Asked by: Stephanie Peacock (Labour - Barnsley East)
Question to the Department for Environment, Food and Rural Affairs:
To ask the Secretary of State for Environment, Food and Rural Affairs, what (a) algorithmic and (b) other automated decision making systems her Department uses; and for what purposes.
Answered by Mark Spencer - Minister of State (Department for Environment, Food and Rural Affairs)
Defra is using algorithms for land use mapping at a national scale which include:
To some extent automated processes are used to complete transactions. But decisions are still governed by the policy lead, budget holder (or other) approvals. There is no independent, algorithmic logic making choices without human approval. Predictive analytics is only used at aggregate level. Individuals are not profiled.
Asked by: Jonathan Ashworth (Labour (Co-op) - Leicester South)
Question to the Department for Work and Pensions:
To ask the Secretary of State for Work and Pensions, with reference to Figure 7 on page 297 of his Department's Annual Report and Accounts 2022-23, if he will provide further details on what is being provided through funding for extra resource for Counter Fraud and Compliance over the Spending Review period.
Answered by Tom Pursglove - Minister of State (Minister for Legal Migration and Delivery)
We continue to use additional investment to build on our existing fraud and error work, as set out in the department’s Annual Report and Accounts, DWP annual report and accounts 2022 to 2023 - GOV.UK (www.gov.uk).
This includes enhancing our counter-fraud resources and investing in the use of data and analytics to identify potential fraud and error.
Asked by: Rachael Maskell (Labour (Co-op) - York Central)
Question to the Department of Health and Social Care:
To ask the Secretary of State for Health and Social Care, what assessment he has made of the adequacy of access by disabled people to cancer screening; and what information his Department holds on such access.
Answered by Helen Whately - Minister of State (Department of Health and Social Care)
NHS England is committed to improving the accessibility of the breast, bowel and cervical cancer screening programmes. Providers of NHS screening services are required to make reasonable adjustments to ensure that their services are accessible to disabled people.
The Department and NHS England are working to set out actions to improve accessibility to and uptake of screening including ensuring that Primary Care Networks are provided with primary care data analytics for population segmentation and risk stratification to allow them to understand in depth their populations’ health and care needs for screening programmes. NHS England is supporting a range of research and evaluation to assess the feasibility and acceptability of self-sampling within the NHS Cervical Screening Programme to support an improvement in the accessibility of cervical screening.
The breast screening service offers longer appointments at accessible sites to support women with physical disabilities to have a successful screen. Services make reasonable adjustments, within the constraints of equipment, to ensure that disabled people are offered the opportunity to have breast screening.
Asked by: Wendy Chamberlain (Liberal Democrat - North East Fife)
Question to the Department for Work and Pensions:
To ask the Secretary of State for Work and Pensions, what estimate his Department has made of the cost to the public purse of benefits that were fraudulently claimed in each of the last ten financial years.
Answered by Tom Pursglove - Minister of State (Minister for Legal Migration and Delivery)
The Department for Work and Pensions’ (DWP) estimates on the value of both fraud and error in the benefit system, can be found in our annually published statistical report on the Monetary Value of Fraud and Error. Reports for each of the last ten financial years can be found at:
Fraud and error in the benefit system - GOV.UK (www.gov.uk).
This year’s figures show that the work we have been undertaking to reduce Fraud and Error is having an impact, with the headline rate of overpayment having decreased by 0.4% from 4.0% to 3.6%.
Our Fraud Plan, Fighting Fraud in the Welfare System, published on 19 May 2022, sets out our approach and explains how additional investment is allowing us to recruit 1,400 more staff into our counter-fraud teams and develop enhanced data analytics as a means of preventing and detecting fraud and error.
Additionally, we are creating a dedicated team to deliver Targeted Case Reviews of existing Universal Credit claims. This supports wider Government aims of strong oversight and control and efficiently managing the public purse. Over the next five years we expect to review millions of potentially high-risk claims, including suspicious cases which entered our system at the height of the pandemic.
More information on our Fraud Plan, which also explains our ambition to modernise and strengthen our legislative framework, can be found here:
Fighting Fraud in the Welfare System - GOV.UK (www.gov.uk).
Asked by: Wendy Chamberlain (Liberal Democrat - North East Fife)
Question to the Department for Work and Pensions:
To ask the Secretary of State for Work and Pensions, what steps her Department is taking to (a) identify and (b) reduce fraud within the benefits system.
Answered by Tom Pursglove - Minister of State (Minister for Legal Migration and Delivery)
The Department for Work and Pensions’ (DWP) estimates on the value of both fraud and error in the benefit system, can be found in our annually published statistical report on the Monetary Value of Fraud and Error. Reports for each of the last ten financial years can be found at:
Fraud and error in the benefit system - GOV.UK (www.gov.uk).
