How AI Could Help the Public Sector

Curated on Posted on by Stefaan Verhulst

Emma Martinho-Truswell in the Harvard Business Review: “A public school teacher grading papers faster is a small example of the wide-ranging benefits that artificial intelligence could bring to the public sector. A.I could be used to make government agencies more efficient, to improve the job satisfaction of public servants, and to increase the quality of services offered. Talent and motivation are wasted doing routine tasks when they could be doing more creative ones.

Applications of artificial intelligence to the public sector are broad and growing, with early experiments taking place around the world. In addition to education, public servants are using AI to help them make welfare payments and immigration decisions, detect fraud, plan new infrastructure projects, answer citizen queries, adjudicate bail hearings, triage health care cases, and establish drone paths.  The decisions we are making now will shape the impact of artificial intelligence on these and other government functions. Which tasks will be handed over to machines? And how should governments spend the labor time saved by artificial intelligence?

So far, the most promising applications of artificial intelligence use machine learning, in which a computer program learns and improves its own answers to a question by creating and iterating algorithms from a collection of data. This data is often in enormous quantities and from many sources, and a machine learning algorithm can find new connections among data that humans might not have expected. IBM’s Watson, for example, is a treatment recommendation-bot, sometimes finding treatments that human doctors might not have considered or known about.

Machine learning program may be better, cheaper, faster, or more accurate than humans at tasks that involve lots of data, complicated calculations, or repetitive tasks with clear rules. Those in public service, and in many other big organizations, may recognize part of their job in that description. The very fact that government workers are often following a set of rules — a policy or set of procedures — already presents many opportunities for automation.

To be useful, a machine learning program does not need to be better than a human in every case. In my work, we expect that much of the “low hanging fruit” of government use of machine learning will be as a first line of analysis or decision-making. Human judgment will then be critical to interpret results, manage harder cases, or hear appeals.

When the work of public servants can be done in less time, a government might reduce its staff numbers, and return money saved to taxpayers — and I am sure that some governments will pursue that option. But it’s not necessarily the one I would recommend. Governments could instead choose to invest in the quality of its services. They can re-employ workers’ time towards more rewarding work that requires lateral thinking, empathy, and creativity — all things at which humans continue to outperform even the most sophisticated AI program….(More)”.

Advanced Design for the Public Sector

Curated on Posted on by Stefaan Verhulst

Essay by Kristofer Kelly-Frere & Jonathan Veale: “…It might surprise some, but it is now common for governments across Canada to employ in-house designers to work on very complex and public issues.

There are design teams giving shape to experiences, services, processes, programs, infrastructure and policies. The Alberta CoLab, the Ontario Digital Service, BC’s Government Digital Experience Division, the Canadian Digital Service, Calgary’s Civic Innovation YYC, and, in partnership with government,MaRS Solutions Lab stand out. The Government of Nova Scotia recently launched the NS CoLab. There are many, many more. Perhaps hundreds.

Design-thinking. Service Design. Systemic Design. Strategic Design. They are part of the same story. Connected by their ability to focus and shape a transformation of some kind. Each is an advanced form of design oriented directly at humanizing legacy systems — massive services built by a culture that increasingly appears out-of-sorts with our world. We don’t need a new design pantheon, we need a unifying force.

We have no shortage of systems that require reform. And no shortage of challenges. Among them, the inability to assemble a common understanding of the problems in the first place, and then a lack of agency over these unwieldy systems. We have fanatics and nativists who believe in simple, regressive and violent solutions. We have a social economy that elevates these marginal voices. We have well-vested interests who benefit from maintaining the status quo and who lack actionable migration paths to new models. The median public may no longer see themselves in liberal democracy. Populism and dogmatism is rampant. The government, in some spheres, is not credible or trusted.

The traditional designer’s niche is narrowing at the same time government itself is becoming fragile. It is already cliche to point out that private wealth and resources allow broad segments of the population to “opt out.” This is quite apparent at the municipal level where privatized sources of security, water, fire protection and even sidewalks effectively produce private shadow governments. Scaling up, the most wealthy may simply purchase residency or citizenship or invest in emerging nation states. Without re-invention this erosion will continue. At the same time artificial intelligence, machine learning and automation are already displacing frontline design and creative work. This is the opportunity: Building systems awareness and agency on the foundations of craft and empathy that are core to human centered design. Time is of the essence. Transitions between one era to the next are historically tumultuous times. Moreover, these changes proceed faster than expected and in unexpected directions….(More).

