The AI Act: Help or Hindrance for SMEs?

whitePaper | October 3, 2022

Contact center associates deal with complex incall tasks, across multiple systems. High staff turnover is common, and training up new staff can take weeks or even months. These challenges can quickly begin to affect engagement, performance and KPIs. With a cohesive digital strategy, your Digital Workforce has instant access to all customer information from one system, meeting the customer’s expectation of immediacy.

whitePaper | January 28, 2022

Since more and more decisions that used to be made by humans are nowadays made either with the help of Artificial Intelligence (AI) or by AI alone, it is essential that the AI algorithms are “fair.” In this whitepaper, we discuss issues surrounding the fairness of AI applications, with a special focus on how it can be assessed independently and subsequently certified. We explain why an AI application cannot be classified – and subsequently certified – as “fair” or “unfair” in a general sense and propose an approach that makes it possible to classify it as “fair” or “unfair” under the (application)-specific definition of fairness.

whitePaper | March 1, 2020

The lucrative business of banks selling their customers “financial advice” coupled with financial products has been in the spotlight recently with the events that have shaken up the banking and financial services world. The most notable ones are the investigations of the Royal Commissions into the misconduct of the banking and superannuation industry1 , the misconduct charges of £264 billion fined by EU on the top 20 banks2 , the Wells Fargo fake accounts scandal3 and the nearly 1000 cases of fraud reported to FINRA resulting in a fine of $61 Million in 20184 .

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 | January 8, 2020

AI in manufacturing is a game-changer. It has the potential to transform performance across the breadth and depth of manufacturing operations. However, the massive potential of this new Industrial 4.0 era will only be realized if manufacturers really focus their efforts on where AI can add most value and then drive the solutions to scale.

whitePaper | December 22, 2022

Machine learning, one of the key building blocks of AI, has been a part of the technological world since the 1950s, when the earliest programmers asked computers to make sense of large sets of data. Programmers have increasingly refined the ability of machines to study data in order to detect patterns that allow computers to then organise information, identify relationships, make predictions and detect anomalies. Today, modern applications of AI have already given us self-driving cars and virtual assistants and have helped us detect fraud and manage resources like electricity more efficiently. Sectors as diverse as retail, sports, banking, manufacturing and healthcare have all found applications for machine learning and AI.