Forecasting for Social Good

• URL: http://arxiv.org/abs/2009.11669v1
• Date: Thu, 24 Sep 2020 13:16:57 GMT
• Title: Forecasting for Social Good
• Authors: Bahman Rostami-Tabar and Mohammad M Ali and Tao Hong and Rob J Hyndman and Michael D Porter and Aris Syntetos
• Abstract summary: We present some key attributes that qualify a forecasting process as Forecasting for Social Good (FSG) FSG is concerned with advancing social and environmental goals and prioritises these over conventional measures of economic success. We propose an FSG maturity framework as the means to engage academics and practitioners with research in this area.
• Score: 0.8295385180806493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forecasting plays a critical role in the development of organisational business strategies. Despite a considerable body of research in the area of forecasting, the focus has largely been on the financial and economic outcomes of the forecasting process as opposed to societal benefits. Our motivation in this study is to promote the latter, with a view to using the forecasting process to advance social and environmental objectives such as equality, social justice and sustainability. We refer to such forecasting practices as Forecasting for Social Good (FSG) where the benefits to society and the environment take precedence over economic and financial outcomes. We conceptualise FSG and discuss its scope and boundaries in the context of the "Doughnut theory". We present some key attributes that qualify a forecasting process as FSG: it is concerned with a real problem, it is focused on advancing social and environmental goals and prioritises these over conventional measures of economic success, and it has a broad societal impact. We also position FSG in the wider literature on forecasting and social good practices. We propose an FSG maturity framework as the means to engage academics and practitioners with research in this area. Finally, we highlight that FSG: (i) cannot be distilled to a prescriptive set of guidelines, (ii) is scalable, and (iii) has the potential to make significant contributions to advancing social objectives.

Related papers

• Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities [48.45951497996322]
  Foundation models (STFMs) have revolutionized artificial intelligence, setting new benchmarks in performance and enabling transformative capabilities across a wide range of vision and language tasks. In this paper, we articulate a vision for the future of STFMs, outlining their essential characteristics and generalization capabilities necessary for broad applicability. We explore potential opportunities and directions to advance research towards the aim of effective and broadly applicable STFMs.
  arXiv  Detail & Related papers  (2025-01-15T08:52:28Z)
• Good Practices for Institutional Organization of Research Institutes: Excellence in Research and Positive Impact on Society [0.0]
  We analyze examples of research institutes that stand out in scientific excellence and social impact. Special focus is placed on small countries and the field of artificial intelligence.
  arXiv  Detail & Related papers  (2024-12-30T23:22:28Z)
• A Toolkit for Measuring the Impacts of Public Funding on Open Source Software Development [0.0]
  Impacts of public funding on open source software development remain poorly understood. We present a taxonomy of potential social, economic, and technological impacts that can be both positive and negative. With this toolkit, we contribute to the multi-stakeholder conversation about the value and impacts of funding on OSS developers and society at large.
  arXiv  Detail & Related papers  (2024-11-09T01:13:45Z)
• Evolutionary mechanisms that promote cooperation may not promote social welfare [1.7819574476785418]
  We show that the objectives of maximising cooperation levels and the objectives of maximising social welfare are often misaligned. We argue for the need of adopting social welfare as the main optimisation objective when designing and implementing evolutionary mechanisms for social and collective goods.
  arXiv  Detail & Related papers  (2024-08-09T22:51:13Z)
• Performative Prediction on Games and Mechanism Design [69.7933059664256]
  We study a collective risk dilemma where agents decide whether to trust predictions based on past accuracy. As predictions shape collective outcomes, social welfare arises naturally as a metric of concern. We show how to achieve better trade-offs and use them for mechanism design.
  arXiv  Detail & Related papers  (2024-08-09T16:03:44Z)
• The Relative Value of Prediction in Algorithmic Decision Making [0.0]
  We ask: What is the relative value of prediction in algorithmic decision making? We identify simple, sharp conditions determining the relative value of prediction vis-a-vis expanding access. We illustrate how these theoretical insights may be used to guide the design of algorithmic decision making systems in practice.
  arXiv  Detail & Related papers  (2023-12-13T20:52:45Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI. It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• Causal Fairness for Outcome Control [68.12191782657437]
  We study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable. In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision. We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this.
  arXiv  Detail & Related papers  (2023-06-08T09:31:18Z)
• Reconciling Predictive and Statistical Parity: A Causal Approach [68.59381759875734]
  We propose a new causal decomposition formula for the fairness measures associated with predictive parity. We show that the notions of statistical and predictive parity are not really mutually exclusive, but complementary and spanning a spectrum of fairness notions.
  arXiv  Detail & Related papers  (2023-06-08T09:23:22Z)
• Envisioning Communities: A Participatory Approach Towards AI for Social Good [10.504838259488844]
  We argue that AI for social good ought to be assessed by the communities that the AI system will impact. We show how the capabilities approach aligns with a participatory approach for the design and implementation of AI for social good research.
  arXiv  Detail & Related papers  (2021-05-04T21:40:04Z)
• Why Talking about ethics is not enough: a proposal for Fintech's AI ethics [0.0]
  This research aims to analyze the benefits of the application of the theory and the idea of Social License. The formation of a Social License will allow early-stage stakeholders to participate in the elaboration of an ethical code.
  arXiv  Detail & Related papers  (2021-02-14T18:23:42Z)
• Forecasting: theory and practice [65.71277206849244]
  This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts.
  arXiv  Detail & Related papers  (2020-12-04T16:56:44Z)

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.