International audience ; This paper aims at addressing the problem of what characterises decisionaiding for public policy making problem situations. Under such a perspective it analyses concepts like "public policy", "deliberation", "legitimation", "accountability" and shows the need to expand the concept of rationality which is expected to support the acceptability of a public policy. We then analyse the more recent attempt to construct a rational support for policy making, the "evidence-based policy making" approach. Despite the innovation introduced with this approach, we show that it basically fails to address the deep reasons why supporting the design, implementation and assessment of public policies is such a hard problem. We finally show that we need to move one step ahead, specialising decision-aiding to meet the policy cycle requirements: a need for policy analytics.
International audience ; This paper aims at addressing the problem of what characterises decisionaiding for public policy making problem situations. Under such a perspective it analyses concepts like "public policy", "deliberation", "legitimation", "accountability" and shows the need to expand the concept of rationality which is expected to support the acceptability of a public policy. We then analyse the more recent attempt to construct a rational support for policy making, the "evidence-based policy making" approach. Despite the innovation introduced with this approach, we show that it basically fails to address the deep reasons why supporting the design, implementation and assessment of public policies is such a hard problem. We finally show that we need to move one step ahead, specialising decision-aiding to meet the policy cycle requirements: a need for policy analytics.
The paper offers a contribution to the interdisciplinary constructs of analyzing fairness issues in automatic algorithmic decisions. Section 1 shows that technical choices in supervised learning have social implications that need to be considered. Section 2 proposes a contextual approach to the issue of unintended group discrimination, i.e. decision rules that are facially neutral but generate disproportionate impacts across social groups (e.g., gender, race or ethnicity). The contextualization will focus on the legal systems of the United States on the one hand and Europe on the other. In particular, legislation and case law tend to promote different standards of fairness on both sides of the Atlantic. Section 3 is devoted to the explainability of algorithmic decisions; it will confront and attempt to cross-reference legal concepts (in European and French law) with technical concepts and will highlight the plurality, even polysemy, of European and French legal texts relating to the explicability of algorithmic decisions. The conclusion proposes directions for further research ; Version 1.0-6 mai 2022 RESUME L'article propose une contribution aux constructions interdisciplinaires de l'analyse des enjeux d'équité dans les décisions algorithmiques automatiques. La section 1 montre que les choix techniques en apprentissage supervisé ont des implications sociales dont il faut prendre la mesure. La section 2 propose une approche contextuelle de la question de la discrimination de groupe non intentionnelle, c'est-à-dire de règles de décision facialement neutres mais qui génèrent des impacts disproportionnés selon les groupes sociaux (selon les cas : genrés, raciaux ou ethniques). La contextualisation portera sur les systèmes juridiques des États-Unis d'un côté, de l'Europe d'un autre côté. En particulier, la législation et la jurisprudence tendent à promouvoir des critères d'équité différents de part et d'autre de l'Atlantique. La section 3 est consacrée à l'explicabilité des décisions algorithmiques ; elle ...
The paper offers a contribution to the interdisciplinary constructs of analyzing fairness issues in automatic algorithmic decisions. Section 1 shows that technical choices in supervised learning have social implications that need to be considered. Section 2 proposes a contextual approach to the issue of unintended group discrimination, i.e. decision rules that are facially neutral but generate disproportionate impacts across social groups (e.g., gender, race or ethnicity). The contextualization will focus on the legal systems of the United States on the one hand and Europe on the other. In particular, legislation and case law tend to promote different standards of fairness on both sides of the Atlantic. Section 3 is devoted to the explainability of algorithmic decisions; it will confront and attempt to cross-reference legal concepts (in European and French law) with technical concepts and will highlight the plurality, even polysemy, of European and French legal texts relating to the explicability of algorithmic decisions. The conclusion proposes directions for further research ; Version 1.0-6 mai 2022 RESUME L'article propose une contribution aux constructions interdisciplinaires de l'analyse des enjeux d'équité dans les décisions algorithmiques automatiques. La section 1 montre que les choix techniques en apprentissage supervisé ont des implications sociales dont il faut prendre la mesure. La section 2 propose une approche contextuelle de la question de la discrimination de groupe non intentionnelle, c'est-à-dire de règles de décision facialement neutres mais qui génèrent des impacts disproportionnés selon les groupes sociaux (selon les cas : genrés, raciaux ou ethniques). La contextualisation portera sur les systèmes juridiques des États-Unis d'un côté, de l'Europe d'un autre côté. En particulier, la législation et la jurisprudence tendent à promouvoir des critères d'équité différents de part et d'autre de l'Atlantique. La section 3 est consacrée à l'explicabilité des décisions algorithmiques ; elle ...
