Unsolved problem
How can we ensure that data sciences’ reliance on trust does not lead to a weakening of the scientific process. Can this reliance allow errors, biases and even misconduct to go unnoticed and lead to flawed conclusions?
Abstract
Data is the foundation of any scientific, industrial or commercial process and yet, despite its importance, it is susceptible to undue disclosures, leaks, losses, manipulation, or fabrication. While best practice and regulations guide data management and protection, academic research and commercial data handling have recently been marred by scandals, revealing the brittleness of data management. These incidents often occur without visibility or accountability, highlighting the urgent need for a systematic structure for safe, honest, and auditable data management.
In a recent paper, Florian and others introduced the concept of “Honest Computing” as the practice and approach that emphasises transparency, integrity, and ethical behaviour within the realm of computing and technology. Honest Computing ensures that computer systems and software operate honestly and reliably without hidden agendas, biases, or unethical practices. It enables privacy and confidentiality of data and code by design and by default. The paper also introduces a reference framework to achieve demonstrable data lineage and provenance, contrasting it with Secure Computing, a related but differently-orientated form of computing.
Honest Computing opens new ways of creating technology-based processes and workflows which permit the migration of regulatory frameworks for data protection from principle-based approaches to rule-based ones. Addressing use cases in many fields, from AI model protection and ethical layering to digital currency formation for finance and banking, trading, and healthcare, this foundational layer approach can help define new standards for appropriate data custody and processing.
About the speaker
Florian Guitton is a software engineer and researcher with extensive experience in advanced computing. Following his PhD at Imperial College London's Discovery Sciences Group, he became a founding member of the Data Science Institute in 2014. Over the past decade his work has focused on applying computing technologies to healthcare and biomedical research with a growing emphasis on Confidential Computing. In 2024, he introduced the concept of "Honest Computing" in a position paper presented at the International Data for Policy conference, marking a significant contribution to the field and reflecting his commitment to advancing secure and ethical computing practices.
About Unsolved Problems
This series of seminars forms part of the DSI Squared collaboration between the Data Science Institutes at LSE and Imperial College London Data Science Institute. innovation by bridging the social sciences and computer science and STEM subjects. Researchers from both Institutes are invited to showcase their ideas in front of an audience. Attendees offer their expertise and knowledge to crowd source solutions to research challenges. At these lunchtime meetings, a light lunch is provided at the seminar.