CREATING APPLICATIONS FOR DIGITAL TRANSFORMATION

whitePaper | August 9, 2020

Even before the COVID-19 pandemic, the pace of change in the financial services industry was exhausting. The challenges just kept coming: from the 2008 financial crisis to a raft of fintech newcomers and established brands pivoting into the industry, a panoply of regulations and the looming threat of a recession — to name a few.

whitePaper | December 26, 2022

IBM embraces five foundational pillars of trustworthy AI: Explainability, Fairness, Robustness, Transparency, and Privacy. These pillars underpin the development, deployment and use of AI systems. This document and IBM’s trustworthy AI pillars are meant to help you align on both process and outcomes.

whitePaper | March 30, 2022

This paper presents data from a large-scale study on how code quality impacts software companies in terms of time-to-market and product experience. We conclude with an analysis of the impact and specific recommendations towards successful software development.

whitePaper | February 15, 2023

Artificial intelligence (AI) is an enabling technology impacting the global economy and international trade. Combined with business-process-oriented automation and more efficient data flow exchanges, AI further promises to lift barriers to international trade, stimulate growth in global electronic commerce and allow for better predictions and associations to inform policy decisions.

whitePaper | August 9, 2020

The payments industry is going through a period of massive disruption. Consumers increasingly expect a fast, easy payment process, coupled with immediate, uninterrupted access to account information and services.

whitePaper | July 20, 2022

The growing diversity of actors and circumstances involved in AI/ML deployment makes establishing a set of ethical participatory practices especially difficult for this field. Even among practitioners working at the same organization, there can be substantial differences in both their knowledge about how AI/ML systems were created or will be used and their ability to incorporate inclusive practices. Additionally, the greater availability of AI development platforms, including “no-code” platforms, means that algorithmic models can be deployed without having deep expertise, expanding the number of circumstances automated systems are deployed in. This means many more instances where automated systems are being deployed without consideration of how the algorithms were developed, the provenance of the datasets and nature of the bias on which they were trained and tested, and the ethical implications of their development and deployment.