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Monetary Authority of Singapore Publishes Methodologies for Responsible AI in Financial Institutions

The Monetary Authority of Singapore (MAS) announced the release of five white papers detailing assessment methodologies for the Fairness, Ethics, Accountability and Transparency (FEAT) principles, to guide the responsible use of AI by financial institutions (FIs). The methodologies have been compiled by MAS and more than 20 private companies working collectively as a consortium called ‘Veritas’.

The white papers provide: 

  • a comprehensive FEAT checklist for FIs to adopt during their Artificial Intelligence and Data Analytics (AIDA) software development lifecycles;
  • an enhanced Fairness Assessment Methodology to enable FIs to define their AIDA system’s fairness objectives, identify personal attributes of individuals and any unintentional bias;
  • a new Ethics and Accountability Assessment Methodology, which provides a framework for FIs to carry out quantifiable measurement of ethical practices, in addition to the qualitative practices currently adopted;
  • a new Transparency Assessment Methodology which helps FIs determine whether and how much internal/external transparency is needed to explain and interpret the predictions of machine learning models.

The new open-source software, assessment methodologies and enhanced guidance will further improve the technical capabilities of financial institutions in developing responsible AI for the financial sector. The Veritas initiative continues to deliver tangible outcomes that demonstrate collaborative public-private partnership to drive trust in the adoption of AI technology, enhance confidence and foster innovation in Singapore’s FinTech ecosystem

– Sopnendu Mohanty, Chief FinTech Officer, MAS

They include a checklist across FEAT principles for financial institutions to adopt during AI and data analytics software development; and a ‘Transparency Assessment Methodology’ that seeks to help financial institutions to determine whether and how much internal and external transparency is needed to ‘explain and interpret’ the predictions of machine-learning models.

The public-private consortium has also just released the first version of an open-source software toolkit, which similarly aims to drive financial institutions’ adoption and adherence to FEAT methodologies and principles. The software enables the automation of metrics assessment and visualisation, with plug-ins integrating with financial institutions’ IT systems.

Veritas is set against the backdrop of regulators worldwide striving to tackle the opportunities and challenges presented by AI and data. Examples include the European Union’s proposal for a regulation laying down harmonised AI rules and, in the UK, the Bank of England and Financial Conduct Authority’s establishment of an ‘Artificial Intelligence Public-Private Forum’ to discuss the use and impact of AI in financial services.

In the next phase, the consortium will develop additional use cases and run pilots with selected FI members to integrate the methodologies with members’ existing governance framework. MAS is also collaborating with the Infocomm Media Development Authority and the Personal Data Protection Commission (PDPC) to include the Toolkit in the PDPC’s Trustworthy AI testing framework.

As reported by OpenGov Asia, AI Singapore (AISG) is looking to boost Singapore’s Artificial Intelligence (AI) and machine learning (ML) capabilities with the use of graph technology, which enhances analytics by finding unknown relationships in data that are not being identified by traditional means. A graph database, with its structure of nodes and edges, creates connecting and traversing links which allow for accelerated processing of inter-connected data. This makes it possible to process terabytes of data and traverse millions of connections in a fraction of a second.

AISG recently signed a memorandum of understanding (MOU) with a graph database vendor to help Singapore businesses to leverage graph database capabilities for AI/ML, and to build Singapore’s graph database capabilities and talent pool. The collaboration is committed to helping build a steady pipeline of AI professionals with graph database analytics capabilities by supporting the research and development needs of the apprentices in AISG’s AI Apprenticeship Programme (AIAP).

According to Asia Pacific AI Readiness Index, Singapore is again at the top spot for readiness in the adoption of AI, compared to 10 other economies in the region. the index assesses the readiness of governments, businesses, and consumers across eleven APAC economies in their adoption of AI technologies.

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