Causal Structure Learning of Bias for Fair Affect Recognition

Download

index.pdf

Date

2023-01-01

Author

Cheong, Jiaee
Kalkan, Sinan
Gunes, Hatice

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

95
views

28
downloads

The problem of bias in facial affect recognition tools can lead to severe consequences and issues. It has been posited that causality is able to address the gaps induced by the associational nature of traditional machine learning, and one such gap is that of fairness. However, given the nascency of the field, there is still no clear mapping between tools in causality and applications in fair machine learning for the specific task of affect recognition. To address this gap, we provide the first causal structure formalisation of the different biases that can arise in affect recognition. We conducted a proof of concept on utilising causal structure learning for the post-hoc understanding and analysing bias.

URI

https://hdl.handle.net/11511/102760

DOI

https://doi.org/10.1109/wacvw58289.2023.00038

Conference Name

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

The Hitchhiker's Guide to Bias and Fairness in Facial Affective Signal Processing: Overview and techniques Cheong, Jiaee; Kalkan, Sinan; Gunes, Hatice (2021-11-01) Given the increasing prevalence of facial analysis technology, the problem of bias in the tools is now becoming an even greater source of concern. Several studies have highlighted the pervasiveness of such discrimination, and many have sought to address the problem by proposing solutions to mitigate it. Despite this effort, to date, understanding, investigating, and mitigating bias for facial affect analysis remain an understudied problem. In this work we aim to provide a guide by 1) providing an overview o...
Behavioral consequences of the third-person effect on turkish voters İz, Bennur; Öner Özkan, Bengi; Department of Psychology (2008) The third-person effect is the tendency of individuals to believe that others are more susceptible to media influence than themselves and this perception causes them to act accordingly. This study aimed to reveal the relationship between the third-person effect and voting intentions. After reading one of the two versions of a vignette about a media discussion of possible election results, both of which claimed only two major parties could pass the election threshold, Turkish university students (N=285) firs...
Implicit evaluations about driving skills predicting driving performance Bicaksiz, Pinar; Harma, Mehmet; Dogruyol, Burak; Lajunen, Timo; Özkan, Türker (Elsevier BV, 2018-04-01) Self-reported measures of driving skills have the potential shortcomings of the general self report methodology such as social responding and self-enhancement biases. In the present study, the Implicit Association Test (IAT) procedure was adapted to measure the implicit evaluations of driving skills. The performance of IAT and an explicit, self-report measure of driving skills were compared in predicting driver behaviors and performance. Ninetyone Turkish male drivers participated in the study. The results ...
Visual perspective in causal attribution, empathy and attitude change Onder, OM; Öner Özkan, Bengi (SAGE Publications, 2003-12-01) The aim of the present study was to test the effect of visual perspective on the actor-observer bias. For this aim, we examined the effects of different visual perspectives on individuals' external and internal attributions. In addition to this, we examined the presence or absence of an attitude change toward the death penalty due to participants' visual perspective. One week before the experiment, we measured the participants' attitudes toward the death penalty. Then, during the experiment, films produced ...
An association rule mining model for the assessment of the correlations between the attributes of severe accidents Ayhan, Bilal Umut; Dogan, Neset Berkay; Tokdemir, Onur Behzat (Vilnius Gediminas Technical University, 2020-01-01) Identifying the correlations between the attributes of severe accidents could be vital to preventing them. If such relationships were known dynamically, it would be possible to take preventative actions against accidents. The paper aims to develop an analytical model that is adaptable for each type of data to create preventative measures that will be suitable for any computational systems. The present model collectively shows the relationships between the attributes in a coherent manner to avoid severe acci...

Citation Formats

J. Cheong, S. Kalkan, and H. Gunes, “Causal Structure Learning of Bias for Fair Affect Recognition,” presented at the 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023, Hawaii, Amerika Birleşik Devletleri, 2023, Accessed: 00, 2023. [Online]. Available: https://hdl.handle.net/11511/102760.