Article by Tara Zimmerman: “As social media, online relationships, and perceived social expectations on platforms such as Facebook play a greater role in people’s lives, a new phenomenon has emerged: social noise. Social noise is the influence of personal and relational factors on information received, which can confuse, distort, or even change the intended message. Influenced by social noise, people are likely to moderate their response to information based on cues regarding what behavior is acceptable or desirable within their social network. This may be done consciously or unconsciously as individuals strive to present themselves in ways that increase their social capital. For example, this might be seen as liking or sharing information posted by a friend or family member as a show of support despite having no strong feelings toward the information itself. Similarly, someone might refrain from liking, sharing, or commenting on information they strongly agree with because they believe others in their social network would disapprove.
This study reveals that social media users’ awareness of observation by others does impact their information behavior. Efforts to craft a personal reputation, build or maintain relationships, pursue important commitments, and manage conflict all influence the observable information behavior of
social media users. As a result, observable social media information behavior may not be an accurate reflection of an individual’s true thoughts and beliefs. This is particularly interesting in light of the role social media plays in the spread of mis- and disinformation…(More)”.
Corruption Risk Forecast
About: “Starting with 2015 and building on the work of Alina Mungiu-Pippidi the European Research Centre for Anti-Corruption and State-Building (ERCAS) engaged in the development of a new generation of corruption indicators to fill the gap. This led to the creation of the Index for Public Integrity (IPI) in 2017, of the Corruption Risk Forecast in 2020 and of the T-index (de jure and de facto computer mediated government transparency) in 2021. Also since 2021 a component of the T-index (administrative transparency) is included in the IPI, whose components also offer the basis for the Corruption Risk Forecast.
This generation is different from perception indicators in a few fundamental aspects:
- Theory-grounded. Our indicators are unique because they are based on a clear theory- why corruption happens, how do countries that control corruption differ from those that don’t and what specifically is broken and should be fixed. We tested for a large variety of indicators before we decided on these ones.
- Specific. Each component is a measurement based on facts of a certain aspect of control of corruption or transparency. Read methodology to follow in detail where the data comes from and how these indicators were selected.
- Change sensitive. Except for the T-index components whose monitoring started in 2021 all other components go back in time at least 12 years and can be compared across years in the Trends menu on the Corruption Risk forecast page. No statistical process blurs the difference across years as with perception indicators. For long term trends, we flag what change is significant and what change is not. T-index components will also be comparable across the nest years to come. Furthermore, our indicators are selected to be actionable, so any significant policy intervention which has an impact is captured and reported when we renew the data.
- Comparative. You can compare every country we cover with the rest of the world to see exactly where it stands, and against its peers from the region and the income group.
- Transparent. Our T-index dataallows you to review and contribute to our work. Use the feedback form on T-index page to send input, and after checking by our team we will upgrade the codes to include your contribution. Use the feedback form on Corruption Risk forecast page to contribute to the forecast…(More)”.
The Model Is The Message
Essay by Benjamin Bratton and Blaise Agüera y Arcas: “An odd controversy appeared in the news cycle last month when a Google engineer, Blake Lemoine, was placed on leave after publicly releasing transcripts of conversations with LaMDA, a chatbot based on a Large Language Model (LLM) that he claims is conscious, sentient and a person.
Like most other observers, we do not conclude that LaMDA is conscious in the ways that Lemoine believes it to be. His inference is clearly based in motivated anthropomorphic projection. At the same time, it is also possible that these kinds of artificial intelligence (AI) are “intelligent” — and even “conscious” in some way — depending on how those terms are defined.
Still, neither of these terms can be very useful if they are defined in strongly anthropocentric ways. An AI may also be one and not the other, and it may be useful to distinguish sentience from both intelligence and consciousness. For example, an AI may be genuinely intelligent in some way but only sentient in the restrictive sense of sensing and acting deliberately on external information. Perhaps the real lesson for philosophy of AI is that reality has outpaced the available language to parse what is already at hand. A more precise vocabulary is essential.
AI and the philosophy of AI have deeply intertwined histories, each bending the other in uneven ways. Just like core AI research, the philosophy of AI goes through phases. Sometimes it is content to apply philosophy (“what would Kant say about driverless cars?”) and sometimes it is energized to invent new concepts and terms to make sense of technologies before, during and after their emergence. Today, we need more of the latter.
We need more specific and creative language that can cut the knots around terms like “sentience,” “ethics,” “intelligence,” and even “artificial,” in order to name and measure what is already here and orient what is to come. Without this, confusion ensues — for example, the cultural split between those eager to speculate on the sentience of rocks and rivers yet dismiss AI as corporate PR vs. those who think their chatbots are persons because all possible intelligence is humanlike in form and appearance. This is a poor substitute for viable, creative foresight. The curious case of synthetic language — language intelligently produced or interpreted by machines — is exemplary of what is wrong with present approaches, but also demonstrative of what alternatives are possible…(More)”.
