AI in public services will require empathy, accountability

Curated on Posted on by Stefaan Verhulst

Article by Yogesh Hirdaramani: “The Australian Government Department of the Prime Minister and Cabinet has released the first of its Long Term Insights Briefing, which focuses on how the Government can integrate artificial intelligence (AI) into public services while maintaining the trustworthiness of public service delivery.

Public servants need to remain accountable and transparent with their use of AI, continue to demonstrate empathy for the people they serve, use AI to better meet people’s needs, and build AI literacy amongst the Australian public, the report stated.

The report also cited a forthcoming study that found that Australian residents with a deeper understanding of AI are more likely to trust the Government’s use of AI in service delivery. However,more than half of survey respondents reported having little knowledge of AI.

Key takeaways

The report aims to supplement current policy work on how AI can be best governed in the public service to realise its benefits while maintaining public trust.

In the longer term, the Australian Government aims to use AI to deliver personalised services to its citizens, deliver services more efficiently and conveniently, and achieve a higher standard of care for its ageing population.

AI can help public servants achieve these goals through automating processes, improving service processing and response time, and providing AI-enabled interfaces which users can engage with, such as chatbots and virtual assistants.

However, AI can also lead to unfair or unintended outcomes due to bias in training data or hallucinations, the report noted.

According to the report, the trustworthy use of AI will require public servants to:

  1. Demonstrate integrity by remaining accountable for AI outcomes and transparent about AI use
  2. Demonstrate empathy by offering face-to-face services for those with greater vulnerabilities 
  3. Use AI in ways that improve service delivery for end-users
  4. Build internal skills and systems to implement AI, while educating the public on the impact of AI

The Australian Taxation Office currently uses AI to identify high-risk business activity statements to determine whether refunds can be issued or if further review is required, noted the report. Taxpayers can appeal the decision if staff decide to deny refunds…(More)”

Enhancing the European Administrative Space (ComPAct)

Curated on Posted on by Stefaan Verhulst

European Commission: “Efficient national public administrations are critical to transform EU and national policies into reality, to implement reforms to the benefit of people and business alike, and to channel investments towards the achievement of the green and digital transition, and greater competitiveness. At the same time, national public administrations are also under an increasing pressure to deal with polycrisis and with many competing priorities. 

For the first time, with the ComPAct, the Commission is proposing a strategic set of actions not only to support the public administrations in the Member States to become more resilient, innovative and skilled, but also to strengthen the administrative cooperation between them, thereby allowing to close existing gaps in policies and services at European level.

With the ComPAct, the Commission aims to enhance the European Administrative Space by promoting a common set of overarching principles underpinning the quality of public administration and reinforcing its support for the administrative modernisation of the Member States. The ComPAct will help Member States address the EU Skills Agenda and the actions under the European Year of Skills, deliver on the targets of the Digital Decade to have 100% of key public services accessible online by 2030, and shape the conditions for the economies and societies to deliver on the ambitious 2030 climate and energy targets. The ComPAct will also help EU enlargement countries on their path to building better public administrations…(More)”.

Data Equity: Foundational Concepts for Generative AI

Curated on Posted on by Stefaan Verhulst

WEF Report: “This briefing paper focuses on data equity within foundation models, both in terms of the impact of Generative AI (genAI) on society and on the further development of genAI tools.

GenAI promises immense potential to drive digital and social innovation, such as improving efficiency, enhancing creativity and augmenting existing data. GenAI has the potential to democratize access and usage of technologies. However, left unchecked, it could deepen inequities. With the advent of genAI significantly increasing the rate at which AI is deployed and developed, exploring frameworks for data equity is more urgent than ever.

The goals of the briefing paper are threefold: to establish a shared vocabulary to facilitate collaboration and dialogue; to scope initial concerns to establish a framework for inquiry on which stakeholders can focus; and to shape future development of promising technologies.

The paper represents a first step in exploring and promoting data equity in the context of genAI. The proposed definitions, framework and recommendations are intended to proactively shape the development of promising genAI technologies…(More)”.

Artificial intelligence in government: Concepts, standards, and a unified framework

Curated on Posted on by Stefaan Verhulst

Paper by Vincent J. Straub, Deborah Morgan, Jonathan Bright, Helen Margetts: “Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society. Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. Finally, we put these concepts to work by using them as dimensions in a conceptual typology of AI-GOV and connecting each with emerging AI technical measurement standards to encourage operationalization, foster cross-disciplinary dialogue, and stimulate debate among those aiming to rethink government with AI…(More)”.

A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data

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Report by Andrés F. Barrientos, Aaron R. Williams, Joshua Snoke, Claire McKay Bowen: “Federal administrative data, such as tax data, are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few individuals. An alternative to sharing microlevel data is to allow individuals to query statistics without directly accessing the confidential data. This paper studies the feasibility of using differentially private (DP) methods to make certain queries while preserving privacy. We also include new methodological adaptations to existing DP regression methods for using new data types and returning standard error estimates. We define feasibility as the impact of DP methods on analyses for making public policy decisions and the queries accuracy according to several utility metrics. We evaluate the methods using Internal Revenue Service data and public-use Current Population Survey data and identify how specific data features might challenge some of these methods. Our findings show that DP methods are feasible for simple, univariate statistics but struggle to produce accurate regression estimates and confidence intervals. To the best of our knowledge, this is the first comprehensive statistical study of DP regression methodology on real, complex datasets, and the findings have significant implications for the direction of a growing research field and public policy…(More)”.