Using new data sources for policymaking

Curated on Posted on by Stefaan Verhulst

Technical report by the Joint Research Centre (JRC) of the European Commission: “… synthesises the results of our work on using new data sources for policy-making. It reflects a recent shift from more general considerations in the area of Big Data to a more dedicated investigation of Citizen Science, and it summarizes the state of play. With this contribution, we start promoting Citizen Science as an integral component of public participation in policy in Europe.

The particular need to focus on the citizen dimension emerged due to (i) the increasing interest in the topic from policy Directorate-Generals (DGs) of the European Commission (EC); (ii) the considerable socio-economic impact policy making has on citizens’ life and society as a whole; and (iii) the clear potentiality of citizens’ contributions to increase the relevance of policy making and the effectiveness of policies when addressing societal challenges.

We explicitly concentrate on Citizen Science (or public participation in scientific research) as a way to engage people in practical work, and to develop a mutual understanding between the participants from civil society, research institutions and the public sector by working together on a topic that is of common interest.

Acknowledging this new priority, this report concentrates on the topic of Citizen Science and presents already ongoing collaborations and recent achievements. The presented work particularly addresses environment-related policies, Open Science and aspects of Better Regulation. We then introduce the six phases of the ‘cyclic value chain of Citizen Science’ as a concept to frame citizen engagement in science for policy. We use this structure in order to detail the benefits and challenges of existing approaches – building on the lessons that we learned so far from our own practical work and thanks to the knowledge exchange from third parties. After outlining additional related policy areas, we sketch the future work that is required in order to overcome the identified challenges, and translate them into actions for ourselves and our partners.

Next steps include the following:

 Develop a robust methodology for data collection, analysis and use of Citizen Science for EU policy;

 Provide a platform as an enabling framework for applying this methodology to different policy areas, including the provision of best practices;

 Offer guidelines for policy DGs in order to promote the use of Citizen Science for policy in Europe;

 Experiment and evaluate possibilities of overarching methodologies for citizen engagement in science and policy, and their case specifics; and

 Continue to advance interoperability and knowledge sharing between currently disconnected communities of practise. …(More)”.

Algorithms can deliver public services, too

Curated on Posted on by Stefaan Verhulst

Diane Coyle in the Financial Times: “As economists have been pointing out for a while, the combination of always-online smartphones and matching algorithms (of the kind pioneered by Nobel Prize-winning economist Richard Thaler and others) reduces the transaction costs involved in economic exchanges. As Ronald Coase argued, transaction costs, because they limit the scope of exchanges in the market, help explain why companies exist. Where those costs are high, it is more efficient to keep the activities inside an organisation. The relevant costs are informational. But in dense urban markets the new technologies make it easy and fast to match up the two sides of a desired exchange, such as a would-be passenger and a would-be driver for a specific journey. This can expand the market (as well as substituting for existing forms of transport such as taxis and buses). It becomes viable to serve previously under-served areas, or for people to make journeys they previously could not have afforded. In principle all parties (customers, drivers and platform) can share the benefits.

Making more efficient use of assets such as cars is nice, but the economic gains come from matching demand with supply in contexts where there are no or few economies of scale — such as conveying a passenger or a patient from A to B, or providing a night’s accommodation in a specific place.

Rather than being seen as a threat to public services, the new technologies should be taken as a compelling opportunity to deliver inherently unscalable services efficiently, especially given the fiscal squeeze so many authorities are facing. Public transport is one opportunity. How else could cash-strapped transportation authorities even hope to provide a universal service on less busy routes? It is hard to see why they should not make contractual arrangements with private providers. Nor is there any good economic reason they could not adopt the algorithmic matching model themselves, although the organisational effort might defeat many.

However, in ageing societies the big prize will be organising other services such as adult social care this way. These are inherently person to person and there are few economies of scale. The financial pressures on governments in delivering care are only going to grow. Adopting a more efficient model for matching demand and supply is so obvious a possible solution that pilot schemes are under way in several cities — both public sector-led and private start-ups. In fact, if public authorities do not try new models, the private sector will certainly fill the gap….(More)”.

Factors Influencing Decisions about Crowdsourcing in the Public Sector: A Literature Review

Curated on Posted on by Stefaan Verhulst

Paper by Regina Lenart‑Gansiniec: “Crowdsourcing is a relatively new notion, nonetheless raising more and more interest with researchers. In short, it means selection of functions which until present have been performed by employees and transferring them, in the form of an open on‑line call, to an undefined virtual community. In economic practice it has become amegatrend, which drives innovations, collaboration in the field of scientific research, business, or society. It is reached by more and more organisations, for instance considering its potential business value (Rouse 2010; Whitla 2009).

