Data as an economic good, data as a commons, and data governance

• URL: http://arxiv.org/abs/2212.10244v2
• Date: Tue, 18 Apr 2023 11:29:29 GMT
• Title: Data as an economic good, data as a commons, and data governance
• Authors: Nadezhda Purtova and Gijs van Maanen
• Abstract summary: We conclude that focusing on data as an economic good in governance efforts is hardwired to only result in more data production. Data governance is often a red herring which distracts from other digital problems. We propose a political-ecological approach to governing the digital society.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper provides a systematic and critical review of the economics literature on data as an economic good and draws lessons for data governance. We conclude that focusing on data as an economic good in governance efforts is hardwired to only result in more data production and cannot deliver other societal goals contrary to what is often claimed in the literature and policy. Data governance is often a red herring which distracts from other digital problems. The governance of digital society cannot rely exclusively on data-centric economic models. We review the literatures and the underlying empirical and political claims concerning data commons. While commons thinking is useful to frame digital problems in terms of ecologies, it has important limitations. We propose a political-ecological approach to governing the digital society, defined by ecological thinking about governance problems and the awareness of the political nature of framing the problems and mapping their ecological makeup.

Related papers

• Navigating the Data Trading Crossroads: An Interdisciplinary Survey [33.64953318642493]
  Data has been increasingly recognized as a critical factor in the future economy. However, constructing an efficient data trading market faces challenges such as privacy breaches, data monopolies, and misuse. This paper aims to identify existing problems, research gaps, and propose potential solutions.
  arXiv  Detail & Related papers  (2024-07-16T08:07:16Z)
• Beyond case studies: Teaching data science critique and ethics through sociotechnical surveillance studies [0.0]
  Ethics have become an urgent concern for data science research, practice, and instruction in the wake of growing critique of algorithms and systems showing that they reinforce structural oppression. We designed a data science ethics course that spoke to the social phenomena at the root of critical data studies through analysis of a pressing sociotechnical system: surveillance systems. Students developed critical analysis skills that allowed them to investigate surveillance systems of their own and identify their benefits, harms, main proponents, those who resist them.
  arXiv  Detail & Related papers  (2023-05-03T20:24:42Z)
• Mapping and Comparing Data Governance Frameworks: A benchmarking exercise to inform global data governance deliberations [0.0]
  Article explores the increasing importance of global data governance due to the rapid growth of data and the need for responsible data use and protection. The report highlights the need for a more holistic, coordinated approach to data governance to manage the global flow of data responsibly and for the public interest.
  arXiv  Detail & Related papers  (2023-02-27T12:56:25Z)
• Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty [0.0]
  Our work focuses on studying the potential of textual data, in particular news pieces, for measuring economic policy uncertainty (EPU) Economic policy uncertainty is defined as the public's inability to predict the outcomes of their decisions under new policies and future economic fundamentals. Our work proposes a machine learning based solution involving weak supervision to classify news articles with regards to economic policy uncertainty.
  arXiv  Detail & Related papers  (2022-08-10T09:08:29Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist [67.08543240320756]
  We show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning and data-driven simulations. We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.
  arXiv  Detail & Related papers  (2021-08-06T01:30:41Z)
• An Ethical Highlighter for People-Centric Dataset Creation [62.886916477131486]
  We propose an analytical framework to guide ethical evaluation of existing datasets and to serve future dataset creators in avoiding missteps. Our work is informed by a review and analysis of prior works and highlights where such ethical challenges arise.
  arXiv  Detail & Related papers  (2020-11-27T07:18:44Z)
• A Survey on Data Pricing: from Economics to Data Science [61.72030615854597]
  We examine various motivations behind data pricing and understand the economics of data pricing. We discuss both digital products and data products. We consider a series of challenges and directions for future work.
  arXiv  Detail & Related papers  (2020-09-09T19:31:38Z)
• Efficiency in Digital Economies -- A Primer on Tokenomics [55.41644538483948]
  cryptographic tokens are a new digital paradigm that can facilitate the establishment of economic incentives in digital ecoystems. We show how certain principles and values that arise from the evolutionary process of digital cooperation can lead to a market economy characterized by economic efficiency of both individuals and the tokenized ecosystem as a whole.
  arXiv  Detail & Related papers  (2020-08-06T09:31:56Z)
• A Philosophy of Data [91.3755431537592]
  We work from the fundamental properties necessary for statistical computation to a definition of statistical data. We argue that the need for useful data to be commensurable rules out an understanding of properties as fundamentally unique or equal. With our increasing reliance on data and data technologies, these two characteristics of data affect our collective conception of reality.
  arXiv  Detail & Related papers  (2020-04-15T14:47:24Z)
• Whose Side are Ethics Codes On? Power, Responsibility and the Social Good [0.0]
  We argue that ethics codes that elevate consumers may simultaneously subordinate the needs of vulnerable populations. We introduce the concept of digital differential vulnerability to explain disproportionate exposures to harm within data technology.
  arXiv  Detail & Related papers  (2020-02-04T22:05:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.