How MongoDB Provides Strategic Advantage in Financial Services

whitePaper | August 23, 2022

This white paper describes the features and benefits of a machine learning-based resource optimization solution that helps the Dell High End Storage Field Services Team perform capacity planning more efficiently and with the potential to optimize resource utilization and overtime.

whitePaper | March 8, 2020

On February 19, 2020, the European Commission (Commission) published proposals for the regulation of Artificial Intelligence (AI) with potentially far-reaching implications both for users and developers worldwide. The proposals are contained in a densely written 26-page “White Paper” which in this alert we distill down to 10 take-away points. The White Paper, together with the other strategy documents published at the same time1, is vital reading for any business which participates in the data economy, and in particular companies that operate across borders.

whitePaper | April 14, 2021

While the new era of remote work has provided some businesses with surprising productivity boosts as high as 13%, many are still struggling to secure their suddenly expanded network of remote endpoints and IT resources against a growing onslaught of cyberthreats.

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 | August 30, 2022

In late 2019, then newly elected European Commission President Ursula von der Leyen announced her intent to make regulating the use of artificial intelligence (AI) a top priority for Europe. She promised to “put forward legislation for a coordinated European approach on the human and ethical implications of Artificial Intelligence” within her first 100 days in office.

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.