Notice bibliographique
Résumé
Trust has, since the early stages of IBM's Autonomic Computing (AC) initiative, been recognized as an important factor in the success of new autonomic features. If operators do not trust the new automated tools, they will not use them -- no matter how useful or efficient they might be. Despite this stated awareness of trust as a major contributing factor to successful operator adoption of AC functionality (e.g., [11]), no clear process of explicitly designing for operator trust has emerged. The purpose of our research is to develop such a process, to provide a theoretically grounded method for designing for appropriate trust in automation. We define "appropriate trust" as it is described in [6]. By this definition, there are two components to appropriate trust. The first is proper calibration of trust, meaning that the operator trusts the automation to the degree of its capability, without over-trust or distrust. The second component is resolution of trust: the operator must be sensitive to different or changing conditions (functional or temporal) that might affect the ability of the automation to achieve the operator's goals.In our research, we have drawn on the extensive review of trust literature by Lee and See [6], who investigated the concept of trust as published from multiple perspectives (e.g., organizational, psychological, and interpersonal). Lee and See have developed a model of trust in automation, based on their review of the literature, which describes the feedback loops that inform one's attitude of trust (or distrust) towards automation. Furthermore, Lee and See identify a continuum of attributional abstraction - information based on which an operator may attribute a sense of trust in an automated tool. Three categories along this continuum are defined: purpose-, process-, and performance-related information are described as being necessary to achieving appropriate trust.Although they provide these categories of information, Lee and See [6] do not provide a process by which the appropriate information might be identified for a given automated tool. We hypothesized that Work Domain Analysis (WDA; [12]) might serve to provide a clear and definite list. WDA is part of a multi-stage analytic framework, developed for the analysis of complex socio-technical systems. It is a constraint-based, formative analysis, which describes the realm of possible actions, rather than a single prescribed path. The WDA, we reasoned, could be adapted and applied to the problem of design for appropriate trust in automation.In this paper, we will introduce the model of trust in automation described by [6]. We will also introduce WDA. We will then describe how this analysis can be applied to the question of trust in automation. Finally, we will present a case study from new automation in the IBM® DB2® Version 9.1 for Linux®, UNIX®, and Windows® product (DB2 V9.1), in which we applied WDA to identify specific information requirements for appropriate trust in the Self-Tuning Memory Manager, and used these findings to impact documentation and logging for this new automated functionality.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,023 |
| Communication savante | 0,017 | 0,017 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,026 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».