This year’s figures show that the work we have been undertaking to reduce Fraud and Error is having an impact, with the headline rate of overpayment having decreased by 0.4% from 4.0% to 3.6%.
Our Fraud Plan, Fighting Fraud in the Welfare System, published on 19 May 2022, sets out our approach and explains how additional investment is allowing us to recruit 1,400 more staff into our counter-fraud teams and develop enhanced data analytics as a means of preventing and detecting fraud and error.
Additionally, we are creating a dedicated team to deliver Targeted Case Reviews of existing Universal Credit claims. This supports wider Government aims of strong oversight and control and efficiently managing the public purse. Over the next five years we expect to review millions of potentially high-risk claims, including suspicious cases which entered our system at the height of the pandemic.
More information on our Fraud Plan, which also explains our ambition to modernise and strengthen our legislative framework, can be found here:
Fighting Fraud in the Welfare System - GOV.UK (www.gov.uk).
Asked by: Bill Esterson (Labour - Sefton Central)
Question to the HM Treasury:
To ask the Chancellor of Exchequer, with reference to the National Semiconductor Strategy, published 19 May 2023, whether it is his policy to fully restore R&D tax credits that were reduced to incentivise research and development.
Answered by Victoria Atkins - Secretary of State for Health and Social Care
Semiconductors are an essential component for the functioning of almost every electronic device we use, as well as underpinning future technologies such as artificial intelligence, quantum and 6G. To support this vitally important sector, the Semiconductor Strategy set out how £1 billion of Government investment over the next decade will improve access to infrastructure, power more research and development and facilitate greater international cooperation.
As part of the ongoing research and development (R&D) tax reliefs review, the Government announced at Autumn Statement 2022 that the R&D tax reliefs would be reformed to ensure taxpayer’s money is spent as effectively as possible, whilst leaving the level of R&D related business investment in the economy unchanged.
The SME scheme cost twice as much as the Research and Development Expenditure Credit (RDEC), and its cash value to firm was three times that of RDEC - yet it incentivised as little as 60p of additional R&D for each £1 spent, compared to as much as £2.70 additional R&D per £1 of RDEC. Following the corporation tax rise from April 2023, the SME scheme would have become even more generous in cash terms, and RDEC less.
The Chancellor committed to considering the case for further support for R&D intensive SMEs, and at Spring Budget announced a new permanent rate of relief for the most R&D intensive loss-making SMEs. This is worth around £500 million a year and will benefit around 20,000 SMEs a year by 2027-2028.
To support modern methods of innovation, the Government is expanding the scope of qualifying expenditure for R&D tax reliefs to include data, cloud computing and pure mathematics costs. This means that businesses will be able to claim more R&D tax relief for cutting-edge R&D methods such as genome sequencing, machine learning, and data analytics.
Asked by: Jonathan Ashworth (Labour (Co-op) - Leicester South)
Question to the Department for Work and Pensions:
To ask the Secretary of State for Work and Pensions, whether he plans to publish details of the (a) nature and (b) operation of the machine learning algorithms used in trials to detect fraud in claims for Universal Credit advances.
Answered by Tom Pursglove - Minister of State (Minister for Legal Migration and Delivery)
The DWP’s Integrated Risk and Intelligence Service uses data and analytics to identify claims that may warrant closer inspection (or may need additional consideration), assisting in the prevention and detection of fraud and error. It is right that we keep up with fraud in today’s digital age, so that we can prevent, detect and deter those who would try to exploit the benefit system and more importantly, improve our support for genuine claimants.
However, we currently have no plans to publish details of either the (a) nature or (b) operation of the machine learning algorithms used in trials to detect fraud in claims for Universal Credit advances. Similarly, we also have no plans to publish the results of trials using machine learning algorithms to detect fraud in claims for Universal Credit advances. It is not in the public interest to publish, as it contains information that fraudulent actors could use to defraud the benefit system and impact the public purse adversely.
We have conducted an equalities assessment of trials using machine learning algorithms to detect fraud in claims for Universal Credit advances.
The department has robust processes to ensure ethical use and impact of data is considered, which includes Equality Impact Assessments for large-scale transformative initiatives that involve personal data, aligned with data-ethics frameworks, codes of practice, and working principles for analytical communities within the department that work with personal data.
The DWP’s Personal Information Charter (PIC) ensures that its customers are aware of the DWP’s use of Artificial Intelligence.
Importantly, it should be noted that we do not use algorithms to make decisions regarding fraudulent claims. These are always made by humans.
The Information Commissioner’s Office have indicated publicly that they are broadly supportive of the current use of AI within the welfare benefit system, based on sampling they have undertaken.
Asked by: Jonathan Ashworth (Labour (Co-op) - Leicester South)
Question to the Department for Work and Pensions:
To ask the Secretary of State for Work and Pensions, whether his Department has conducted an equalities impact assessment of trials using machine learning algorithms to detect fraud in claims for Universal Credit advances.
