Everyday Ethics for Artificial Intelligence

whitePaper | June 28, 2022

Artificial intelligence (AI) has been accelerating across sectors. It has great potential to benefit society but the technology’s full potential can only be realized if it includes the diversity of the populations it represents throughout every step of its development. With growing concerns about bias, data privacy and lack of representation, it is critical to re-evaluate the way in which AI is both designed and deployed to ensure that all affected stakeholders and communities reap the benefits of the technology, rather than any harm.

whitePaper | November 1, 2021

Robotics, automation, Internet of Things (IoT), and data are not new in manufacturing. Manufacturing is one of the most data-rich and first industries piloting artificial intelligence at the edge. It’s been estimated, however, that about 95% of all industrial data goes unused. The reason? Legacy equipment and operational systems that tend to be proprietary and siloed are incapable of communicating with each other.

whitePaper | October 26, 2022

The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was published by the White House Office of Science and Technology Policy in October 2022. This framework was released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered world.” Its release follows a year of public engagement to inform this initiative. The framework is available online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights

whitePaper | December 27, 2022

Welcome back to our white paper series about how to use AI and data throughout the new business and underwriting processes. In the first white paper we discussed the importance of product design and the benefits of using AI.

whitePaper | October 18, 2022

The term Artificial Intelligence for IT Operations (AIOps) combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.

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.