Bias in Technology
Despite our best intentions, biases – be they from our subconscious, or influenced by culture and history – can find their way into our work.
In this webinar, we’ll look at examples of how unintentional biases are detected and avoided in the things we design and engineer.
Dr Renate Baumgartner
Bias in Technology
Technology may seem objective, at least more objective than humans. From a socio-technical point of view, however, we know that people inscribe their values and norms in technologies. This talk will explain what we mean by cultural/social/historical bias, how it permeates technologies and what we should be wary about, when developing and employing technologies. Drawing on examples from artificial intelligence, we will also learn some ways in which bias can be mitigated.
Dr Patricia Xavier
Can dissonance between engineering mindsets and justice impede responsible decision making?
Inherent in any engineering system are issues of power and oppression, who benefits and who is impacted. Consider cases of data bias driving discrimination and the lack of action in the sector proportionate to the threat from the deteriorating climate. This presentation discusses how well engineers are trained to engage in discussions of justice, power and discrimination by interrogating the values and norms of engineering culture. In exploring the ontological gaps between engineering and justice, we can see how some engineering teaching methods can damage our ability to practice inclusively, and find ways to incorporate more justice into our work.
Victor Ochoa-Gutierrez
Race and Gender Bias in Medical Devices, case:
Pulse Oximetry Technology
The current COVID-19 pandemic has shown us that the pulse oximeter is a key medical device for monitoring blood-oxygen levels non-invasively in patients with chronic or acute illness. It has also emphasized limitations in accuracy for individuals with high skin pigmentation and woman, calling for new methods to provide better oxygen measurements. Is it possible to eradicate racial and gender bias in the clinical application?
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