Multiple decision makers and managers, competing interests and values, scarcity of resources, complex legislative requirements, and vast uncertainties about the future due to a more connected and rapidly changing world and the impacts of climate change, are just some of the issues that impact upon the capacity to carry out effective water planning and management. Throughout the world these issues are becoming increasingly difficult to handle, and there have been calls for more adapted approaches to aid the decision making processes required for water planning and management. Participatory risk management approaches appear appropriate to such situations as they can be designed to increase collaboration and manage conflict, explicit uncertainties, and structure complexity in more understandable forms. This paper will outline some insights and lessons learnt from the design and implementation of two different participatory risk management processes for water planning and management: a values-based method based on the Australian and New Zealand Standard for Risk Management for the development of the Lower Hawkesbury Estuary Management Plan in Australia; and a participatory modelling approach to manage the risks of living with floods and droughts in the Iskar basin in Bulgaria. Both processes were designed and implemented with the aid of researchers, local managers, government representatives at various levels of jurisdiction, community stakeholders and external legislative, scientific or engineering experts. The Australian process was an initiative driven and funded by the Hornsby Shire Council, a peri-urban municipality of Sydney. It consisted of three interactive stakeholder workshops with an average of 20 participants, held over a period of four months, as well as an external scientific and legislative review. The workshops focussed on establishing estuarine values, issues and current management practices; performing a risk assessment based on the stakeholder defined values (assets) and issues (risks); and formulating strategies to treat the highest prioritised risks as input to the estuary management "risk response" plan. The Bulgarian process in the region of Sofia formed part of the European Project "AquaStress", funded by the European Union, and was primarily driven from a research perspective. The participatory process was more elaborate in design than the Australian process with around 60 stakeholders divided into 6 groups taking part in a series of 15 workshops, individual interviews and evaluation exercises over a one year period. The process included cognitive mapping of the current management context and physical system, values, visions and preference elicitation for actions, strategy development and evaluation. Both cases provided rich insights into the value and constraints of participatory risk management approaches in different regulatory and political environments, as well as some important recurrent issues that organising teams of participatory approaches need to appreciate including: impacts of last minute process changes; how to deal with divergent objectives in a multi-institutional organising team; and the unintended ethical issues that can arise when working in "real-world" management situations. Increasing awareness of the value and potential issues associated with participatory risk management approaches should aid their adoption and the subsequent improvement of water planning and management around the world.
International audience ; Our digital age is characterized by both a generalized access to data and an increased call for participation of the public and other stakeholders and communities in policy design and decision-making. This context raises new challenges for political decision-makers and analysts in providing these actors with new means and moral duties for decision support, including in the area of environmental policy. The concept of "policy analytics" was introduced in 2013 as an attempt to develop a framework, tools, and methods to address these challenges. This conceptual initiative prompted numerous research teams to develop empirical applications of this framework and to reflect on their own decision-support practice at the science-policy interface in various environmental domains around the world. During a workshop in Paris in 2018, participants shared and discussed their experiences of these applications and practices. In this paper, we present and analyze a set of applications to identify a series of key properties that underpin a policy analytics approach, in order to provide the conceptual foundation for policy analytics to address current policy design and decision-making challenges. The induced properties are demand-orientedness, performativity, normative transparency, and data meaningfulness. We show how these properties materialized through these six case studies, and we explain why we consider them key to effective policy analytics applications, particularly in environmental policy design and decision-making on environmental issues. This clarification of the policy analytics concept eventually enables us to highlight research frontiers to further improve the concept.
International audience ; Our digital age is characterized by both a generalized access to data and an increased call for participation of the public and other stakeholders and communities in policy design and decision-making. This context raises new challenges for political decision-makers and analysts in providing these actors with new means and moral duties for decision support, including in the area of environmental policy. The concept of "policy analytics" was introduced in 2013 as an attempt to develop a framework, tools, and methods to address these challenges. This conceptual initiative prompted numerous research teams to develop empirical applications of this framework and to reflect on their own decision-support practice at the science-policy interface in various environmental domains around the world. During a workshop in Paris in 2018, participants shared and discussed their experiences of these applications and practices. In this paper, we present and analyze a set of applications to identify a series of key properties that underpin a policy analytics approach, in order to provide the conceptual foundation for policy analytics to address current policy design and decision-making challenges. The induced properties are demand-orientedness, performativity, normative transparency, and data meaningfulness. We show how these properties materialized through these six case studies, and we explain why we consider them key to effective policy analytics applications, particularly in environmental policy design and decision-making on environmental issues. This clarification of the policy analytics concept eventually enables us to highlight research frontiers to further improve the concept.
International audience ; Our digital age is characterized by both a generalized access to data and an increased call for participation of the public and other stakeholders and communities in policy design and decision-making. This context raises new challenges for political decision-makers and analysts in providing these actors with new means and moral duties for decision support, including in the area of environmental policy. The concept of "policy analytics" was introduced in 2013 as an attempt to develop a framework, tools, and methods to address these challenges. This conceptual initiative prompted numerous research teams to develop empirical applications of this framework and to reflect on their own decision-support practice at the science-policy interface in various environmental domains around the world. During a workshop in Paris in 2018, participants shared and discussed their experiences of these applications and practices. In this paper, we present and analyze a set of applications to identify a series of key properties that underpin a policy analytics approach, in order to provide the conceptual foundation for policy analytics to address current policy design and decision-making challenges. The induced properties are demand-orientedness, performativity, normative transparency, and data meaningfulness. We show how these properties materialized through these six case studies, and we explain why we consider them key to effective policy analytics applications, particularly in environmental policy design and decision-making on environmental issues. This clarification of the policy analytics concept eventually enables us to highlight research frontiers to further improve the concept.
