Call to make tech firms report data centre energy use as AI booms

Curated on Posted on by Stefaan Verhulst

Article by Sandra Laville: “Tech companies should be required by law to report the energy and water consumption for their data centres, as the boom in AI risks causing irreparable damage to the environment, experts have said.

AI is growing at a rate unparalleled by other energy systems, bringing heightened environmental risk, a report by the National Engineering Policy Centre (NEPC) said.

The report calls for the UK government to make tech companies submit mandatory reports on their energy and water consumption and carbon emissions in order to set conditions in which data centres are designed to use fewer vital resources…(More)”.

The new politics of AI

Curated on Posted on by Stefaan Verhulst

Report by the IPPR: AI is fundamentally different from other technologies – it is set to unleash a vast number of highly sophisticated ‘artificial agents’ into the economy. AI systems that can take actions and make decisions are not just tools – they are actors. This can be a good thing. But it requires a novel type of policymaking and politics. Merely accelerating AI deployment and hoping it will deliver public value will likely be insufficient.

In this briefing, we outline how the summit constitutes the first event of a new era of AI policymaking that links AI policy to delivering public value. We argue that AI needs to be directed towards societies’ goals, via ‘mission-based policies’….(More)”.

Unlocking AI’s potential for the public sector

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Article by Ruth Kelly: “…Government needs to work on its digital foundations. The extent of legacy IT systems across government is huge. Many were designed and built for a previous business era, and still rely on paper-based processes. Historic neglect and a lack of asset maintenance has added to the difficulty. Because many systems are not compatible, sharing data across systems requires manual extraction which is risky and costly. All this adds to problems with data quality. Government suffers from data which is incomplete, inconsistent, inaccessible, difficult to process and not easily shareable. A lack of common data models, both between and within government departments, makes it difficult and costly to combine different sources of data, and significant manual effort is required to make data usable. Some departments have told us that they spend 60% to 80% of their time on cleaning data when carrying out analysis.

Why is this an issue for AI? Large volumes of good-quality data are important for training, testing and deploying AI models. Poor data leads to poor outcomes, especially where it involves personal data. Access to good-quality data was identified as a barrier to implementing AI by 62% of the 87 government bodies responding to our survey. Simple productivity improvements that provide integration with routine administration (for example summarising documents) is already possible, but integration with big, established legacy IT is a whole other long-term endeavour. Layering new technology on top of existing systems, and reusing poor-quality and aging data, carries the risk of magnifying problems and further embedding reliance on legacy systems…(More)”

Local Government: Artificial intelligence use cases

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Repository by the (UK) Local Government Association: “Building on the findings of our recent AI survey, which highlighted the need for practical examples, this bank showcases the diverse ways local authorities are leveraging AI. 

Within this collection, you’ll discover a spectrum of AI adoption, ranging from utilising AI assistants to streamline back-office tasks to pioneering the implementation of bespoke Large Language Models (LLMs). These real-world use cases exemplify the innovative spirit driving advancements in local government service delivery. 

Whether your council is at the outset of its AI exploration or seeking to expand its existing capabilities, this bank offers a wealth of valuable insights and best practices to support your organisation’s AI journey…(More)”.

Data Governance Meets the EU AI Act

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Article by Axel Schwanke: “..The EU AI Act emphasizes sustainable AI through robust data governance, promoting principles like data minimization, purpose limitation, and data quality to ensure responsible data collection and processing. It mandates measures such as data protection impact assessments and retention policies. Article 10 underscores the importance of effective data management in fostering ethical and sustainable AI development…This article states that high-risk AI systems must be developed using high-quality data sets for training, validation, and testing. These data sets should be managed properly, considering factors like data collection processes, data preparation, potential biases, and data gaps. The data sets should be relevant, representative, error-free, and complete as much as possible. They should also consider the specific context in which the AI system will be used. In some cases, providers may process special categories of personal data to detect and correct biases, but they must follow strict conditions to protect individuals’ rights and freedoms…

However, achieving compliance presents several significant challenges:

  • Ensuring Dataset Quality and Relevance: Organizations must establish robust data and AI platforms to prepare and manage datasets that are error-free, representative, and contextually relevant for their intended use cases. This requires rigorous data preparation and validation processes.
  • Bias and Contextual Sensitivity: Continuous monitoring for biases in data is critical. Organizations must implement corrective actions to address gaps while ensuring compliance with privacy regulations, especially when processing personal data to detect and reduce bias.
  • End-to-End Traceability: A comprehensive data governance framework is essential to track and document data flow from its origin to its final use in AI models. This ensures transparency, accountability, and compliance with regulatory requirements.
  • Evolving Data Requirements: Dynamic applications and changing schemas, particularly in industries like real estate, necessitate ongoing updates to data preparation processes to maintain relevance and accuracy.
  • Secure Data Processing: Compliance demands strict adherence to secure processing practices for personal data, ensuring privacy and security while enabling bias detection and mitigation.

