5 research outputs found
Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms
Bias evaluation benchmarks and dataset and model documentation have emerged
as central processes for assessing the biases and harms of artificial
intelligence (AI) systems. However, these auditing processes have been
criticized for their failure to integrate the knowledge of marginalized
communities and consider the power dynamics between auditors and the
communities. Consequently, modes of bias evaluation have been proposed that
engage impacted communities in identifying and assessing the harms of AI
systems (e.g., bias bounties). Even so, asking what marginalized communities
want from such auditing processes has been neglected. In this paper, we ask
queer communities for their positions on, and desires from, auditing processes.
To this end, we organized a participatory workshop to critique and redesign
bias bounties from queer perspectives. We found that when given space, the
scope of feedback from workshop participants goes far beyond what bias bounties
afford, with participants questioning the ownership, incentives, and efficacy
of bounties. We conclude by advocating for community ownership of bounties and
complementing bounties with participatory processes (e.g., co-creation).Comment: To appear at AIES 202
Using Deep Learning to Detect Facial Markers of Complex Decision Making
In this paper, we report on an experiment with The Walking Dead (TWD), which is a narrative-driven adventure game where players have to survive in a post-apocalyptic world filled with zombies. We used OpenFace software to extract action unit (AU) intensities of facial expressions characteristic of decision-making processes and then we implemented a simple convolution neural network (CNN) to see which AUs are predictive of decision-making. More specifically, this study aims to identify the facial regions that are predictive of decision-making. Our results provide evidence that the pre-decision variations in action units 17 (chin raiser), 23 (lip tightener), and 25 (parting of lips) are predictive of decision-making processes. Furthermore, when combined, their predictive power increased up to .81 accuracy on the test set; we offer speculations about why it is that these particular three AUs were found to be connected to decision-making. Our results also suggest that machine learning methods in combination with video games may be used to accurately and automatically identify complex decision-making processes using AU intensity alone. Finally, our study offers a new method to test specific hypotheses about the relationships between higher-order cognitive processes and behavior, which relies on both narrative video games and easily accessible software, like OpenFace
Using Deep Learning to Detect Facial Markers of Complex Decision Making
In this paper, we report on an experiment with The Walking Dead (TWD), which is a narrative-driven adventure game where players have to survive in a post-apocalyptic world filled with zombies. We used OpenFace software to extract action unit (AU) intensities of facial expressions characteristic of decision-making processes and then we implemented a simple convolution neural network (CNN) to see which AUs are predictive of decision-making. More specifically, this study aims to identify the facial regions that are predictive of decision-making. Our results provide evidence that the pre-decision variations in action units 17 (chin raiser), 23 (lip tightener), and 25 (parting of lips) are predictive of decision-making processes. Furthermore, when combined, their predictive power increased up to .81 accuracy on the test set; we offer speculations about why it is that these particular three AUs were found to be connected to decision-making. Our results also suggest that machine learning methods in combination with video games may be used to accurately and automatically identify complex decision-making processes using AU intensity alone. Finally, our study offers a new method to test specific hypotheses about the relationships between higher-order cognitive processes and behavior, which relies on both narrative video games and easily accessible software, like OpenFace