Award

Explainability for Vulnerability Identification in AI Systems

DEFENCE SCIENCE AND TECHNOLOGY LABORATORY

This public procurement record has 1 release in its history.

Award

24 Aug 2022 at 12:49

Award Notice
Summary of the contracting process

The procurement process for the contract titled "Explainability for Vulnerability Identification in AI Systems" was managed by the Defence Science and Technology Laboratory from Porton Down, SALISBURY, England. This award falls under the industry category of "Software package and information systems." The procurement stage was completed with a contract value of 149,717 GBP. The procurement method used was a direct procurement method with single tender action. The contract period spans from September 1, 2022, to August 31, 2023.

This tender presents an opportunity for businesses, particularly those involved in developing AI technologies and software solutions, to engage with the Defence Science and Technology Laboratory. Small and medium-sized enterprises (SMEs) would be well-suited for this opportunity. Understanding and exploiting artificial intelligence explainability methodologies is the focus of this contract, aimed at identifying and exposing vulnerabilities in neural network-based machine vision algorithms. Businesses interested in advancing AI technology in security and defence contexts could benefit from competing in this procurement process.

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Notice Information

Notice Title

Explainability for Vulnerability Identification in AI Systems

Notice 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.

Notice Details

Publication & Lifecycle

Open Contracting ID
ocds-b5fd17-00a2575c-79f5-4d35-9d5f-abc8fa2c317c
Publication Source
Contracts Finder
Latest Notice
https://www.contractsfinder.service.gov.uk/Notice/32b71324-c3b8-4715-a708-db4659db552a
Current Stage
Award
All Stages
Award

Procurement Classification

Notice Type
Award Notice
Procurement Type
Standard
Procurement Category
Goods
Procurement Method
Direct
Procurement Method Details
Single tender action (below threshold)
Tender Suitability
SME
Awardee Scale
Large

Common Procurement Vocabulary (CPV)

CPV Divisions

48 - Software package and information systems

CPV Codes

48000000 - Software package and information systems

Notice Value(s)

Tender Value
£149,717 £100K-£500K
Lots Value
Not specified
Awards Value
£149,717 £100K-£500K
Contracts Value
Not specified

Notice Dates

Publication Date
24 Aug 20223 years ago
Submission Deadline
8 Jul 2022Expired
Future Notice Date
Not specified
Award Date
3 Aug 20223 years ago
Contract Period
31 Aug 2022 - 31 Aug 2023 6-12 months
Recurrence
Not specified

Notice Status

Tender Status
Complete
Lots Status
Not Specified
Awards Status
Active
Contracts Status
Not Specified

Buyer & Supplier

Contracting Authority (Buyer)

Main Buyer
DEFENCE SCIENCE AND TECHNOLOGY LABORATORY
Contact Name
Commercial CIS Transparency
Contact Email
commercialcistransparency@dstl.gov.uk
Contact Phone
Not specified

Buyer Location

Locality
SALISBURY
Postcode
SP4 0JQ
Post Town
Salisbury
Country
England
Major Region (ITL 1)
TLK South West (England)
Basic Region (ITL 2)
TLK7 Gloucestershire and Wiltshire
Small Region (ITL 3)
TLK72 Wiltshire
Delivery Location
Not specified
Local Authority
Wiltshire
Electoral Ward
Winterslow & Upper Bourne Valley
Westminster Constituency
Salisbury

Supplier Information

Number of Suppliers
1
Supplier Name

CITY, UNIVERSITY OF LONDON

Further Information

Open Contracting Data Standard (OCDS)

View full OCDS Record for this contracting process

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The Open Contracting Data Standard (OCDS) is a framework designed to increase transparency and access to public procurement data in the public sector. It is widely used by governments and organisations worldwide to report on procurement processes and contracts. 

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