Lessons from the PULSE Model and Discussion

In this post I will attempt to summarize some elements of the recent discussion that happened among AI researchers as a result of bias found in the model associated with the paper "PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models". I will also offer my personal reflection on this discussion and on what can be learned from it with regards to the technical topic of bias in machine learning, best practices for AI research, and best practices for discussing such topics. I write this because I believe they are valuable lessons that are worth noting. Some of these lessons are more subjective than others, so I hope that even if you disagree with the perspective offered here you will still consider it.


Table of Contents

On the Value of Demos

It started with a Tweet announcing a Colab notebook containing a demo of the work presented in the recent paper "PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models":

This was soon responded to with a demonstration that the method appears to be biased in favor of outputting images of white people, with a concrete example of a pixelated image of Barack Obama:

The reaction to this bias, and the discussion concerning it, was loud enough that it was covered in the popular press with The Verge's "What a machine learning tool that turns Obama white can (and can’t) tell us about AI bias". While seeing yet another example of problematic bias in AI was not pleasant for anyone, I still think it’s good insofar as it did not simply go unnoticed and the conversation was had, so:

Lesson 1: interactive demos are useful to have in addition to plain source code, as they can allow people to easily interact with the model and potentially point out issues with it.

On Best Practices for Addressing Bias in AI Models

In response, the authors of the paper (Sachit Menon*, Alexandru Damian*, Shijia Hu, Nikhil Ravi, Cynthia Rudin) added a comment on the project GitHub repo:

The new section in the paper addresses the issue directly:

The first two paragraph of this section in paper.

As does the model card in the appendix:

This, in my opinion, was a well done response by the authors. Sections addressing limitations or ethical concerns with the research and the idea of model cards are both existing ideas advocated by researchers focused on the ethics of AI. In fact, the paper introducing the idea of model cards had an example of a model card for a smile detection model with very similar caveats as are found in this paper:

Therefore we can conclude:

Lesson 2: there exist recommended best practices for addressing potential bias in applied AI research, and the idea of model cards is particularly relevant.

On the Source of Bias in Machine Learning Systems

Meanwhile, the conversation around this topic on Twitter further took off based on a tweet by Dr. Yann LeCun that offered an explanation on why the bias in the model existed:

In response, Dr. Timnit Gebru, a leading researcher on fairness, accountability, and transparency in AI, noted that the idea that bias in ML systems comes only from data is incorrect:

She also noted the recent Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision at CVPR 2020 as an educational resource to better understand this topic:

Other AI researchers also replied with commentary on this:

A particularly relevant reply also pointed out the paper "Predictive Biases in Natural Language Processing Models:A Conceptual Framework and Overview" and noted that "we just published a study of the most common sources of bias in NLP systems, and data is only one of them":

Dr. LeCun responded to these points by clarifying he more specifically meant that data is the primary source of bias in most modern ML systems and that addressing issues in the data is the best way to approach such problems:

Which was met with skepticism:

Dr. LeCun generally responded by reasserting his position:

And further scrutiny of this stance followed:

Further discussion on the subject also occurred on reddit in the thread "[Discussion] about data bias vs inductive bias in machine learning sparked by the PULSE paper/demo". While people are still split on the conclusions to be drawn from this discussion and finer points that arose from it, at a high level at least this lesson can be drawn:

Lesson 3: data can be a source of bias in Machine Learning systems, but is not the only source, and the harms potentially caused by such systems can result from more than just flawed datasets.

On Etiquette For Public Debates

In reply to Dr. Gebru, Dr. LeCun responded by clarifying he didn’t mean that data is the only source of bias, and offered his thoughts on the sources of bias in AI in a Twitter thread:

He concluded this thread by calling for discussion to happen in a non-emotional way and for others to not assume bad intent from other people:

To which Dr. Gebru responded by disengaging:

And Dr. LeCun responded as follows:

Some criticized the thread from Dr. LeCun and characterized it as lecturing Dr. Gebru on her subject of expertise, as being condescending, and as involving Tone-Policing:

In partial response to this criticism, Dr. LeCun said the thread was “obviously not just for Timnit”, and acknowledged her expertise in this subject:

Which was met with this criticism:

Others defended Dr. LeCun:

This was largely also the stance of the YouTube video "[Drama] Yann LeCun against Twitter on Dataset Bias", and some comments in response to it, such as this one:

"We need more people like Yann defending their rational positions against ignorant mobs in public so we can be inspired to think for ourselves! Yann's contributions to humanity are immeasurable, if only the people jumping to attacks realized the pettyness of their actions..."