The first paper dedicated to crowdsourcing appeared relatively recently, in 2006 thanks to J. Howe’s article entitled:“The Rise of Crowdsourcing”. Although crowdsourcing is more and more the subject of scientific research, one may note in the literature many ambiguities, which result from proliferation of various research approaches and perspectives. Therefore, this may lead to many misunderstandings (Hopkins, 2011). This especially concerns the key aspects and factors, which have an impact on making decisions about crowdsourcing by organisations, particularly the public ones.

The aim of this article is identification of the factors that influence making decisions about implementing crowdsourcing by public organisations in their activity, in particular municipal offices in Poland. The article is of a theoretical and review nature. Searching for the answer to this question, a literature review was conducted and an analysis of crowdsourcing initiatives used by self‑government units in Poland was made….(More)”.

Solving Public Problems with Data

Curated on Posted on by Stefaan Verhulst

Dinorah Cantú-Pedraza and Sam DeJohn at The GovLab: “….To serve the goal of more data-driven and evidence-based governing,  The GovLab at NYU Tandon School of Engineering this week launched “Solving Public Problems with Data,” a new online course developed with support from the Laura and John Arnold Foundation.

This online lecture series helps those working for the public sector, or simply in the public interest, learn to use data to improve decision-making. Through real-world examples and case studies — captured in 10 video lectures from leading experts in the field — the new course outlines the fundamental principles of data science and explores ways practitioners can develop a data analytical mindset. Lectures in the series include:

  1. Introduction to evidence-based decision-making  (Quentin Palfrey, formerly of MIT)
  2. Data analytical thinking and methods, Part I (Julia Lane, NYU)
  3. Machine learning (Gideon Mann, Bloomberg LP)
  4. Discovering and collecting data (Carter Hewgley, Johns Hopkins University)
  5. Platforms and where to store data (Arnaud Sahuguet, Cornell Tech)
  6. Data analytical thinking and methods, Part II (Daniel Goroff, Alfred P. Sloan Foundation)
  7. Barriers to building a data practice (Beth Blauer, Johns Hopkins University and GovEx)
  8. Data collaboratives (Stefaan G. Verhulst, The GovLab)
  9. Strengthening a data analytic culture (Amen Ra Mashariki, ESRI)
  10. Data governance and sharing (Beth Simone Noveck, NYU Tandon/The GovLab)

The goal of the lecture series is to enable participants to define and leverage the value of data to achieve improved outcomes and equities, reduced cost and increased efficiency in how public policies and services are created. No prior experience with computer science or statistics is necessary or assumed. In fact, the course is designed precisely to serve public professionals seeking an introduction to data science….(More)”.

Lights on the Shadows of Public Procurement: Transparency in government contracting as an antidote to corruption?

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Report by Agnes Czibik, Mihaly Fazekas, Monika Bauhr and  Jenny de Fine Licht for Digiwhist: “Transparency is widely promoted as an essential condition for good governance, and as an effective tool against public sector corruption more specifically. Although the empirical evidence on the impact of transparency on corruption is growing, empirical evidence remains mixed. Recent critique holds that a main reason for the lack of robust empirical evidence is that both conceptualization and available measures of government transparency are broad and sometimes imprecise, and that the concepts of transparency are often far removed from the type of information that is relevant to assess government performance.

This paper seeks to develop a more precise conceptualization and measure of transparency that is actionable for the stakeholders of government decisions. The paper uses newly collected data of more than 4 million public procurement contracts between 2006-2015 to investigate the impact of transparency on high-level corruption risks in public procurement across Europe. We find a strong negative impact of overall tender transparency on corruption risks. The results also show that exante transparency, i.e. transparency before the contract is awarded, has a stronger effect on corruption risks than ex-post transparency, i.e. the availability of information after the contract has been awarded to a bidder. This suggest that internal transparency, or transparency first and foremost directed to provide information to the parties involved in the bidding process rather than to outside observers, is the main condition for wider public accountability to emerge. However, the effectiveness of this type of transparency is strengthened in contexts where there is also a wider societal demand for reduced corruption. In sum, our results suggest that transparency can reduce corruption risks if the information is both relevant to inside observers and actionable….(More)”.

No risk, no innovation: the double-bind for the public sector

Curated on Posted on by Stefaan Verhulst

Apolitical: “The political incentives to risk public money are non-existent – it’s too easy to see the short-term political consequences of initiatives gone wrong and debate whether taxpayers’ money is going down the drain. Public money is to be spent according to rules and regulations.”

This is how Jon Simonsson, Head of Innovation, Research and Capital at Sweden’s Ministry for Enterprise and Innovation, sees the potential for public servants to take risks. You may think that someone in Simonsson’s line of work – government innovation – would assume a more entrepreneurial mindset, but he’s hardly alone….

