, still largely based on on-premise technology, where modifications are timely and costly. Given the rapid pace with which the machine learning environment changes these days, companies struggle to stay up-to-date with the latest software releases, the changes in regulation and the newest market trends. As a result, machine learning often fails to deliver according to expectations. And more worryingly, this can result in unwanted risks for users, for the company itself and even for the society as a whole, insofar the negative impact of these risks is perpetuated in time. In this context, adaptation is an instrument that is both necessary and crucial for ensuring a sustainable deployment of industrial machine learning
This dissertation is devoted to developing theoretical and practical tools to enable adaptation of machine learning models in company production environments. More precisely, we focus on devising mechanisms to exploit the knowledge acquired by models to train future generations that are better fit to meet the stringent demands of a changing ecosystem. We introduce copying as a mechanism to replicate the decision behaviour of a model using another that presents differential characteristics, in cases where access to both the models and their training data are restricted. We discuss the theoretical implications of this methodology and show how it can be performed and evaluated in practice. Under the conceptual framework of actionable accountability we also explore how copying can be used to ensure risk mitigation in circumstances where deployment of a machine learning solution results in a negative impact to individuals or organizations.
It can be read in full at this link.