# Explainability for Vulnerability Identification in AI Systems

> A sourced UK public-sector contract award published by Defence Science and Technology Laboratory.

## Key facts

- Public buyer: [Defence Science and Technology Laboratory](/buyers/defence-science-and-technology-laboratory/contract-awards)
- Contract winner: [City, University of London](/contract-winners/city-university-of-london/public-sector-contract-awards)
- Award value: £149,717
- Tender value: £149,717
- Award date: 4 August 2022
- Published: 24 August 2022
- Contract start: 1 September 2022
- Contract end: 31 August 2023
- Stage: awarded
- Procurement category: goods
- Notice ID: 32b71324-c3b8-4715-a708-db4659db552a-566358
- OCID: ocds-b5fd17-00a2575c-79f5-4d35-9d5f-abc8fa2c317c

## Description

The Research and Development submission to the Strategic Review \(SR20\) recognised the need to advance MOD's ability to adopt critical and game-changing technology, enabling autonomous systems on the battlefield and in the command space through the use of artificial intelligence\. It proposed to do this by establishing a Defence AI Centre with the science and technology component delivered by a Defence AI Centre Experimentation hub \(DAIC-X\) led by Dstl\. A key objective for DAIC-X is to understand and develop good practice in managing AI verification, validation, vulnerabilities as well as wider issues including trust and transparency and legal and ethical considerations\. This task will research the potential to exploit artificial intelligence explainability \(XAI\) methodologies to identify and expose vulnerabilities in neural network-based machine vision algorithms\. Please see the attached Tasking Form for further information regarding this award\.

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## Source

[View the original notice on Contracts Finder](https://www.contractsfinder.service.gov.uk/Notice/32b71324-c3b8-4715-a708-db4659db552a-566358)