Example: Real Estate Data
Immowelt’s real estate price map, awarded as the top performer in a 2022 test of real estate price maps, exemplifies the challenges of achieving high-quality datasets. The prepared data powers numerous services and applications, including data analysis, price predictions, personalization, recommendations, and market research…(More)”

Big data for decision-making in public transport management: A comparison of different data sources

Curated on Posted on by Stefaan Verhulst

Paper by Valeria Maria Urbano, Marika Arena, and Giovanni Azzone: “The conventional data used to support public transport management have inherent constraints related to scalability, cost, and the potential to capture space and time variability. These limitations underscore the importance of exploring innovative data sources to complement more traditional ones.

For public transport operators, who are tasked with making pivotal decisions spanning planning, operation, and performance measurement, innovative data sources are a frontier that is still largely unexplored. To fill this gap, this study first establishes a framework for evaluating innovative data sources, highlighting the specific characteristics that data should have to support decision-making in the context of transportation management. Second, a comparative analysis is conducted, using empirical data collected from primary public transport operators in the Lombardy region, with the aim of understanding whether and to what extent different data sources meet the above requirements.

The findings of this study support transport operators in selecting data sources aligned with different decision-making domains, highlighting related benefits and challenges. This underscores the importance of integrating different data sources to exploit their complementarities…(More)”.

State of Digital Local Government

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Report by the Local Government Association (UK): “This report is themed around four inter-related areas on the state of local government digital: market concentration, service delivery, technology, and delivery capabilities.  It is particularly challenging to assess the current state of digital transformation in local government, given the diversity of experience, resources and lack of consistent data collection on digital transformation and technology estates. 

This report is informed through our regular and extensive engagement with local government, primary research carried out by the LGA, and the research of stakeholders. It is worth noting that research on market concentration is challenging as it is a highly sensitive area.

Key messages:

  1. Local Government is a vital part of the public sector innovation ecosystem. Local government needs their priorities and context to be understood within cross public sector digital transformation ambitions through representation on public sector strategic boards and subsequently integrated into the design of public sector guidance and cross-government products at the earliest point. This will reduce the likelihood of duplication at public expense. Local government must also have equivalent access to training as civil servants…(More)”.

Governing artificial intelligence means governing data: (re)setting the agenda for data justice

Curated on Posted on by Stefaan Verhulst

Paper by Linnet Taylor, Siddharth Peter de Souza, Aaron Martin, and Joan López Solano: “The field of data justice has been evolving to take into account the role of data in powering the field of artificial intelligence (AI). In this paper we review the main conceptual bases for governing data and AI: the market-based approach, the personal–non-personal data distinction and strategic sovereignty. We then analyse how these are being operationalised into practical models for governance, including public data trusts, data cooperatives, personal data sovereignty, data collaboratives, data commons approaches and indigenous data sovereignty. We interrogate these models’ potential for just governance based on four benchmarks which we propose as a reformulation of the Data Justice governance agenda identified by Taylor in her 2017 framework. Re-situating data justice at the intersection of data and AI, these benchmarks focus on preserving and strengthening public infrastructures and public goods; inclusiveness; contestability and accountability; and global responsibility. We demonstrate how they can be used to test whether a governance approach will succeed in redistributing power, engaging with public concerns and creating a plural politics of AI…(More)”.

Data sharing restrictions are hampering precision health in the European Union

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Paper by Cristina Legido-Quigley et al: “Contemporary healthcare is undergoing a transition, shifting from a population-based approach to personalized medicine on an individual level. In October 2023, the European Partnership for Personalized Medicine was officially launched to communicate the benefits of this approach to citizens and healthcare systems in member countries. The main debate revolves around the inconsistency in regulatory changes within personal data access and its potential commercialization. Moreover, the lack of unified consensus within European Union (EU) countries is leading to problems with data sharing to progress personalized medicine. Here we discuss the integration of biological data with personal information on a European scale for the advancement of personalized medicine, raising legal considerations of data protection under the EU General Data Protection Regulation (GDPR)…(More)”.

Survey of attitudes in a Danish public towards reuse of health data

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Paper by Lea Skovgaard et al: “Everyday clinical care generates vast amounts of digital data. A broad range of actors are interested in reusing these data for various purposes. Such reuse of health data could support medical research, healthcare planning, technological innovation, and lead to increased financial revenue. Yet, reuse also raises questions about what data subjects think about the use of health data for various different purposes. Based on a survey with 1071 respondents conducted in 2021 in Denmark, this article explores attitudes to health data reuse. Denmark is renowned for its advanced integration of data infrastructures, facilitating data reuse. This is therefore a relevant setting from which to explore public attitudes to reuse, both as authorities around the globe are currently working to facilitate data reuse opportunities, and in the light of the recent agreement on the establishment in 2024 of the European Health Data Space (EHDS) within the European Union (EU). Our study suggests that there are certain forms of health data reuse—namely transnational data sharing, commercial involvement, and use of data as national economic assets—which risk undermining public support for health data reuse. However, some of the purposes that the EHDS is supposed to facilitate are these three controversial purposes. Failure to address these public concerns could well challenge the long-term legitimacy and sustainability of the data infrastructures currently under construction…(More)”