arXiv subjects

Alexis Tsoukiàs

Publications and source records attributed to Alexis Tsoukiàs.

14 recordsLinked to original sources

arXiv2026

Explainability and justification of automatic-decision making: A conceptual framework and a practical application

Explainability of algorithmic decision-making systems is both a regulatory objective and an area of intense research. The article argues that a crucial condition for the acceptability of algorithmic decision-making systems is that decisions must be justified in the eyes of their recipients. We make a clear distinction between explanation and justification. Explanations describe how a decision was made, while justifications give reasons that aim to make the decision acceptable. We propose a conceptual framework of explanations and justifications, based on Habermas's theory of communicative action and Perelman's New Rhetoric theory of law. This framework helps to analyze how different forms of explanation can support or fail to support justification. We illustrate our approach with a case study on university admissions in France.

cs.CY↗

arXiv2025

On compromising freedom of choice and subjective

This paper proposes a new framework for evaluating capability sets by incorporating individual preferences over the diversity of accessible options. Building on the Capability Approach, we introduce a compromise method that balances between the notions of negative and positive freedom, effectively capturing the intrinsic and instrumental values of diverse choices within capability sets.

cs.CY↗

arXiv2025

Meaningful, Useful and Legitimate Information in the Use of Index Numbers for Decision Making

Often information relevant to a decision is summarized in an index number. This paper explores conditions under which conclusions using index numbers are relevant to the decision that needs to be made. Specifically it explores the idea that a statement using scales of measurement is meaningful in the sense that its truth or falsity does not depend on an arbitrary choice of parameters; the concept that a conclusion using index numbers is useful for the specific decision that needs to be made; and the notion that such a conclusion is legitimate in the sense that it is collected and used in a way that satisfies cultural, historical, organizational and legal constraints. While meaningfulness is a precisely defined concept, usefulness and legitimacy are not, and the paper explores properties of these concepts that lay the groundwork for making them more precise. Many examples involving two well-known and widely-used index numbers, body mass indices and air pollution indices, are used to explore the properties of and interrelationships among meaningfulness, usefulness, and legitimacy.

econ.TH↗

arXiv2024

Integrating problem structuring methods with formal design theory: collective water management policy design in Tunisia

Groundwater management, especially in regions like Tunisia, is challenging due to diverse stakeholder interests and the dry structure of climate, which is extremely challenging for the sustainability of water resources. This paper proposes an innovative approach to policy design by merging Problem Structuring Methods (PSMs) and the Policy-Knowledge, Concepts, Proposals (P-KCP) methodology. Utilizing cognitive maps and value trees, the study aims to generate new collective groundwater management practices. Bridging decision theory and design theory, the study addresses the gap in new alternative generation and highlights the P-KCP's role in innovative policy design. Integrating PSMs and C-K theory, the framework expands policy alternatives and advocates for participatory approaches. It emphasizes adaptability across contexts, provides replicable process descriptions, and encourages the creation of unconventional policy solutions. Ultimately, this comprehensive framework offers a practical guide for policy innovation and collaboration.

physics.soc-ph↗

arXiv2024

Subjective fairness in algorithmic decision-support

The treatment of fairness in decision-making literature usually involves quantifying fairness using objective measures. This work takes a critical stance to highlight the limitations of these approaches (group fairness and individual fairness) using sociological insights. First, we expose how these metrics often fail to reflect societal realities. By neglecting crucial historical, cultural, and social factors, they fall short of capturing all discriminatory practices. Second, we redefine fairness as a subjective property moving from a top-down to a bottom-up approach. This shift allows the inclusion of diverse stakeholders perceptions, recognizing that fairness is not merely about objective metrics but also about individuals views on their treatment. Finally, we aim to use explanations as a mean to achieve fairness. Our approach employs explainable clustering to form groups based on individuals subjective perceptions to ensure that individuals who see themselves as similar receive similar treatment. We emphasize the role of explanations in achieving fairness, focusing not only on procedural fairness but also on providing subjective explanations to convince stakeholders of their fair treatment.

cs.CY↗

arXiv2024

From decision aiding to the massive use of algorithms: where does the responsibility stand?

