Paper by Daron Acemoglu: “This essay discusses several potential economic, political and social costs of the current path of AI technologies. I argue that if AI continues to be deployed along its current trajectory and remains unregulated, it may produce various social, economic and political harms. These include: damaging competition, consumer privacy and consumer choice; excessively automating work, fueling inequality, inefficiently pushing down wages, and failing to improve worker productivity; and damaging political discourse, democracy’s most fundamental lifeblood. Although there is no conclusive evidence suggesting that these costs are imminent or substantial, it may be useful to understand them before they are fully realized and become harder or even impossible to reverse, precisely because of AI’s promising and wide-reaching potential. I also suggest that these costs are not inherent to the nature of AI technologies, but are related to how they are being used and developed at the moment – to empower corporations and governments against workers and citizens. As a result, efforts to limit and reverse these costs may need to rely on regulation and policies to redirect AI research. Attempts to contain them just by promoting competition may be insufficient….(More)”.
Can Gamification be Used for Spatial Energy Data Collection?
Paper by Ernst Gebetsroither-Geringer et al regarding “Experiences Gained from the Development of the HotCity Game to Collect Urban Waste Heat Sources”: Availability of reliable data is one of the most important elements for fact-based decisions. Urban planning and spatial energy planning often suffers from a lack of availability of good, validated and up-to-date data sets. Furthermore, integrated spatial and energy planning needs to incorporate new spatially distributed energy sources and understand how these sources can be used in the future to meet climate protection targets. These new energy sources can be, for example, waste heat from industrial food production, local industrial/commercial enterprises, data centers, or urban infrastructure such as tunnels and metro stations. The utilization of such waste heat sources in heating networks has been demonstrated several times, however, their proper identification in an urban environment can be challenging, especially for smaller and unconventional sources (Schmidt, 2020).
Gamification as an innovative way to collect the needed data was investigated within a national funded research project called “HotCity”. Gamification builds on the use of game mechanics in contexts that are, by nature, unrelated to the game (Deterding, 2011). Within the project the HotCity-App was developed enabling users to spatially report and evaluate different sources of waste heat. The gamification of data collection was also intended to raise awareness of waste heat and energy use on the one hand, and to facilitate the collection of data from small energy sources on the other. For the first time, the game framework is secured using a blockchain and mapped by means of a token system. The HotCity-App was tested in the Austrian cities Vienna and Graz as a proof of concept to analyse if and how the gamification approach can deliver valid results….(More)”
Contemplating the COVID crisis: what kind of inquiry do we need to learn the right lessons?
Essay by Geoff Mulgan: “Boris Johnson has announced a UK inquiry into COVID-19 to start in 2022, a parallel one is being planned in Scotland, and many more will emerge all over the world. But how should such inquiries be designed and run? What kind of inquiry can do most to mitigate or address the harms caused by the pandemic?
We’re beginning to look at this question at IPPO (the International Public Policy Observatory), including a global scan with our partners, INGSA and the Blavatnik School of Government, on how inquiries are being developed around the world, plus engagement with governments and parliaments across the UK.
It’s highly likely that the most traditional models of inquiries will be adopted – just because that’s what people at the top are used to, or because they look politically expedient. But we think it would be good to look at the options and to encourage some creativity.
The pandemic has prompted extraordinary innovation; there is no reason why inquiries should be devoid of any. Moreover, the pandemic affected every sector of life – and was far more ‘systemic’ than the kinds of issue or event addressed by typical inquiries in the past. That should be reflected in how lessons are learned.
So here are some initial thoughts on what the defaults look like, why they are likely to be inadequate, and what some alternatives might be. This article proposes the idea of a ‘whole of society’ inquiry model which has a distributed rather than centralised structure, which focuses on learning more than blame, and which can connect the thousands of organisations that have had to make so many difficult decisions throughout the crisis, and also the lived experiences of public and frontline staff. We hope that it will prompt responses, including better ideas about what kinds of inquiry will serve us best…
There are many different options for inquiries, and this is a good moment to consider them. They range from ‘truth and reconciliation’ inquiries to no-fault compensation processes to the ways industries such as airlines deal with crashes, through to academic analyses of events like the 2007/08 financial crash. They can involve representative or random samples of the public (e.g. citizens’ assemblies and juries) or just experts and officials…
The idea of a distributed inquiry is not entirely new. Colombia, for example, attempted something along these lines as part of its peace process. Many health systems use methods such as ‘collaboratives’ to organise accelerated learning. Doubtless there is much to be learned from these and other examples. For the UK in particular, it is vital there are contextually appropriate designs for the four nations as well as individual cities and regions.