Further discussion of Dr. LeCun's response to Dr. Gebru followed:

Which led to responses that characterized Dr. LeCun's response as just an attempt at rational debate and dismissed the criticisms regarding Tone-Policing and Mansplaining as "social justice critique":

Which Dr. LeCun endorsed:

From the public Facebook post 

Dr. LeCun issued this statement soon after that:

The PULSE model and this exchange were later covered in VentureBeat with the article "A deep learning pioneer’s teachable moment on AI bias".

Regardless of which stance you agree with, it makes sense to at least understand the criticisms directed at Dr. LeCun. From Racism 101: Tone Policing:

“Tone policing describes a diversionary tactic used when a person purposely turns away from the message behind her interlocutor’s argument in order to focus solely on the delivery of it.
...
Here’s a good rule of thumb: when you are out of line, you don’t get to set the conditions in which a conversation can occur. That’s privilege at play. You need to truly listen to how and where you went wrong, and then do better in future.”

Regarding the criticisms of Dr. LeCun explaining the topic to Dr. Gebru despite her being an expert on it, it is worth knowing The Psychology of Mansplaining:

According to the Oxford English Dictionary editors, mansplaining is “to explain something to someone, typically a man to woman, in a manner regarded as condescending or patronizing” (Steinmetz, 2014). The American Dialect Society defines it as “when a man condescendingly explains something to female listeners” (Zimmer, 2013). Lily Rothman, in her “Cultural History of Mansplaining,” elaborates it as "explaining without regard to the fact that the explainee knows more than the explainer, often done by a man to a woman.”

Mansplaining as a portmanteau may be new, but the behavior has been around for centuries (Rothman, 2012). The scholarly literature has long documented gendered power differences in verbal interaction: Men are more likely to interrupt, particularly in an intrusive manner (Anderson and Leaper, 1998). Compared to men, women are more likely to be interrupted, both by men and by other women (Hancock and Rubin, 2015). Perhaps, in part, because they are accustomed to it, women also respond more amenably to interruption than men do, being more likely to smile, nod, agree, laugh, or otherwise facilitate the conversation (Farley, 2010).
...
Mansplaining is problematic because the behavior itself reinforces gender inequality. When a man explains something to a woman in a patronizing or condescending way, he reinforces gender stereotypes about women’s presumed lesser knowledge and intellectual ability.

This is especially true when the woman is, in fact, more knowledgeable on the subject.

Related to this is The Universal Phenomenon of Men Interrupting Women:

“Victoria L. Brescoll, associate professor of organizational behavior at the Yale School of Management, published a paper in 2012 showing that men with power talked more in the Senate, which was not the case for women. Another study, “Can an Angry Woman Get Ahead?” concluded that men who became angry were rewarded, but that angry women were seen as incompetent and unworthy of power in the workplace.“

To be clear, I am not claiming Dr. LeCun intended any disrespect in how he communicated or otherwise attributing any negative intent to him, but rather am explaining the criticisms of his response.

I have phrased the above as objectively as I could, because I really do want people uncomfortable with the criticism directed at Dr. LeCun to at least consider and try to understand it better. Some may question whether it's worth it to consider this exchange at such length, but research is a community endeavour, and norms of communication within the community therefore naturally matter. I will contain my personal perspective on this exchange to the following optional section, in case it may further help people consider these criticisms:


Further discussion of this exchange, from my perspective

I do think Dr. LeCun's response to Dr. Gebru deserves the scrutiny it received. From my perspective, this is a fair summary:

  1. Dr. LeCun tweets that “ML systems are biased when data is biased.” This can be interpreted in multiple ways, with one interpretation being that data is the only factor that matters, and another being that data is the main problem in this particular case.

  2. Dr. Gebru replies in an exasperated way noting that the first possible interpretation is incorrect and that experts such as her say this often. Implicitly, it's clear that this exasperation must be partially because this is a common and harmful misconception experts such as Dr. Gebru have to fight against.

  3. Dr. LeCun responds with

    “If I had wanted to 'reduce harms caused by ML to dataset bias', I would have said "ML systems are biased only when data is biased". But I'm absolutely not making that reduction. I'm making the point that in the particular case of this specific work, the bias clearly comes from the data.”

    This to me is a response in the form of "No, I did not meant that, clearly I meant this," and this seems to me like a defensive and rather hostile way to respond, in any response to criticism. It's a direct dismissal of Dr. Gebru's criticism, despite the interpretation that prompted it being perfectly reasonable, and does not acknowledge anything else in Dr. Gebru's statement.

  4. After many tweets discussing bias in Machine Learning and expanding on his original point in a direct reply to Dr. Gebru -- an expert on the subject -- Dr. LeCun concludes with

    "Post-scriptum: I think people like us should strive to discuss the substance of these questions in a non-emotional and rational manner. "

    and

    “It's also important to avoid assuming bad intent from your interlocutor.”

