Face Recognition: Bias and Privacy Concerns
By: Chris Dare
Tags: Privacy Protection, Artificial Intelligence, Bias, Privacy, Face Recognition

Privacy risks

The law also needs to grow with the ubiquity of face recognition in order to meet the security and privacy threats that this technology faces. One such is deepfakes. Deepfakes use deep learning, a prominent form of artificial intelligence, to map the face of one person unto another person. Think of it like swapping faces — not only in photos, but videos as well. One of the most famous deepfake videos on the sane internet is the impersonation of Barack Obama by Jordan Peele…and oh boy, this version of Obama said some interesting things[6]. The prominence of deepfakes in 2019 also brought attention — as well as criticism; privacy and security concerns — to facial recognition technologies — especially in China where face recognition has been widely adopted for many important use cases in governance and business (e.g. surveillance and payment authorization) [7]. The Beijing News in particular, highlights the situation in China. They reveal that “artificial intelligence is not only a technical problem, but also a governance examination question”. They allude that personal privacy is growing scarce especially with the advent of face recognition and the emergence of deepfakes as seen in the Chinese app Zao (at the time of their writing). What happens when someone steals your face and uses it for nefarious purposes? The news agency concluded that legislation needs to catch up with artificial intelligence and companies currently need to observe ethical concerns in the design and use of AI models for face recognition[8]. (Now it’s important to note, that China is currently in the process of enacting their data protection and privacy law.)

Final thoughts

The fight to ensure a fair and balanced use of facial recognition technologies is not yet over. However there have been promising responses and results. We have since seen corporations respond to this call. Companies such as Alipay have added additional verification to determine whether an image being used to verify a payment is from a video, image or a fresh live scan of a person’s face[9]. That’s a great step to progress. Next up would be accurately detecting deepfakes. In the US, Amazon also published best practices for using its facial recognition API [10]. Following police brutality against people of color earlier this year (2020) and the accompanying protests, big tech companies such as Amazon, Microsoft and IBM have discontinued programs providing law enforcement agencies with access to their facial recognition technologies. While these decisions have been implemented in many flavors, it is still a step in the right direction. Many critics do, however, deliberate whether this will only be a 9-day wonder. [11]

Face recognition, just like many other technologies, promises a great way to ease our living and activities. However, until we develop the right laws and policies to view and manage this technology, it may cause more harm than good. There are many lessons we can learn from our experiences with good and bad cases of the use of this technology. Ultimately, it is of uttermost importance that the voice of the public is highly considered in the design and deployment of such technologies.

Joy’s poem

“Obliged to you for hearing me, and now old Sojourner ain’t got nothing more to say.” — Sojourner Truth.

I’ll conclude with Joy’s poem: “AI, ain’t I a woman?” Enjoy:

Other notes

A lot has happened since I drafted this article in October 2020. A lot will happen after I publish it as well. I will use this section to provide additional notes which I believe are relevant and, as Jack Dorsey puts it, “point to a broader conversation” on this topic. I will also hyperlink references here instead of using IEEE style as I feel they are more appropriate for this exploratory section of the article.

November 2020

We had a talk concerning Bias in AI at CMU-Africa with the GIZ’s FAIR Forward and Stanford’s Renata Avila. Renata’s presentation was very informative. As aspiring researchers and policy makers, our students took pointers concerning designing and developing ‘fair machine learning models’. Amongst the many things she discussed, she shared information on efforts some researchers are making to mitigate bias in AI models. If you’re interested in learning more about it, read it here.

December 2020

I was just about to publish this article when the bomb dropped: Timnit Gebru gets ‘fired’ from Google. $%#@!?!

Backstory: Timnit Gebru is a world renowned AI researcher best known for her work in the bias and ethics of AI. I mention her paper concerning accuracy disparities in gender classification in this article[4]. Timnit and a few colleagues were asked by staff at Google to dissociate Google from a paper they co-authored for some reasons. Timnit gave them an ultimatum to drop it or she leaves. Google agreed to the latter.

The details of her departure from Google are out of the scope of my article and there’s also a lot we still don’t know surrounding these circumstances. Consequently, I will refrain from giving or linking details so as not to accidentally spread misinformation.

But here’s why I bring this up: Because of how important Timnit’s work is in the area of bias in AI, I believe this will draw a lot of attention to her papers…which is a good thing. Hopefully, we will still see some collaboration between Jeff Dean and Timnit in the future; Twitter convos, at least. The paper she recently co-authored is titled “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” If you have a copy/link to the preprint, kindly leave it as a comment to this article. I look forward to reading it myself.

If you’d like to read Timnit’s other papers, check them out at PapersWithCode.

What a way to enter Neurips this week though!

New note to data scientists researchers, ML engineers/enthusiasts:

NeurIPS 2020!!

At the time I’m writing this NeurIPS 2020 (nips) has just kicked off!!! Super exciting! (Unfortunately for me, I have to juggle between nips and CMU homeworks :-\ ). We’re 40 minutes into nips and both ongoing presentations are hitting the nail on the head when it comes to fairness in AI. Speakers from Netflix and Scikit-learn are discussing ways to mitigate bias in reinforcement learning (Netflix) as well as traditional machine learning (sci-kit learn). I would try to provide updates on nips with respect to bias and privacy sometime later. FYI, p(doing_this)=0.4

References