As already indicated, a key is to combine sensible inquiries focused on particular sectors (e.g. what did universities do, what worked…) and make connections between them. As IPPO’s work on COVID inequalities has highlighted, the patterns are very complex but involved a huge amount of harm – captured in our ‘inequalities matrix’, below.
So, while the inquiries need to dig deep on multiple fronts and to look more like a matrix than a single question, what might connect all the inquiries would be a commitment to some common elements which would be shared:
- Facts: In each case, a precondition for learning is establishing the facts, as well as the evidence on what did or didn’t work well. This is a process closer to what evidence intermediary organisations – such as the UK’s What Works Network – do than a judicial process designed for binary judgments (guilty/not guilty). This would be helped by some systematic curation and organisation of the evidence in easily accessible forms, of the kind that IPPO is doing….(More)”
Introducing collective crisis intelligence
Blogpost by Annemarie Poorterman et al: “…It has been estimated that over 600,000 Syrians have been killed since the start of the civil war, including tens of thousands of civilians killed in airstrike attacks. Predicting where and when strikes will occur and issuing time-critical warnings enabling civilians to seek safety is an ongoing challenge. It was this problem that motivated the development of Sentry Syria, an early warning system that alerts citizens to a possible airstrike. Sentry uses acoustic sensor data, reports from on-the-ground volunteers, and open media ‘scraping’ to detect warplanes in flight. It uses historical data and AI to validate the information from these different data sources and then issues warnings to civilians 5-10 minutes in advance of a strike via social media, TV, radio and sirens. These extra minutes can be the difference between life and death.
Sentry Syria is just one example of an emerging approach in the humanitarian response we call collective crisis intelligence (CCI). CCI methods combine the collective intelligence (CI) of local community actors (e.g. volunteer plane spotters in the case of Sentry) with a wide range of additional data sources, artificial intelligence (AI) and predictive analytics to support crisis management and reduce the devastating impacts of humanitarian emergencies….(More)”
Government Lawyers: Technicians, Policy Shapers and Organisational Brakes
Paper by Philip S.C. Lewis and Linda Mulcahy: “Government lawyers have been rather neglected by scholars interested in the workings of the legal profession and the role of professional groups in contemporary society. This is surprising given the potential for them to influence the internal workings of an increasingly legalistic and centralized state. This article aims to partly fill the gap left by looking at the way that lawyers employed by the government and the administrators they work with talk about their day to day practices. It draws on the findings of a large-scale empirical study of government lawyers in seven departments, funded by the ESRC. The study was undertaken between 2002-2003 by Philip Lewis, and is reported for the first time here. By looking at lawyers in bureaucracies the interviews conducted sought to explore what government lawyers do, how they talked about their work, and what distinguished them from the administrative grade clients and colleagues they worked with….(More)”.
Enrollment algorithms are contributing to the crises of higher education
Paper by Alex Engler: “Hundreds of higher education institutions are procuring algorithms that strategically allocate scholarships to convince more students to enroll. In doing so, these enrollment management algorithms help colleges vary the cost of attendance to students’ willingness to pay, a crucial aspect of competition in the higher education market. This paper elaborates on the specific two-stage process by which these algorithms first predict how likely prospective students are to enroll, and second help decide how to disburse scholarships to convince more of those prospective students to attend the college. These algorithms are valuable to colleges for institutional planning and financial stability, as well as to help reach their preferred financial, demographic, and scholastic outcomes for the incoming student body.
Unfortunately, the widespread use of enrollment management algorithms may also be hurting students, especially due to their narrow focus on enrollment. The prevailing evidence suggests that these algorithms generally reduce the amount of scholarship funding offered to students. Further, algorithms excel at identifying a student’s exact willingness to pay, meaning they may drive enrollment while also reducing students’ chances to persist and graduate. The use of this two-step process also opens many subtle channels for algorithmic discrimination to perpetuate unfair financial aid practices. Higher education is already suffering from low graduation rates, high student debt, and stagnant inequality for racial minorities—crises that enrollment algorithms may be making worse.
This paper offers a range of recommendations to ameliorate the risks of enrollment management algorithms in higher education. Categorically, colleges should not use predicted likelihood to enroll in either the admissions process or in awarding need-based aid—these determinations should only be made based on the applicant’s merit and financial circumstances, respectively. When colleges do use algorithms to distribute scholarships, they should proceed cautiously and document their data, processes, and goals. Colleges should also examine how scholarship changes affect students’ likelihood to graduate, or whether they may deepen inequities between student populations. Colleges should also ensure an active role for humans in these processes, such as exclusively using people to evaluate application quality and hiring internal data scientists who can challenge algorithmic specifications. State policymakers should consider the expanding role of these algorithms too, and should try to create more transparency about their use in public institutions. More broadly, policymakers should consider enrollment management algorithms as a concerning symptom of pre-existing trends towards higher tuition, more debt, and reduced accessibility in higher education….(More)”.
