Governance of data for children’s learning in UK state schools

When I first introduced the Age Appropriate Design Code (AADC) into the Data Protection Bill in 2018, I had no idea that it may not apply to education settings. Now, a few years on, there is still some confusion. What happens if schools are working remotely: does the AADC suddenly apply? Or if a teacher uses an app or service in the classroom that they downloaded directly from the internet: does the AADC no longer apply? Why is there a difference between state and private schools, when surely all pupils need their data protected? Why is the burden disproportionately put on teachers and schools to understand the complex data processing terms set out in the terms and conditions of services that are hungry for data? And, perhaps most crucially of all, why are schools sharing intimate pupil data (wittingly and not) with commercial companies at all? This report, authored by Emma Day, starts the work of unravelling some of these questions, and in doing so identifies gaps in provision, gaps in clarity, gaps in understanding. As such, it is the first step to working out what good might look like when the education sector and schools are brought into an effective data protection regime

Lessons Learned to Improve the UX Practices in Agile Projects Involving Data Science and Process Automation

Context: User-Centered Design and Agile methodologies focus on human issues. Nevertheless, agile methodologies focus on contact with contracting customers and generating value for them. Usually, the communication between end users and the agile team is mediated by customers. However, they do not know the problems end users face in their routines. Hence, UX issues are typically identified only after the implementation, during user testing and validation. Objective: Aiming to improve the understanding and definition of the problem in agile projects, this research investigates the practices and difficulties experienced by agile teams during the development of data science and process automation projects. Also, we analyze the benefits and the teams' perceptions regarding user participation in these projects. Method: We collected data from four agile teams in an academia-industry collaboration focusing on delivering data science and process automation solutions. Therefore, we applied a carefully designed questionnaire answered by developers, scrum masters, and UX designers. In total, 18 subjects answered the questionnaire. Results: From the results, we identify practices used by the teams to define and understand the problem and to represent the solution. The practices most often used are prototypes and meetings with stakeholders. Another practice that helped the team to understand the problem was using Lean Inceptions. Also, our results present some specific issues regarding data science projects. Conclusion: We observed that end-user participation can be critical to understanding and defining the problem. They help to define elements of the domain and barriers in the implementation. We identified a need for approaches that facilitate user-team communication in data science projects and the need for more detailed requirements representations to support data science solutions

Speeding up to keep up: exploring the use of AI in the research process.

There is a long history of the science of intelligent machines and its potential to provide scientific insights have been debated since the dawn of AI. In particular, there is renewed interest in the role of AI in research and research policy as an enabler of new methods, processes, management and evaluation which is still relatively under-explored. This empirical paper explores interviews with leading scholars on the potential impact of AI on research practice and culture through deductive, thematic analysis to show the issues affecting academics and universities today. Our interviewees identify positive and negative consequences for research and researchers with respect to collective and individual use. AI is perceived as helpful with respect to information gathering and other narrow tasks, and in support of impact and interdisciplinarity. However, using AI as a way of 'speeding up-to keep up' with bureaucratic and metricised processes, may proliferate negative aspects of academic culture in that the expansion of AI in research should assist and not replace human creativity. Research into the future role of AI in the research process needs to go further to address these challenges, and ask fundamental questions about how AI might assist in providing new tools able to question the values and principles driving institutions and research processes. We argue that to do this an explicit movement of meta-research on the role of AI in research should consider the effects for research and researcher creativity. Anticipatory approaches and engagement of diverse and critical voices at policy level and across disciplines should also be considered

Information, sticky prices and macroeconomic foundations.

Macroeconomics

Is the Euro up for Grabs? Evidence from a Survey Experiment

The COVID-19 pandemic may lead to a resurgence of the euro crisis. In this context, Italy seems particularly vulnerable: support for the euro is lower than in most other eurozone countries, and a possible exit could have serious consequences for the common currency. Based on a novel survey experiment, this paper shows that the pro-euro coalition is fragile in Italy and preferences are malleable. They are heavily dependent on the perceived costs of continued membership, as a majority of Italians would opt for Italexit rather than accepting a bailout plan requiring the implementation of austerity policies. Individuals who feel they have not benefited from the euro are most likely to support exit when faced with the prospect of austerity. This suggests that, differently from Greece, where voters were determined to remain in the euro at all costs, the pro-euro coalition may crumble if Italy is exposed to harsh conditionality.Die COVID-19-Pandemie hat das Potenzial, zu einem Wiederaufleben der Eurokrise beizutragen. Italien erscheint unter diesen Umständen besonders verwundbar: Die Unterstützung für den Euro ist geringer als in den meisten anderen Ländern der Eurozone und ein möglicher Ausstieg Italiens aus dem Euro könnte schwerwiegende Folgen für die gesamte Währungsunion haben. Anhand eines neuen Umfrageexperiments zeigt dieses Papier, dass das den Euro unterstützende gesellschaftliche Bündnis in Italien brüchig ist und die Präferenzen in der italienischen Wählerschaft in hohem Maße veränderlich sind. Individuelle Präferenzen zum Euro hängen stark von den wahrgenommenen Kosten einer fortwährenden Mitgliedschaft im Euro ab. Eine Mehrheit der Italienerinnen und Italiener würde eher für einen Italexit stimmen, als ein Rettungspaket zu akzeptieren, welches die Umsetzung von Austeritätspolitik erfordern würde. Sind die Befragten mit der Aussicht auf Austeritätspolitik konfrontiert, stimmen insbesondere diejenigen für einen Ausstieg Italiens aus dem Euro, die glauben, dass der Euro ihnen nicht genutzt habe. Im Gegensatz zu Griechenland, wo Wählerinnen und Wähler entschlossen waren, zu jedem Preis im Euro zu verbleiben, zeigt dieser Befund, dass das Pro-Euro-Bündnis in Italien auseinanderfallen könnte, sollte Italien mit einer harten Austeritätspolitik konfrontiert sein.Contents 1 Introduction 2 Individual-level preferences for eurozone membership and exit 3 Framing effects on support for the euro 4 Data and methods Experiment design and dependent variable Independent variables Empirical strategy 5 Results The social support base of eurozone membership and exit Multivariate analysis of support for the euro Results from the survey experiment Heterogeneous framing effects 6 Conclusion Reference