Answered by Tom Pursglove - Minister of State (Minister for Legal Migration and Delivery)
The DWP’s Integrated Risk and Intelligence Service uses data and analytics to identify claims that may warrant closer inspection (or may need additional consideration), assisting in the prevention and detection of fraud and error. It is right that we keep up with fraud in today’s digital age, so that we can prevent, detect and deter those who would try to exploit the benefit system and more importantly, improve our support for genuine claimants.
However, we currently have no plans to publish details of either the (a) nature or (b) operation of the machine learning algorithms used in trials to detect fraud in claims for Universal Credit advances. Similarly, we also have no plans to publish the results of trials using machine learning algorithms to detect fraud in claims for Universal Credit advances. It is not in the public interest to publish, as it contains information that fraudulent actors could use to defraud the benefit system and impact the public purse adversely.
We have conducted an equalities assessment of trials using machine learning algorithms to detect fraud in claims for Universal Credit advances.
The department has robust processes to ensure ethical use and impact of data is considered, which includes Equality Impact Assessments for large-scale transformative initiatives that involve personal data, aligned with data-ethics frameworks, codes of practice, and working principles for analytical communities within the department that work with personal data.
The DWP’s Personal Information Charter (PIC) ensures that its customers are aware of the DWP’s use of Artificial Intelligence.
Importantly, it should be noted that we do not use algorithms to make decisions regarding fraudulent claims. These are always made by humans.
The Information Commissioner’s Office have indicated publicly that they are broadly supportive of the current use of AI within the welfare benefit system, based on sampling they have undertaken.
Asked by: Jonathan Ashworth (Labour (Co-op) - Leicester South)
Question to the Department for Work and Pensions:
To ask the Secretary of State for Work and Pensions, whether his Department plans to publish the results of trials using machine learning algorithms to detect fraud in claims for Universal Credit advances.
Answered by Tom Pursglove - Minister of State (Minister for Legal Migration and Delivery)
The DWP’s Integrated Risk and Intelligence Service uses data and analytics to identify claims that may warrant closer inspection (or may need additional consideration), assisting in the prevention and detection of fraud and error. It is right that we keep up with fraud in today’s digital age, so that we can prevent, detect and deter those who would try to exploit the benefit system and more importantly, improve our support for genuine claimants.
However, we currently have no plans to publish details of either the (a) nature or (b) operation of the machine learning algorithms used in trials to detect fraud in claims for Universal Credit advances. Similarly, we also have no plans to publish the results of trials using machine learning algorithms to detect fraud in claims for Universal Credit advances. It is not in the public interest to publish, as it contains information that fraudulent actors could use to defraud the benefit system and impact the public purse adversely.
We have conducted an equalities assessment of trials using machine learning algorithms to detect fraud in claims for Universal Credit advances.
The department has robust processes to ensure ethical use and impact of data is considered, which includes Equality Impact Assessments for large-scale transformative initiatives that involve personal data, aligned with data-ethics frameworks, codes of practice, and working principles for analytical communities within the department that work with personal data.
The DWP’s Personal Information Charter (PIC) ensures that its customers are aware of the DWP’s use of Artificial Intelligence.
Importantly, it should be noted that we do not use algorithms to make decisions regarding fraudulent claims. These are always made by humans.
The Information Commissioner’s Office have indicated publicly that they are broadly supportive of the current use of AI within the welfare benefit system, based on sampling they have undertaken.
Asked by: Rushanara Ali (Labour - Bethnal Green and Bow)
Question to the Department of Health and Social Care:
To ask the Secretary of State for Health and Social Care, what assessment he has made of the implications for his policies of comments from Sir David King in the Independent newspaper on the UK's current preparedness for a pandemic.
Answered by Maria Caulfield - Parliamentary Under Secretary of State (Department for Business and Trade) (Minister for Women)
It is not possible to predict when the next pandemic will occur or what virus will cause it. Therefore, our strategic approach to pandemic preparedness constantly evolves in response to new scientific information, learning from our response to prior pandemics, responses to other infectious disease outbreaks and rigorous exercising to test our response mechanisms.
The Department is working with the Centre for Pandemic Preparedness in the UK Health Security Agency (UKHSA) to ensure we have a flexible and capabilities-based approach to pandemic preparedness that will allow us to respond to future outbreaks, protect the health of the United Kingdom population, and contribute to minimising the wider societal disruption that pandemics and infectious diseases can cause.
The UKHSA brings together our world-leading public health science and expertise, cutting-edge capabilities in data analytics and genomic surveillance, at-scale testing and contact tracing capabilities to respond to a range of pandemic threats, as outlined in UKHSA’s Science Strategy, published on 16 May 2023. Information on the strategy is available at the following link: