ΑΙhub.org
 

Tutorial on fairness, accountability, transparency and ethics in computer vision

by
14 July 2020



share this:

CVPR FATE
The Computer Vision and Pattern Recognition conference (CVPR) was held virtually on 14-19 June. As well as invited talks, posters and workshops, there were a number of tutorials on a range of topics. Timnit Gebru and Emily Denton were the organisers of one of the tutorials, which covered fairness, accountability, transparency and ethics in computer vision.

As the organisers write in the introduction to their tutorial, computer vision is no longer a purely academic endeavour; computer vision systems have been utilised widely across society. Such systems have been applied to law enforcement, border control, employment and healthcare.

Seminal works, such as the Gender Shades project (read the paper here), and organisations campaigning for equitable and accountable AI systems, such as The Algorithmic Justice League, have been instrumental in encouraging a rethink from some big tech companies regarding facial recognition systems, with Amazon, Microsoft and IBM all announcing that they would (for the time being) stop selling the technology to police forces.

This tutorial helps lay the foundations for community discussions about the ethical considerations of some of the current use cases of computer vision technology. The presentations also seek to highlight research which focusses on uncovering and mitigating issues of bias and historical discrimination.

The tutorial comprises three parts, to be watched in order.

Part 1: Computer vision in practice: who is benefiting and who is being harmed?

Speaker: Timnit Gebru

Part 2: Data ethics

Speakers: Timnit Gebru and Emily Denton

Part 3: Towards more socially responsible and ethics-informed research practices

Speaker: Emily Denton

Following the tutorial there was a panel discussion, moderated by Angjoo Kanazawa, which you can watch below.




AIhub is dedicated to free high-quality information about AI.
AIhub is dedicated to free high-quality information about AI.




            AIhub is supported by:


Related posts :



AIhub monthly digest: November 2023 – deconstructing sentiment analysis, few-shot learning for medical images, and Angry Birds structure generation

Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
29 November 2023, by

An introduction to science communication at #NeurIPS2023

Find out more about our short course to be held in-person at NeurIPS on Monday 11 December.
28 November 2023, by

Co-creating better images of AI

In July, 2023, Science Gallery London and the London Office of Technology and Innovation co-hosted a workshop helping Londoners think about the kind of AI they want.
27 November 2023, by

The power of collaboration: power grid control with multi-agent reinforcement learning

A promising AI tool for assisting network operators in their real-time decision-making and operations

Goal representations for instruction following

How can we reconcile the ease of specifying tasks through natural language-based approaches with the performance improvements of goal-conditioned learning?
23 November 2023, by

A comprehensive survey on rare event prediction

We review the rare event prediction literature and highlight open research questions and future directions in the field.





©2021 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association