3 ms·

The discussion mentioned the Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision at CVPR 2020. The tutorial was recorded in three par

The discussion mentioned the Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision at CVPR 2020. The tutorial was recorded in three parts. The third part[1] includes solutions. These are either direct quotes or paraphrases of the bullet points contained therein. 1. Disaggregated Evaluations[2][3], Counterfactual Testing[4], Interpretability Methods[5] 2. Recognize [l]imitations(sic) of technical approaches (this discussion) 3. Model documentation frameworks[2] 4. Standardized framework for transparent dataset documentation [6] 5. Positionality awareness [7] 6. Actively follow the perspectives of people in marginalized groups 7. Make intentional design choices to privilege the perspectives of marginalized stakeholders who are at most risk of being harmed by the technology we develop 8. Value interdisciplinary and 'non-technical' work Note: These are some of the sources given as examples. Some are omitted for the sake of time. Also note: Just because there are points doesn't obligate you to agree with them [1]. https://www.youtube.com/watch?v=vpPpwa7W93I&t=1499s https://www.youtube.com/watch?v=vpPpwa7W93I&t=1499s [2] https://arxiv.org/pdf/1810.03993.pdf https://arxiv.org/pdf/1810.03993.pdf [3] http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a... [4] https://research.google/pubs/pub46743/ https://research.google/pubs/pub46743/ [5] https://arxiv.org/pdf/1711.11443.pdf https://arxiv.org/pdf/1711.11443.pdf [6] https://arxiv.org/pdf/1803.09010.pdf https://arxiv.org/pdf/1803.09010.pdf [7] https://dl.acm.org/doi/abs/10.1145/3351095.3375666 https://dl.acm.org/doi/abs/10.1145/3351095.3375666

imperfect_blue 6y ago
To clarify, The Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision is not a technical presentation, it is very much a intersectionalistic take on the topic, more concerned with power, oppression, and social justice than engineering. Suggested technical solutions [1 above] was exactly one sentence and the only sentence in the presentation that suggested any sort of technical approach, presented without elaboration, and the recommended solutions like Counterfactual Testing are also methods for fixing the training data. The remaining 99% of the presentation was spent on the idea that if AI engineers and scientists were more diverse and attended more diversity training, the models they produce hopefully wouldn't be biased.

imperfect_blue 6y ago

To clarify, The Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision is not a technical presentation, it is very much a intersectionalistic take on the topic, more concerned with power, oppression, and social justice than engineering. Suggested technical solutions [1 above] was exactly one sentence and the only sentence in the presentation that suggested any sort of technical approach, presented without elaboration, and the recommended solutions like Counterfactual Testing are also methods for fixing the training data. The remaining 99% of the presentation was spent on the idea that if AI engineers and scientists were more diverse and attended more diversity training, the models they produce hopefully wouldn't be biased.