What Is Beautiful Continues to Be Good

Matsangidou, Maria; Otterbacher, Jahna

doi:10.1007/978-3-030-29390-1_14

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…In the HCI literature, auditing often involves characterizing the behavior of the algorithm from a user perspective. For instance, in Matsangidou and Otterbacher [94], the authors consider the inferences on physical attractiveness made by image tagging algorithms on images of people. They audited the output of four image recognition APIs on standardized portraits of people across genders and races.…”

Section: Auditing Approaches For Bias Detectionmentioning

confidence: 99%

Mitigating Bias in Algorithmic Systems -- A Fish-Eye View

Orphanou,

Otterbacher,

Kleanthous

et al. 2021

Preprint

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Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. Given the complexity of the problem and the involvement of multiple stakeholders -including developers, end-users and third-parties -there is a need to understand the landscape of the sources of bias, and the solutions being proposed to address them. This survey provides a "fish-eye view," examining approaches across four areas of research. The literature describes three steps toward a comprehensive treatment -bias detection, fairness management and explainability management -and underscores the need to work from within the system as well as from the perspective of stakeholders in the broader context.

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Section: Auditing Approaches For Bias Detectionmentioning

confidence: 99%

Mitigating Bias in Algorithmic Systems -- A Fish-Eye View

Orphanou,

Otterbacher,

Kleanthous

et al. 2021

Preprint

Self Cite

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“…These characteristics can have an influence on how people are perceived by others. For instance, it is well documented in studies, such as that of Matsangidou and Otterbacher [13], that those who are perceived to be more attractive are assumed to possess more desirable personality traits and tend to be more successful occupationally. One very early study conducted by Steffensmeier et al [14] found that individuals who were dressed in "hippy"-style clothing were significantly more likely to be reported by other customers for shoplifting than those who were wearing "straight" clothing such as a suit and tie.…”

Section: Physical Appearancementioning

confidence: 99%

Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem

Reid

Coleman

Vance

et al. 2021

Sensors

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Retail shoplifting is one of the most prevalent forms of theft and has accounted for over one billion GBP in losses for UK retailers in 2018. An automated approach to detecting behaviours associated with shoplifting using surveillance footage could help reduce these losses. Until recently, most state-of-the-art vision-based approaches to this problem have relied heavily on the use of black box deep learning models. While these models have been shown to achieve very high accuracy, this lack of understanding on how decisions are made raises concerns about potential bias in the models. This limits the ability of retailers to implement these solutions, as several high-profile legal cases have recently ruled that evidence taken from these black box methods is inadmissible in court. There is an urgent need to develop models which can achieve high accuracy while providing the necessary transparency. One way to alleviate this problem is through the use of social signal processing to add a layer of understanding in the development of transparent models for this task. To this end, we present a social signal processing model for the problem of shoplifting prediction which has been trained and validated using a novel dataset of manually annotated shoplifting videos. The resulting model provides a high degree of understanding and achieves accuracy comparable with current state of the art black box methods.

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