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Register a new userMMOGs as Social Experiments: the Case of Environmental Laws
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Added by
Joost Broekens
Publication date
2008
fields
Informatics Engineering
and research's language is
English
Authors
Joost Broekens
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In this paper we argue that Massively Multiplayer Online Games (MMOGs), also known as Large Games are an interesting research tool for policy experimentation. One of the major problems with lawmaking is that testing the laws is a difficult enterprise. Here we show that the concept of an MMOG can be used to experiment with environmental laws on a large scale, provided that the MMOG is a real game, i.e., it is fun, addictive, presents challenges that last, etc.. We present a detailed game concept as an initial step.
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Youth unemployment rates are still in alerting levels for many countries, among which Italy. Direct consequences include poverty, social exclusion, and criminal behaviours, while negative impact on the future employability and wage cannot be obscured. In this study, we employ survey data together with social media data, and in particular likes on Facebook Pages, to analyse personality, moral values, but also cultural elements of the young unemployed population in Italy. Our findings show that there are small but significant differences in personality and moral values, with the unemployed males to be less agreeable while females more open to new experiences. At the same time, unemployed have a more collectivist point of view, valuing more in-group loyalty, authority, and purity foundations. Interestingly, topic modelling analysis did not reveal major differences in interests and cultural elements of the unemployed. Utilisation patterns emerged though; the employed seem to use Facebook to connect with local activities, while the unemployed use it mostly as for entertainment purposes and as a source of news, making them susceptible to mis/disinformation. We believe these findings can help policymakers get a deeper understanding of this population and initiatives that improve both the hard and the soft skills of this fragile population.
In a world increasingly dominated by AI applications, an understudied aspect is the carbon and social footprint of these power-hungry algorithms that require copious computation and a trove of data for training and prediction. While profitable in the short-term, these practices are unsustainable and socially extractive from both a data-use and energy-use perspective. This work proposes an ESG-inspired framework combining socio-technical measures to build eco-socially responsible AI systems. The framework has four pillars: compute-efficient machine learning, federated learning, data sovereignty, and a LEEDesque certificate. Compute-efficient machine learning is the use of compressed network architectures that show marginal decreases in accuracy. Federated learning augments the first pillars impact through the use of techniques that distribute computational loads across idle capacity on devices. This is paired with the third pillar of data sovereignty to ensure the privacy of user data via techniques like use-based privacy and differential privacy. The final pillar ties all these factors together and certifies products and services in a standardized manner on their environmental and social impacts, allowing consumers to align their purchase with their values.
Ethics in the emerging world of data science are often discussed through cautionary tales about the dire consequences of missteps taken by high profile companies or organizations. We take a different approach by foregrounding the ways that ethics are implicated in the day-to-day work of data science, focusing on instances in which data scientists recognize, grapple with, and conscientiously respond to ethical challenges. This paper presents a case study of ethical dilemmas that arose in a data science for social good (DSSG) project focused on improving navigation for people with limited mobility. We describe how this particular DSSG team responded to those dilemmas, and how those responses gave rise to still more dilemmas. While the details of the case discussed here are unique, the ethical dilemmas they illuminate can commonly be found across many DSSG projects. These include: the risk of exacerbating disparities; the thorniness of algorithmic accountability; the evolving opportunities for mischief presented by new technologies; the subjective and value- laden interpretations at the heart of any data-intensive project; the potential for data to amplify or mute particular voices; the possibility of privacy violations; and the folly of technological solutionism. Based on our tracing of the teams responses to these dilemmas, we distill lessons for an ethical data science practice that can be more generally applied across DSSG projects. Specifically, this case experience highlights the importance of: 1) Setting the scene early on for ethical thinking 2) Recognizing ethical decision-making as an emergent phenomenon intertwined with the quotidian work of data science for social good 3) Approaching ethical thinking as a thoughtful and intentional balancing of priorities rather than a binary differentiation between right and wrong.
As the amount of scientific data continues to grow at ever faster rates, the research community is increasingly in need of flexible computational infrastructure that can support the entirety of the data science lifecycle, including long-term data storage, data exploration and discovery services, and compute capabilities to support data analysis and re-analysis, as new data are added and as scientific pipelines are refined. We describe our experience developing data commons-- interoperable infrastructure that co-locates data, storage, and compute with common analysis tools--and present several cases studies. Across these case studies, several common requirements emerge, including the need for persistent digital identifier and metadata services, APIs, data portability, pay for compute capabilities, and data peering agreements between data commons. Though many challenges, including sustainability and developing appropriate standards remain, interoperable data commons bring us one step closer to effective Data Science as Service for the scientific research community.
Article about objective laws of formation of social and economic institutes in system of electronic commerce. Rapid development of Internet technologies became the reason of deep institutional transformation of economic relations. The author analyzes value transaction costs as motive power of formation of new economic institutes in network economy.
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