In the very large debates on ethics of algorithms, this paper proposes an analysis on human responsibility. On one hand, algorithms are designed by some humans, who bear a part of responsibility in the results and unexpected impacts. Nevertheless, we show how the fact they cannot embrace the full situations of use and consequences lead to an unreachable limit. On the other hand, using technology is never free of responsibility, even if there also exist limits to characterise. Massive uses by unprofessional users introduce additional questions that modify the possibilities to be ethically responsible. The article is structured in such a way as to show how the limits have gradually evolved, leaving unthought of issues and a failure to share responsibility.

cs.CY↗

arXiv2024

A framework for expected capability sets

This paper addresses decision-aiding problems that involve multiple objectives and uncertain states of the world. Inspired by the capability approach, we focus on cases where a policy maker chooses an act that, combined with a state of the world, leads to a set of choices for citizens. While no preferential information is available to construct importance parameters for the criteria, we can obtain likelihoods for the different states. To effectively support decision-aiding in this context, we propose two procedures that merge the potential set of choices for each state of the world taking into account their respective likelihoods. Our procedures satisfy several fundamental and desirable properties that characterize the outcomes.

cs.CY↗

arXiv2023

Conflict Transformation and Management. From Cognitive Maps to Value Trees

Conflict transformation and management are complex decision processes with extremely high stakes at hand and could greatly benefit from formal approaches to decision support. For this purpose we develop a general framework about how to use problem structuring methods for such purposes. More precisely we show how to transform cognitive maps to value trees in order to promote a more design-oriented approach to decision support aiming at constructing innovative solutions for conflict management purposes. We show that our findings have a much wider validity since they allow to move from a descriptive representation of a problem situation to a more prescriptive one using formal procedures and models.

cs.AI↗

arXiv2023

What is a decision problem?

This paper presents a general framework about what is a decision problem. Our motivation is related to the fact that decision analysis and operational research are structured (as disciplines) around classes of methods, while instead we should first characterise the decision problems our clients present us. For this purpose we introduce a new framework, independent from any existing method, based upon primitives provided by (or elicited from) the client. We show that the number of archetypal decision problems are finite and so the archetypal decision support methods.

cs.AI↗

arXiv2022

Is there an Ethical Operational Research?

The ethical dimension of Operational Research and Decision Aiding, although not a new subject, turns to be a concern, both for the large public and the OR community, because of the wide spread of autonomous artefacts endowed with decision capacity thanks to the use of models, methods and tools developed within our field. The paper addresses the question of whether there exists an "Ethical Operational Research", identifies the ethical questions which are specific to our professional community and suggests research topics which, although independently developed, are relevant for handling such questions.

cs.CY↗

arXiv2022

Fairness and Explainability in Automatic Decision-Making Systems. A challenge for computer science and law

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.

cs.CY↗

arXiv2022

What is Legitimate Decision Support?

Decision support is the science and associated practice that consist in providing recommendations to decision makers facing problems, based on available theoretical knowledge and empirical data. Although this activity is often seen as being concerned with solving mathematical problems and conceiving algorithms, it is essentially an empirical and socially framed activity, where interactions between clients and analysts, and between them and concerned third parties, play a crucial role. Since the 80s, two concepts have structured the literature devoted to analysing this aspect of decision support: validity and legitimacy. Whereas validity is focused on the interactions between the client and the analyst, legitimacy refers to the broader picture: the organisational context, the overall problem situation, the environment, culture, history. Despite its importance, this concept has not received the attention it deserves in the literature in decision support. The present paper aims at filling this gap. For that purpose, we review the literature in other disciplines relevant to elaborate a concept of legitimacy useful in decision support contexts. Based on this review, we propose a general theory of legitimacy, adapted to decision support contexts, encompassing the relevant contributions we found in the literature. According to this general theory, a legitimate decision support intervention is one for which the decision support provider produces a justification that satisfies two conditions: (i) it effectively convinces the decision support provider's interlocutors (effectiveness condition) and (ii) it is organised around the active elicitation of as many and as diverse counterarguments as possible (truthfulness condition). Despite its conceptual simplicity, legitimacy, understood in this sense, is a very exacting requirement, opening ambitious research avenues that we delineate.

cs.CY↗

arXiv2021

Is the Capability approach a useful tool for decision aiding in public policy making?

This paper aims at proposing a model representing individuals' welfare using Sen's capability approach (CA). It is the first step of an attempt to measure the negative impact caused by the damage at a Common on a given population's welfare, and widely speaking, a first step into modelling collective threat. The CA is a multidimensional representation of persons' well-beings which account for human diversity. It has received substantial attention from scholars from different disciplines such as philosophy, economics and social scientist. Nevertheless, there is no empirical work that really fits the theoretical framework. Our goal is to show that the capability approach can be very useful for decision aiding, especially if we fill the gap between the theory and the empirical work; thus we will propose a framework that is both usable and a close representation of what capability is.

econ.GN↗

arXiv2020

Social Responsibility of Algorithms

Should we be concerned by the massive use of devices and algorithms which automatically handle an increasing number of everyday activities within our societies? The paper makes a short overview of the scientific investigation around this topic, showing that the development, existence and use of such autonomous artifacts is much older than the recent interest in machine learning monopolised artificial intelligence. We then categorise the impact of using such artifacts to the whole process of data collection, structuring, manipulation as well as in recommendation and decision making. The suggested framework allows to identify a number of challenges for the whole community of decision analysts, both researchers and practitioners.

cs.CY↗