Artificial intelligence tools to promote social good in gig markets
Notice bibliographique
Résumé
The Artificial Intelligence (A.I.) industry has been essential to creating new jobs for the deployment of real-world solutions. As a result, the implementation of these new jobs involves the execution of multiple human intelligence micro-tasks, such as data labeling tasks for training Machine Learning models. The workers who perform those tasks, also known as crowd workers, usually are independent workers within crowdsourcing platforms. These platforms are subject to the free market, where the forces of supply and demand produce various power dynamics among stakeholders. As a result, disassociation between stakeholders often generates unbalanced power dynamics where workers are paid below minimum wage and are intimidated to keep their reputation or face termination. Within this thesis, I introduce computational techniques to audit the workplace conditions of crowd workers and design tools to address these power imbalances, as a positive and more efficient alternative for the labor conditions of crowd workers. Developing these objectives through the design and evaluation of tools in digital labor platforms, the first "Invisible Labor Tracker'' is a web browser plugin that audits and brings light to an important power dynamic: forcing others to do invisible labor (i.e., do unpaid tasks). Through my tool, I discovered that workers dedicate on average a third of their time to invisible labor, with a very large portion being used to check their payments, as well as being vigilantly on call for "good employers''. The second system, "Reputation Agent'', is an intelligent tool that helps workers to address power dynamics around being unjustly evaluated. The system detects when employers write unfair evaluations about workers, and in such cases, the tool prompts employers to reflect and focus on the performance metrics that are within workers' control. My third system, called "CultureFit'', is an intelligent tool that addresses power dynamics around workers having to change culturally for employers. Instead of forcing workers to change, my system detects a crowd worker's cultural background and then learns the type of cultural interface settings that are best suited to dispatch labor to the worker. Throughout my thesis, I will demonstrate the sustainability of systems that point to a future where A.I. can be used to audit and address power imbalances in the workplace. --Author's abstract
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».