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In support of the Monetary Authority of Singapore’s FEAT principles (Fairness, Ethics, Accountability and Transparency), UOB has formulated a validation framework that defines the key processes and guidelines on data ethics, including independent validation of high materiality AIDA (Artificial Intelligence and Data Analytics) models.
Data forms the rails on which the digital economy runs. As we become more digital-focused in more areas of our lives, the proliferation of data being generated and stored will only increase. Being the trusted custodians of customer data, financial services firms will need to understand the responsibilities associated with data ownership and the ethical use of these data.
As we are still at the early stages of our data journey, we have the opportunity to start the journey on the right footing by championing a common set of principles on how financial industry can use data responsibly. These principles should be the standard that guides the financial institutions as they balance data-driven customer innovation with ethics.
In November 2018, MAS released a set of principles to promote fairness, ethics, accountability and transparency (FEAT) in the use of artificial intelligence and data analytics (AIDA) in Singapore’s financial sector. This set of principles is not intended to be prescriptive and it provides high-level guidelines for financial services firms to adopt as a foundational framework.
At UOB, we are also committed to being honourable and our priority is to safeguard the interests of our customers. In view of these principles, UOB has set up a multi-disciplinary data ethics task force to develop a governing framework of policies and processes that ensure its AIDA-driven decisions comply with the FEAT principles. In addition, through the MAS Veritas Consortium, of which UOB is a founding member, we have collaborated with MAS and our partner, Element AI, to develop an assessment methodology to measure fairness in AIDA models.
At UOB, we have robust standards, processes and policies to ensure the ethical governance and use of data. We will continue to strengthen this foundation of trust, by building upon the best practices in Responsible AI and Data Ethics.