International audience ; Our digital age is characterized by both a generalized access to data and an increased call for participation of the public and other stakeholders and communities in policy design and decision-making. This context raises new challenges for political decision-makers and analysts in providing these actors with new means and moral duties for decision support, including in the area of environmental policy. The concept of "policy analytics" was introduced in 2013 as an attempt to develop a framework, tools, and methods to address these challenges. This conceptual initiative prompted numerous research teams to develop empirical applications of this framework and to reflect on their own decision-support practice at the science-policy interface in various environmental domains around the world. During a workshop in Paris in 2018, participants shared and discussed their experiences of these applications and practices. In this paper, we present and analyze a set of applications to identify a series of key properties that underpin a policy analytics approach, in order to provide the conceptual foundation for policy analytics to address current policy design and decision-making challenges. The induced properties are demand-orientedness, performativity, normative transparency, and data meaningfulness. We show how these properties materialized through these six case studies, and we explain why we consider them key to effective policy analytics applications, particularly in environmental policy design and decision-making on environmental issues. This clarification of the policy analytics concept eventually enables us to highlight research frontiers to further improve the concept.
International audience ; Our digital age is characterized by both a generalized access to data and an increased call for participation of the public and other stakeholders and communities in policy design and decision-making. This context raises new challenges for political decision-makers and analysts in providing these actors with new means and moral duties for decision support, including in the area of environmental policy. The concept of "policy analytics" was introduced in 2013 as an attempt to develop a framework, tools, and methods to address these challenges. This conceptual initiative prompted numerous research teams to develop empirical applications of this framework and to reflect on their own decision-support practice at the science-policy interface in various environmental domains around the world. During a workshop in Paris in 2018, participants shared and discussed their experiences of these applications and practices. In this paper, we present and analyze a set of applications to identify a series of key properties that underpin a policy analytics approach, in order to provide the conceptual foundation for policy analytics to address current policy design and decision-making challenges. The induced properties are demand-orientedness, performativity, normative transparency, and data meaningfulness. We show how these properties materialized through these six case studies, and we explain why we consider them key to effective policy analytics applications, particularly in environmental policy design and decision-making on environmental issues. This clarification of the policy analytics concept eventually enables us to highlight research frontiers to further improve the concept.
International audience ; Our digital age is characterized by both a generalized access to data and an increased call for participation of the public and other stakeholders and communities in policy design and decision-making. This context raises new challenges for political decision-makers and analysts in providing these actors with new means and moral duties for decision support, including in the area of environmental policy. The concept of "policy analytics" was introduced in 2013 as an attempt to develop a framework, tools, and methods to address these challenges. This conceptual initiative prompted numerous research teams to develop empirical applications of this framework and to reflect on their own decision-support practice at the science-policy interface in various environmental domains around the world. During a workshop in Paris in 2018, participants shared and discussed their experiences of these applications and practices. In this paper, we present and analyze a set of applications to identify a series of key properties that underpin a policy analytics approach, in order to provide the conceptual foundation for policy analytics to address current policy design and decision-making challenges. The induced properties are demand-orientedness, performativity, normative transparency, and data meaningfulness. We show how these properties materialized through these six case studies, and we explain why we consider them key to effective policy analytics applications, particularly in environmental policy design and decision-making on environmental issues. This clarification of the policy analytics concept eventually enables us to highlight research frontiers to further improve the concept.
Our digital age is characterized by both a generalized access to data and an increased call for participation of the public and other stakeholders and communities in policy design and decision-making. This context raises new challenges for political decision-makers and analysts in providing these actors with new means and moral duties for decision support, including in the area of environmental policy. The concept of "policy analytics" was introduced in 2013 as an attempt to develop a framework, tools, and methods to address these challenges. This conceptual initiative prompted numerous research teams to develop empirical applications of this framework and to reflect on their own decision-support practice at the science-policy interface in various environmental domains around the world. During a workshop in Paris in 2018, participants shared and discussed their experiences of these applications and practices. In this paper, we present and analyze a set of applications to identify a series of key properties that underpin a policy analytics approach, in order to provide the conceptual foundation for policy analytics to address current policy design and decision-making challenges. The induced properties are demand-orientedness, performativity, normative transparency, and data meaningfulness. We show how these properties materialized through these six case studies, and we explain why we consider them key to effective policy analytics applications, particularly in environmental policy design and decision-making on environmental issues. This clarification of the policy analytics concept eventually enables us to highlight research frontiers to further improve the concept.
