The ethical questions that haunt facial-recognition research

Noorden, Richard Van

doi:10.1038/d41586-020-03187-3

Cited by 87 publications

(75 citation statements)

References 10 publications

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“…Researchers cannot access proprietary algorithms, but patents show how these methods operate, in considerable detail (Table 2). While one might think that those are intrusive, some countries have now adopted facial recognition technology nationally [139], and suggestions are already being made, only slightly tongue-in-cheek, as to how marketers will shortly sell globalised individual personal data [140].…”

Section: Generalist Algorithms Including Tourismmentioning

confidence: 99%

Assortative Matching of Tourists and Destinations: Agents or Algorithms?

Buckley

Cooper²

2021

Sustainability

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We propose that assortative matching, a well-established paradigm in other industry sectors and academic disciplines, can underpin the concept of destination matching. This provides a new foundation to integrate research concepts and terminology in destination marketing and destination choice. We argue that the commercial tourism industry already applies destination matching approaches, with three historical phases. Initially, matching of tourists and destinations relied on the tacit expertise of specialist agents. This still applies in specialist subsectors. For generalist travel and accommodation, human agents were partially replaced by online travel agents, OTAs, which are customised algorithms operating only in the travel sector. These still exist, but their share price trends suggest decreasing significance. Currently, automated assortative algorithms use multiple sources of digital data to push appealing offers to potential purchasers, across all retail sectors. Digital marketing strategies for tourism products, enterprises, and destinations are now just one category of generalised product–purchaser matching, using entirely automated algorithms. Researchers do not have access to proprietary algorithms, but we can identify which components they incorporate by analysing their underlying patents. We propose that theories of destination marketing and choice need to reflect these recent and rapid real-world changes via deliberate analysis of destination matching.

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Section: Generalist Algorithms Including Tourismmentioning

confidence: 99%

Assortative Matching of Tourists and Destinations: Agents or Algorithms?

Buckley

Cooper²

2021

Sustainability

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“…Editorial boards of peer-reviewed journals and peer-review conferences can also require sophisticated ethical analysis when selecting papers. The journal Nature Machine Intelligence is considering requiring authors to include a statement on the broader societal impacts and ethics of their work-but this has yet to be implemented [63]. The Lancet has pledged to advance racial equality through editorial oversight in publications [64] and also to conduct editorial checks for appropriate use of sex and gender analysis before accepting manuscripts for publication [65].…”

Section: Conferences and Peer-reviewed Journalsmentioning

confidence: 99%

Ensuring that biomedical AI benefits diverse populations

Zou

Schiebinger

2021

EBioMedicine

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Artificial Intelligence (AI) can potentially impact many aspects of human health, from basic research discovery to individual health assessment. It is critical that these advances in technology broadly benefit diverse populations from around the world. This can be challenging because AI algorithms are often developed on non-representative samples and evaluated based on narrow metrics. Here we outline key challenges to biomedical AI in outcome design, data collection and technology evaluation, and use examples from precision health to illustrate how bias and health disparity may arise in each stage. We then suggest both short term approaches-more diverse data collection and AI monitoring-and longer term structural changes in funding, publications, and education to address these challenges.

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“…Distinct works focus on hand gesture [12], facial recognition, affective states, however there are a very few works on elaborating the available dataset on assessing various cognitive levels of understanding students emotional state for engagement and distraction. There are distinctive doubts on the curation and usage of facial data in facial recognition [13]. Few researchers have captured emotions under controlled environments while the subjects watch videos of different emotions [14][15][16][17][18].…”

Section: Related Workmentioning

confidence: 99%

Engagement Detection through Facial Emotional Recognition Using a Shallow Residual Convolutional Neural Networks

Thiruthuvanathan¹,

Balachandran²,

Rangaswamy³

2021

IJIES

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Online teaching and learning has recently turned out to be the order of the day, where majority of the learners undergo courses and trainings over the new environment. Learning through these platforms have created a requirement to understand if the learner is interested or not. Detecting engagement of the learners have sought increased attention to create learner centric models that can enhance the teaching and learning experience. The learner will over a period of time in the platform, tend to expose various emotions like engaged, bored, frustrated, confused, angry and other cues that can be classified as engaged or disengaged. This paper proposes in creating a Convolutional Neural Network (CNN) and enabling it with residual connections that can enhance the learning rate of the network and improve the classification on three Indian datasets that predominantly work on classroom engagement models. The proposed network performs well due to introduction of Residual learning that carries additional learning from the previous batch of layers into the next batch, Optimized Hyper Parametric (OHP) setting, increased dimensions of images for higher data abstraction and reduction of vanishing gradient problems resulting in managing overfitting issues. The Residual network introduced, consists of a shallow depth of 50 layers which has significantly produced an accuracy of 91.3% on ISED & iSAFE data while it achieves a 93.4% accuracy on the Daisee dataset. The average accuracy achieved by the classification network is 0.825 according to Cohens Kappa measure.

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The ethical questions that haunt facial-recognition research

Cited by 87 publications

References 10 publications

Assortative Matching of Tourists and Destinations: Agents or Algorithms?

Assortative Matching of Tourists and Destinations: Agents or Algorithms?

Ensuring that biomedical AI benefits diverse populations

Engagement Detection through Facial Emotional Recognition Using a Shallow Residual Convolutional Neural Networks

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