The Future of Citizen Engagement: Rebuilding the Democratic Dialogue
Report by the Congressional Management Foundation: “The Future of Citizen Engagement: Rebuilding the Democratic Dialogue” explores the current challenges to engagement and trust between Senators and Representatives and their constituents; proposes principles for rebuilding that fundamental democratic relationship; and describes innovative practices in federal, state, local, and international venues that Congress could look to for modernizing the democratic dialogue.
The report answers the following questions:
- What factors have contributed to the deteriorating state of communications between citizens and Congress?
- What principles should guide Congress as it tries to transform its communications systems and practices from administrative transactions to substantive interactions with the People it represents?
- What models at the state and international level can Congress follow as it modernizes and rebuilds the democratic dialogue?
The findings and recommendations in this report are based on CMF’s long history of researching the relationship between Members of Congress and their constituents…(More)”.
Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access
Paper by Nathan Ratledge et al: “In many regions of the world, sparse data on key economic outcomes inhibits the development, targeting, and evaluation of public policy. We demonstrate how advancements in satellite imagery and machine learning can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show how a combination of satellite imagery and computer vision can be used to develop local-level livelihood measurements appropriate for inferring the causal impact of electricity access on livelihoods. We then show how ML-based inference techniques deliver more reliable estimates of the causal impact of electrification than traditional alternatives when applied to these data. We estimate that grid access improves village-level asset wealth in rural Uganda by 0.17 standard deviations, more than doubling the growth rate over our study period relative to untreated areas. Our results provide country-scale evidence on the impact of a key infrastructure investment, and provide a low-cost, generalizable approach to future policy evaluation in data sparse environments….(More)”.
Citizen Needs – To Be Considered
Paper by Franzisca Maas, Sara Wolf, Anna Hohm and Jörn Hurtienne outlining “Requirements for Local Civic Participation Tools. In this paper, we argue for and present an empirical study of putting citizens into focus during the early stages of designing tools for civic participation in a mid-sized German town. Drawing on Contextual and Participatory Design, we involved 105 participants by conducting interviews, using Photovoice and participating in a local neighbourhood meeting.
Together with citizens, we built an Affinity Diagram, consolidated the data and identified key insights. As a result, we present and discuss different participation identities such as Motivated Activists, Convenience Participants or Companions and a collection of citizen needs for local civic participation, e. g., personal contact is irreplaceable for motivation, trust and mutual understanding, and some citizens preferred to “stumble across” information rather than actively searching for it. We use existing participation tools to demonstrate how individual needs could be addressed. Finally, we apply our insights to an example in our local context. We conclude that if we want to build digital tools that go beyond tokenistic, top-down ways of civic participation and that treat citizens as one homogeneous group, citizens need to be part of the design process right from the start. Supplemental material can be retrieved from https://osf.io/rxd7h/….(More)”
Social welfare gains from innovation commons: Theory, evidence, and policy implications
Paper by Jason Potts, Andrew W. Torrance, Dietmar Harhoff and Eric A. von Hippel: “Innovation commons – which we define as repositories of freely-accessible, “open source” innovation-related information and data – are a very significant resource for innovating and innovation-adopting firms and individuals: Availability of free data and information reduces the innovation-specific private or open investment required to make the next innovative advance. Despite the clear social welfare value of innovation commons under many conditions, academic innovation research and innovation policymaking have to date focused almost entirely on enhancing private incentives to innovate by enabling innovators to keep some types of innovation-related information at least temporarily apart from the commons, via intellectual property rights.
In this paper, our focus is squarely on innovation commons theory, evidence, and policy implications. We first discuss the varying nature of and contents of innovation commons extant today. We summarize what is known about their functioning, their scale, the value they provide to innovators and to general social welfare, and the mechanisms by which this is accomplished. Perhaps somewhat counterintuitively, and with the important exception of major digital platform firms, we find that many who develop innovation-related information at private cost have private economic incentives to contribute their information to innovation commons for free access by free riders. We conclude with a discussion of the value of more general support for innovation commons, and how this could be provided by increased private and public investment in innovation commons “engineering”, and by specific forms of innovation policymaking to increase social welfare via enhancement of innovation commons….(More)”.