On the relevance of APIs facing fairwashed audits

Recent legislation required AI platforms to provide APIs for regulators to assess their compliance with the law. Research has nevertheless shown that platforms can manipulate their API answers through fairwashing. Facing this threat for reliable auditing, this paper studies the benefits of the joint use of platform scraping and of APIs. In this setup, we elaborate on the use of scraping to detect manipulated answers: since fairwashing only manipulates API answers, exploiting scraps may reveal a manipulation. To abstract the wide range of specific API-scrap situations, we introduce a notion of proxy that captures the consistency an auditor might expect between both data sources. If the regulator has a good proxy of the consistency, then she can easily detect manipulation and even bypass the API to conduct her audit. On the other hand, without a good proxy, relying on the API is necessary, and the auditor cannot defend against fairwashing. We then simulate practical scenarios in which the auditor may mostly rely on the API to conveniently conduct the audit task, while maintaining her chances to detect a potential manipulation. To highlight the tension between the audit task and the API fairwashing detection task, we identify Pareto-optimal strategies in a practical audit scenario. We believe this research sets the stage for reliable audits in practical and manipulation-prone setups.Comment: 18 pages, 7 figure

Semi-definite positive programming relaxations for graph K-n-coloring in frequency assignment

noteIn this paper we will describe a new class of coloring problems, arising from military frequency assignment, where we want to minimize the number of distinct n-uples of colors used to color a given set of n-complete-subgraphs of a graph. We will propose two relaxations based on Semi-Definite Programming models for graph and hypergraph coloring, to approximate those (generally) NP-hard problems, as well as a generalization of the works of Karger et al. for hypergraph coloring, to find good feasible solutions with a probabilistic approach

Semi-Definite positive Programming Relaxations for Graph K

In this paper we will describe a new class of coloring problems, arising from military frequency assignment, where we want to minimize the number of distinct n-uples of colors used to color a given set of n-complete-subgraphs of a graph. We will propose two relaxations based on Semi-Definite Programming models for graph and hypergraph coloring, to approximate those (generally) NP-hard problems, as well as a generalization of the works of Karger et al. for hypergraph coloring, to find good feasible solutions with a probabilistic approach

Relaxation lagrangienne et filtrage par coûts réduits appliqués à la production d'éléctricité.

Le problème UCP (Unit Commitment Problem) consiste à planifier la productiond'un parc de centrales électriques de manière à satisfaire un besoinprévisionnel donné sur une échelle de temps discrétisée (besoin horaire sur 24heures). L'objectif consiste à définir à moindre coût d'une part, l'ordonnancement d'allumage/extinction de chaque centrale sur toute la période considérée, et d'autre part, la production de chaque centrale pour toute date où elle est allumée ; de façon à satisfaire l'ensemble des **deux contraintes globales** (demandeprévisionnelle et réserve de 10% modélisant l'incertitude de la prévision) etde **trois contraintes techniques** propres à chaque générateur: puissancebornée, temps minimum d'arrêt avant redémarrage, temps minimum defonctionnement avant extinction. La fonction de coût d'un générateur comprendun coût de fonctionnement légèrement quadratique auquel s'ajoute un coût dedémarrage dépendant de la durée d'arrêt d'une centrale que l'on allume. Nousréalisons une relaxation lagrangienne en dualisant les contraintes globales, etnous résolvons le problème ainsi relâché par programmation dynamique aprèsavoir précalculé pour chaque date la production optimale connaissant lesmultiplicateurs de Lagrange. La programmation dynamique permet également lecalcul du coût réduit nécessaire pour compenser le viol de certainescontraintes pour litération suivante. Ceci nous fournit une **borne dualeadditive** que nous exploitons dune part pour améliorer la borne inférieure,et d'autre part pour filtrer des variables par Programmation Par Contraintes encours du processus dénumération implicite. Les coûts réduits sont également exploités pour guider le choix des couples(variable valeur) dans la phase de séparation du Branch and Bound

Towards stochastic constraint programming: A study of online multi-choice knapsack with deadlines

Abstract. Constraint Programming (CP) is a very general programming paradigm that proved its efficiency on solving complex industrial problems. Most real-life problems are stochastic in nature, which is usually taken into account through different compromises, such as applying a deterministic algorithm to the average values of the input, or performing multiple runs of simulation. Our goal in this paper is to analyze different techniques taken either from practical CP applications or from stochastic optimization approaches. We propose a benchmark issued from our industrial experience, which may be described as an On-Line Multi-Choice Knapsack with Deadlines. This benchmark is used to test a framework with four different dynamic strategies that utilize a different combination of the stochastic and combinatorial aspects of the problem. To evaluate the expected future state of the reservations at the time horizon, we either use simulation, average values, systematic study of the most probable scenarios, or yield management techniques. 1

Using Constraint Programming for the Urban Transit Crew Rescheduling Problem

International audienceScheduling urban and trans-urban transportation is an important issue for industrial societies. The Urban Transit Crew Scheduling Problem is one of the most important optimization problem related to this issue. It mainly relies on scheduling bus drivers' workday respecting both collective agreements and the bus schedule needs. If this problem has been intensively studied from a tactical point of view, its operational aspect has been neglected while the problem becomes more and more complex and more and more prone to disruptions. In this way, this paper presents how the constraint programming technologies are able to recover the tactical plans at the operational level in order to efficiently help in answering regulation needs after disruptions. 1 Context and opportunities Scheduling urban and trans-urban transportation is an important issue for industrial societies. Several aspects are considered by territorial collectivities and transportation operators: human and material resource for, environmental and social constraint enforcement , user need requirements. Basically, there exists two central problems: transit scheduling (vehicles-buses, trains, tramways-planning on routes) and driver duty planning (assigning crew to those routes). These problems become more and more complex (regulation, network expansion) and more and more prone to disruptions (city events, accidents, resource failures) in the operational phase. This is the context in which the Urban Transit Crew Scheduling Problem (UTCSP) has been introduced [5]. In our proposal, we have to schedule bus drivers' workdays according to several constraints mainly related to: (1) collective agreements, e.g. breaking rules (basically, how long a bus driver can work before a break) ; (2) the bus schedule itself, e.g. chaining rules (geographic position, schedule compatibility between two tasks, etc.). From a tactical point of view, the UTCSP has been thoroughly studied both at the academic and industrial levels. Primarily, the technologies derived from mathematical programming (from integer linear programming to column generation and dynamic programming) dominate the literature and offer satisfying results. The problem is mainly solved using a set covering approach that represents bus schedule as a set of tasks, linked together by chaining rules and respecting the breaking rules. From an operational point of view, these technologies become useless due to their time consumption. Such a pitfall leads the operators to manually repair the tactical solutions after