Governing Urban Data for the Public Interest

Curated on Posted on by Stefaan Verhulst

Report by The New Hanse: “…This report represents the culmination of our efforts and offers actionable guidelines for European cities seeking to harness the power of data for the public good.

The key recommendations outlined in the report are:

1. Shift the Paradigm towards Democratic Control of Data: Advocate for a policy that defaults to making urban data accessible, requiring private data holders to share in the public interest.

2. Provide Legal Clarity in a Dynamic Environment: Address legal uncertainties by balancing privacy and confidentiality needs with the public interest in data accessibility, working collaboratively with relevant authorities at national and EU level.

3. Build a Data Commons Repository of Use cases: Streamline data sharing efforts by establishing a standardised use case repository with common technical frameworks, procedures, and contracts.

4. Set up an Urban Data Intermediary for the Public Interest: Institutionalise data sharing, by building urban data intermediaries to address complexities, following principles of public purpose, transparency, and accountability.

5. Learning from the Hamburg Experiment and Scale it across Europe: Embrace experimentation as a vital step, even if outcomes are uncertain, to adapt processes for future innovations. Experiments at the local level can inform policy and scale nationally and across Europe…(More)”.

Data collaboration to enable the EU Green Deal

Curated on Posted on by Stefaan Verhulst

Article by Justine Gangneux: “In the fight against climate change, local authorities are increasingly turning to cross-sectoral data sharing as a game-changing strategy.

This collaborative approach empowers cities and communities to harness a wealth of data from diverse sources, enabling them to pinpoint emission hotspots, tailor policies for maximum impact, and allocate resources wisely.

Data can also strengthen climate resilience by engaging local communities and facilitating real-time progress tracking…

In recent years, more and more local data initiatives aimed at tackling climate change have emerged, spanning from urban planning to mobility, adaptation and energy management.

Such is the case of Porto’s CityCatalyst – the project put five demonstrators in place to showcase smart cities infrastructure and develop data standards and models, contributing to the efficient and integrated management of urban flows…

In Latvia, Riga is also exploring data solutions such as visualisations, aggregation or analytics, as part of the Positive Energy District strategy.  Driven by the national Energy Efficiency Law, the city is developing a project to monitor energy consumption based on building utility use data (heat, electricity, gas, or water), customer and billing data, and Internet of Things smart metre data from individual buildings…

As these examples show, it is not just public data that holds the key; private sector data, from utilities as energy or water, to telecoms, offers cities valuable insights in their efforts to tackle climate change…(More)”.

Policy brief: Generative AI

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Policy Brief by Ann Kristin Glenster, and Sam Gilbert: “The rapid rollout of generative AI models, and public attention to Open AI’s ChatGPT, has raised concerns about AI’s impact on the economy and society. In the UK, policy-makers are looking to large language models and other so-called foundation models as ways to potentially improve economic productivity.

This policy brief outlines which policy levers could support those goals. The authors argue that the UK should pursue becoming a global leader in applying generative AI to the economy. Rather than use public support for building new foundation models, the UK could support the growing ecosystem of startups that develop new applications for these models, creating new products and services.

This policy brief answers three key questions:

  1. What policy infrastructure and social capacity does the UK need to lead and manage deployment of responsible generative AI (over the long term)?
  2. What national capability does the UK need for large-scale AI systems in the short- and medium-term?
  3. What governance capacity does the UK need to deal with fast moving technologies, in which large uncertainties are a feature, not a bug?…(More)”.

Setting Democratic Ground Rules for AI: Civil Society Strategies

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Report by Beth Kerley: “…analyzes priorities, challenges, and promising civil society strategies for advancing democratic approaches to governing artificial intelligence (AI). The report is based on conversations from a private Forum workshop in Buenos Aires, Argentina that brought together Latin American and global researchers and civil society practitioners.

With recent leaps in the development of AI, we are experiencing a seismic shift in the balance of power between people and governments, posing new challenges to democratic principles such as privacy, transparency, and non-discrimination. We know that AI will shape the political world we inhabit–but how can we ensure that democratic norms and institutions shape the trajectory of AI?

Drawing on global civil society perspectives, this report surveys what stakeholders need to know about AI systems and the human relationships behind them. It delves into the obstacles– from misleading narratives to government opacity to gaps in technical expertise–that hinder democratic engagement on AI governance, and explores how new thinking, new institutions, and new collaborations can better equip societies to set democratic ground rules for AI technologies…(More)”.

Everybody is looking into the Future! 

Curated on Posted on by Stefaan Verhulst

Report as part of the “Anticipation and monitoring of emerging technologies and disruptive innovation” (ANTICIPINNOV) project, a collaboration between the European Commission Joint Research Centre with the European Innovation Council (EIC): “Growing volatility, uncertainty, complexity and ambiguity, present leading challenges in policy-making nowadays. Anticipatory thinking and foresight are of utmost importance to help explore trends, risks, emerging issues, and their potential implications and opportunities in order to draw useful insights for strategic planning, policy-making and preparedness.
The findings include a set of 106 signals and trends on emerging technologies and disruptive innovations across several areas of application based on a review of key reports on technology and innovation trends and signals produced by public and private entities outside of the EU institutions. Its goal is to strengthen the EIC’s strategic intelligence capacity through the use and development of anticipatory approaches that will – among other goals – support innovation funding prioritisation…(More)”.