Artificial Intelligence in the City: Building Civic Engagement and Public Trust
Collection of essays edited by Ana Brandusescu, Ana, and Jess Reia: “After navigating various challenging policy and regulatory contexts over the years, in different regions, we joined efforts to create a space that offers possibilities for engagement focused on the expertise, experiences and hopes to shape the future of technology in urban areas. The AI in the City project emerged as an opportunity to connect people, organizations, and resources in the networks we built over the last decade of work on research and advocacy in tech policy. Sharing non-Western and Western perspectives from five continents, the contributors questioned, challenged, and envisioned ways public trust and meaningful civic engagement can flourish and persist as data and AI become increasingly pervasive in our lives. This collection of essays brings together a group of multidisciplinary scholars, activists, and practitioners working on a diverse range of initiatives to map strategies going forward. Divided into five parts, the collection brings into focus: 1) Meaningful engagement and public participation; 2) Addressing inequalities and building trust; 3) Public and private boundaries in tech policy; 4) Legal perspectives and mechanisms for accountability; and 5) New directions for local and urban governance. The focus on civil society and academia was deliberate: a way to listen to and learn with people who have dedicated many years to public interest advocacy, governance and policy that represents the interests of their communities…(More)”.
14 tech-based innovations tackle youth mental health challenges
Blog by Elisha London and Anna Huber: “Depression, anxiety and behavioural conditions are the leading cause of illness for young people and suicide is the fourth most prevalent cause of death amongst 15- to 19-year-olds. Meanwhile, around 50 per cent of mental health conditions begin by the age 14 and 75 per cent by age 24. So, if youth mental health challenges and their environmental factors aren’t addressed, they extend into adulthood. Conversely, having good mental health means being better able to cope, connect and function, leading to more fulfilling and productive lives…
The Uplink Youth Mental Health Challenge by the World Economic Forum sought to identify some of the leading innovations around the world working to address these transformational needs, especially those led by young people themselves….
Here are the top 14 innovators selected:
1. Attensi and Dr. Raknes have developed the learning simulation Helping Hand, with the aim of preventing mental health disorders in adolescents. The game takes players through a series of life-like scenarios to reinforce positive decision-making, facilitate talking about feelings and thoughts, helping others master challenges and asking for help when needed.
2. Neolth Inc. offers a range of activities to help teens build coping skills and learn about mental health. Upon sign up, its proprietary algorithm matches teens with content personalized for their health needs, such as educational videos by clinicians and stigma-reducing content about lived experiences by teens.
3. Onkout connects a culturally relevant and unique trauma-informed, collective mental health peer support program to a virtual business training program, and the financial tools to improve young people’s lives. It supports young people in conflict-affected countries to be able to access services that are currently not available
4. Opa Mind has developed a “Voice Driven” support platform for people who struggle with emotional & mental health pressures. Opa Mind’s voice input system can listen and display various emotional based metrics, vocal biomarkers and supports, enabling individuals to undertake actionable follow-up steps in order to improve health and wellbeing.
5. OPTT, together with Curatio, offer an online psychotherapy tool to provide a technology-embedded, peer-to-peer social network for improved health outcomes. They allow mental wellness content producers, mental health teams, local health providers, and communities to work together to offer solutions proactively to their community members.
6. Renewal International Trust developed Positive Konnections (PK), a mobile application with a mental health intervention for young people with HIV that is designed to counter effects of stigma and help them access services privately or anonymously. The PK model uses creative narrative therapy techniques delivered on an accessible, youth-friendly platform…(More)”
The People Versus The Algorithm: Stakeholders and AI Accountability
Paper by Jbid Arsenyan and Julia Roloff: “As artificial intelligence (AI) applications are used for a wide range of tasks, the question about who is responsible for detecting and remediating problems caused by AI applications remains disputed. We argue that responsibility attributions proposed by management scholars fail to enable a practical solution as two aspects are overlooked: the difficulty to design a complex algorithm that does not produce adverse outcomes, and the conflict of interest inherited in some AI applications by design as proprietors and users employ the application for different purposes. In this conceptual paper, we argue that effective accountability can only be delivered through solutions that enable stakeholders to employ their collective intelligence effectively in compiling problem reports and analyze problem patterns. This allows stakeholders, including governments, to hold providers of AI applications accountable, and ensure that appropriate corrections are carried out in a timely manner…(More)”.
Mobile Big Data for Cities: Urban climate resilience strategies for low- and middle-income countries
GSMA Report: “Cities in low- and middle-income countries (LMICs) are increasingly vulnerable to the impacts of climate change, including rising sea levels and storm surges, heat stress, extreme precipitation, inland and coastal flooding and landslides. The physical effects of climate change have disrupted supply chains, led to lost productivity from health issues and incurred costs associated with rebuilding or repairing physical assets, such as buildings and transport infrastructure.
