VEGA: a Virtual Environment for Exploring Gender Bias vs. Accuracy Trade-offs in AI Translation Services

Bernagozzi, Mariana; Srivastava, Biplav; Rossi, Francesca; Usmani, Sheema

doi:10.1609/aaai.v35i18.17991

Cited by 3 publications

(1 citation statement)

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“…In (Srivastava andRossi 2020, 2018), we proposed a technique to rate automated machine language translators for gender bias. Further, we created visualizations to communicate ratings (Bernagozzi et al 2021b) to users, and conducted user studies to determine how users perceive trust in the presence of such ratings (Bernagozzi et al 2021a).…”

Section: Initial Approach -Rating Machine Translatorsmentioning

confidence: 99%

Advances in Automatically Rating the Trustworthiness of Text Processing Services

Srivastava¹,

Kausik²,

Bernagozzi³

et al. 2023

Preprint

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AI services are known to have unstable behavior when subjected to changes in data, models or users. Such behaviors, whether triggered by omission or commission, lead to trust issues when AI works with humans. The current approach of assessing AI services in a black box setting, where the consumer does not have access to the AI's source code or training data, is limited. The consumer has to rely on the AI developer's documentation and trust that the system has been built as stated. Further, if the AI consumer reuses the service to build other services which they sell to their customers, the consumer is at the risk of the service providers (both data and model providers). Our approach, in this context, is inspired by the success of nutritional labeling in food industry to promote health and seeks to assess and rate AI services for trust from the perspective of an independent stakeholder. The ratings become a means to communicate the behavior of AI systems so that the consumer is informed about the risks and can make an informed decision. In this paper, we will first describe recent progress in developing rating methods for text-based machine translator AI services that have been found promising with user studies. Then, we will outline challenges and vision for a principled, multi-modal, causality-based rating methodologies and its implication for decision-support in real-world scenarios like health and food recommendation.

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Section: Initial Approach -Rating Machine Translatorsmentioning

confidence: 99%