ODI’s Director of Research Professor Elena Simperl.

In this session, Lora Aroya highlights the importance of culturally-aware and society-centred research on the data used in training and evaluating machine models and fostering responsible AI deployment in diverse sociocultural contexts. She presents a number of data-centric use cases that illustrate the inherent ambiguity of content and natural diversity of human perspectives. These cause unavoidable disagreement that needs to be treated as signal and not noise.

Lora will present her research for 30 minutes, and this will be followed by a Q&A and discussion.

This webinar is perfect for anyone who wants to gain expertise on data-centric AI, and is interested in the importance of data in creating safe, trustworthy and responsible AI.

Session abstract: the many faces of responsible AI

Conventional machine learning paradigms often rely on binary distinctions between positive and negative examples, disregarding the nuanced subjectivity that permeates real-world tasks and content. This simplistic dichotomy has served us well so far, but because it obscures the inherent diversity in human perspectives and opinions, as well as the inherent ambiguity of content and tasks, it poses limitations on model performance aligned with real-world expectations. This becomes even more critical when we study the impact and potential multifaceted risks associated with the adoption of emerging generative AI capabilities across different cultures and geographies. To address this, we argue that to achieve robust and responsible AI systems we need to shift our focus away from a single point of truth and weave in a diversity of perspectives in the data used by AI systems to ensure the trust, safety and reliability of model outputs.

In this session, Lora Aroya presents a number of data-centric use cases that illustrate the inherent ambiguity of content and natural diversity of human perspectives that cause unavoidable disagreement that needs to be treated as signal and not noise. This leads to a call for action to establish culturally-aware and society-centered research on impacts of data quality and data diversity for the purposes of training and evaluating ML models and fostering responsible AI deployment in diverse sociocultural contexts.

This session is the first in our Data-Centric AI webinar series.

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Please note:

The Zoom link for the event will be emailed to all bookers.

This event will be filmed, if you do not wish to be filmed please keep your camera turned off.

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