Government incentives for risk, meanwhile, don’t really exist. If you pull off a major improvement in service delivery, you don’t get a bump in compensation or promoted faster. It can feel really scary because any time you take a risk, you know that if you fail you’ll deal with criticism from the public,” said Reed.

Reed believes that the best way for governments to champion innovation is for them to institute programs and spaces designated for experimentation. San Francisco does this with several projects designed for collaboration between startups and government employees, like Startup in Residence, through which public agencies work with entrepreneurs. The startup employees give city officials a fresh perspective on long-standing civic problems, and help them prototype and user-test solutions. “[The government] tells public servants that this is sanctioned risk, and they’ll have moral support,” said Reed.

The City of West Hollywood, in Los Angeles, takes a similar approach. It recently instituted a two-person innovation division to act as consultants for its entire staff.

“Working with an innovation lab challenges your assumptions. That journey can be confronting and quite challenging to many people”

“I think government has a responsibility to take risks – we need to cultivate a culture of innovation, and sometimes that means spending money on projects that support staff ideas,” said Kate Mayerson, the city’s Innovation Analyst. “There’s something a little magical here: leadership that supports innovation and risk-taking from the top down.”…(More)”.

Government 3.0 – Next Generation Government Technology Infrastructure and Services

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Book edited by Adegboyega  Ojo and Jeremy Millard: “Historically, technological change has had significant effect on the locus of administrative activity, cost of carrying out administrative tasks, the skill sets needed by officials to effectively function, rules and regulations, and the types of interactions citizens have with their public authorities. Next generation Public Sector Innovation will be  “Government 3.0” powered by innovations related to Open and big data, administrative and business process management, Internet-of-Things and blockchains for public sector innovation to drive improvements in service delivery, decision and policy making and resource management. This book provides fresh insights into this transformation while also examining possible negative side effects of the increasing ope

nness of governments through the adoption of these new innovations. The goal is for technology policy makers to engage with the visions of Government 3.0 . Researchers should be able to critically examine some of the innovations described in the book as the basis for developing research agendas related to challenges associated with the adoption and use of some of the associated technologies.  The book serves as a rich source of materials from leading experts in the field that enables Public administration practitioners to better understand how these new technologies impact traditional public administration paradigms. The book is suitable for graduate courses in Public Sector Innovation, Innovation in Public Administration, E-Government and Information Systems. Public sector technology policy makers, e-government, information systems and public administration researchers and practitioners should all benefit from reading this book….(More).”

Growing the artificial intelligence industry in the UK

Curated on Posted on by Stefaan Verhulst

Summary from an independent review, carried out by Professor Dame Wendy Hall and Jérôme Pesenti: “Increased use of Artificial Intelligence (AI) can bring major social and economic benefits to the UK. With AI, computers can analyse and learn from information at higher accuracy and speed than humans can. AI offers massive gains in efficiency and performance to most or all industry sectors, from drug discovery to logistics. AI is software that can be integrated into existing processes, improving them, scaling them, and reducing their costs, by making or suggesting more accurate decisions through better use of information.

It has been estimated that AI could add an additional USD $814 billion (£630bn) to the UK economy by 2035, increasing the annual growth rate of GVA from 2.5 to 3.9%.

Our vision is for the UK to become the best place in the world for businesses developing and deploying AI to start, grow and thrive, to realise all the benefits the technology offers….

Key factors have combined to increase the capability of AI in recent years, in particular:

  • New and larger volumes of data
  • Supply of experts with the specific high level skills
  • Availability of increasingly powerful computing capacity. The barriers to achieving performance have fallen significantly, and continue to fall.

To continue developing and applying AI, the UK will need to increase ease of access to data in a wider range of sectors. This Review recommends:

  • Development of data trusts, to improve trust and ease around sharing data
  • Making more research data machine readable
  • Supporting text and data mining as a standard and essential tool for research.

Skilled experts are needed to develop AI, and they are in short supply. To develop more AI, the UK will need a larger workforce with deep AI expertise, and more development of lower level skills to work with AI. …

Increasing uptake of AI means increasing demand as well as supply through a better understanding of what AI can do and where it could be applied. This review recommends:

  • An AI Council to promote growth and coordination in the sector
  • Guidance on how to explain decisions and processes enabled by AI
  • Support for export and inward investment
  • Guidance on successfully applying AI to drive improvements in industry
  • A programme to support public sector use of AI
  • Funded challenges around data held by public organisations.

Our work has indicated that action in these areas could deliver a step-change improvement in growth of UK AI. This report makes the 18 recommendations listed in full below, which describe how Government, industry and academia should work together to keep the UK among the world leaders in AI…(More)”