Resulting from the adverse effects of climate change, municipal governments and systems often lack the adaptive capacity or resources to keep up. Hence, the adaptative capacity of cities can be enhanced by corresponding to more comprehensive and real-time data. Such data will give municipal agencies the ability to watch events as they unfold, understand how demand patterns are changing and respond with faster and lower-cost solutions. This provides a solid basis for innovative data sources, such as mobile big data (MBD), to help strengthen urban climate resilience.
This study highlights the potential value of using mobile big data (MBD) in preparing for and responding to climate-related disasters in cities. In line with the “3As” of urban climate resilience, a framework adopted by the GSMA Mobile for Development programme, this study examines how MBD could help cities and their populations adapt to multiple long-term challenges brought about by climate change, anticipate climate hazards or events and/or absorb (face, manage and recover from) adverse conditions, emergencies or disasters…(More)”.
The Secret Language of Maps
Book by Carissa Carter: “Maps aren’t just geographic, they are also infographic and include all types of frameworks and diagrams. Any figure that sorts data visually and presents it spatially is a map. Maps are ways of organizing information and figuring out what’s important. Even stories can be mapped! The Secret Language of Maps provides a simple framework to deconstruct existing maps and then shows you how to create your own.
An embedded mystery story about a woman who investigates the disappearance of an old high school friend illustrates how to use different maps to make sense of all types of information. Colorful illustrations bring the story to life and demonstrate how the fictional character’s collection of data, properly organized and “mapped,” leads her to solve the mystery of her friend’s disappearance.
You’ll learn how to gather data, organize it, and present it to an audience. You’ll also learn how to view the many maps that swirl around our daily lives with a critical eye, aware of the forces that are in play for every creator…(More)”.
How Three False Starts Stifle Open Social Science
Article by Patrick Dunleavy: “Open social science is new, and like any beginner is still finding its way. However, to a large extent we are still operating in the shadow of open science (OS) in the Science, technology, engineering, mathematics, and medicine, or STEMM, disciplines. Nearly a decade ago an influential Royal Society report argued:
‘Open science is often effective in stimulating scientific discovery, [and] it may also help to deter, detect and stamp out bad science. Openness facilitates a systemic integrity that is conducive to early identification of error, malpractice and fraud, and therefore deters them. But this kind of transparency only works when openness meets standards of intelligibility and assessability – where there is intelligent openness’.
More recently, the Turing Way project defined open science far more broadly as a range of measures encouraging reproducibility, replication, robustness, and the generalisability of research. Alongside CIVICA researchers we have put forward an agenda for progressing open social science in line with these ambitions. Yet for open social science to take root it must develop an ‘intelligent’ concept of openness, one that is adapted to the wide range of concerns that our discipline group addresses, and is appropriate for the sharply varying conditions in which social research must be carried out.
This task has been made more difficult by a number of premature and partial efforts to ‘graft’ an ‘open science’ concept from STEMM disciplines onto the social sciences. Three false starts have already been made and have created misconceptions about open social science. Below, I want to show how each of the strategies may actually work to obstruct the wider development of open social science.
Bricolage – Reading across directly from STEMM
This approach sees open social science as just about picking up (not quite at random) the best-known or most discussed individual components of open science in STEMM disciplines – focusing on specific things like open access publishing, the FAIR principles for data management, replication studies, or the pre-registration of hypotheses…(More)”.
Efficient and stable data-sharing in a public transit oligopoly as a coopetitive game
Paper by Qi Liu and Joseph Y.J. Chow: “In this study, various forms of data sharing are axiomatized. A new way of studying coopetition, especially data-sharing coopetition, is proposed. The problem of the Bayesian game with signal dependence on actions is observed; and a method to handle such dependence is proposed. We focus on fixed-route transit service markets. A discrete model is first presented to analyze the data-sharing coopetition of an oligopolistic transit market when an externality effect exists. Given a fixed data sharing structure, a Bayesian game is used to capture the competition under uncertainty while a coalition formation model is used to determine the stable data-sharing decisions. A new method of composite coalition is proposed to study efficient markets. An alternative continuous model is proposed to handle large networks using simulation. We apply these models to various types of networks. Test results show that perfect information may lead to perfect selfishness. Sharing more data does not necessarily improve transit service for all groups, at least if transit operators remain non-cooperative. Service complementarity does not necessarily guarantee a grand data-sharing coalition. These results can provide insights on policy-making, like whether city authorities should enforce compulsory data-sharing along with cooperation between operators or setup a voluntary data-sharing platform…(More)”.