    To me, this suggests the underlying message “I was perfectly reasonable, and you are being irrationally negative towards me, and should calm down”. This is once again defensive and dismissive even though Dr. LeCun's response does not address most of Dr. Gebru's statement.

    In summary: if I am corrected by someone on a point, especially if that someone is an expert on the topic, I strive to listen to their point and acknowledge the cause for criticism (even if it was just due to ambiguous wording). It's easy to want to defend yourself, and sometimes appropriate, but acknowleding and listening should still be part of the response. I feel that was missing here, and this is important to point out because of Dr. LeCun's stature in this field and the potential for others to emulate his communication practices. A better response might have been a simple "It was not my intent to suggest that, thank you for pointing this out."

    I avoided the use of Mansplaining and Tone-Policing in writing this because I think using them would prejudice some to not be able to consider this perspective, although I do think they are appropriate descriptors in this case. I hold immense respect for Dr. LeCun and Dr. Gebru and once again make no claims about them personally here, but only what I have said concerning this particular exchange.


Regardless of whether agree with the criticisms directed at Dr. LeCun in this case, I believe all should be able to agree with the following:

Lesson 4: it is important to be able to have rational discussions of complicated topics such as bias and address disagreements respectfully. When responding to criticism by an expert on the topic in question in such a discussion, one way to be respectful is to be mindful of whether you are replying by needlessly explaining the topic back to this expert and being condescending (otherwise known as Mansplaining), or by not addressing the points made and instead criticizing an expression of emotion by the expert (otherwise known as Tone-Policing).

On the Responsibilities of AI Researchers

Another aspect of the discussion arose in response to this exchange:

Which itself led to many responses:

While others noted that researchers to some extent have to use flawed datasets to make progress:

"I guess I'm in that camp that's just a little bit confused about where and how to proceed. I'm getting anger and frustration but no clear, pragmatic recommendations of how to remedy that. The reality is that data acquisition is hard, and it can (and often is) incredibly expensive. If we required every researcher to acquire specific datasets, then ML research would inevitably suffer. I don't see how we can avoid what amounts to convenience sampling in the short-term.

So, is the suggestion that models/papers should be explicitly labeled as: (a) trained on a convenience sample not representative of real-world conditions and (b) "use at your own risk" due to bias? Personally, I think those two qualifiers would be sufficient for continuing research while taking some effort towards addressing your criticisms."
-Source

With the above coming from a more extensive discussion on reddit. Others replied noting that researchers definitely can take concrete actions to conduct their research more responsibly in light of bias in existing datasets:

"I understand that. Trust me, I have to justify my research funds to people with Bschool degrees. I would never say that collecting good data sets is easy.

But you can’t use bad data sets, pretend that they’re not bad, and expect people to not call you out on it.

Some concrete things you can do that’s more than “ignore the problem” that doesn’t cost any money:
1. Weigh samples to correct for disproportionate representation in data.
2. Do an ablation study comparing the impacts of different weighing schemes on both to-the-data accuracy and measures of bias.
3. Evaluate and be open about the extent to which your algorithm exhibits undesirable biases.
4. Discuss how your algorithm may need to be modified to be put into production.
5. Conjecture as to the effects of latent hidden variables that skew your results.
6. Consider bias, including data collection bias ("our data comes from Stanford students and so extremely over represents rich people"), bias caused by discrimination ("the credit scores of black people are non-representative of their likelihood of repaying a loan"), and bias caused by your algorithm ("As we are optimizing the median accuracy and South East Asians represent only 4% of our data set, the algorithm is not rewarded for increasing performance on South East Asians"), when giving causal stories about your algorithm."
...
-Source
"Every other field of research is able to do this, why is ML somehow not able to? When I worked in the lab, if we had any bias in our participants, it was made very clear as a caveat in the published data. Because of participant pools, data collection bias are still skewed, but it's at least acknowledged as a problem and attempts are made to rectify it."
-Source

Dr. LeCun also later addressed this topic in a quote for the article "What a machine learning tool that turns Obama white can (and can’t) tell us about AI bias":

“Yann LeCun leads an industry lab known for working on many applied research problems that they regularly seek to productize,” says [Deb] Raji. “I literally cannot understand how someone in that position doesn’t acknowledge the role that research has in setting up norms for engineering deployments.”

When contacted by The Verge about these comments, LeCun noted that he’d helped set up a number of groups, inside and outside of Facebook, that focus on AI fairness and safety, including the Partnership on AI. “I absolutely never, ever said or even hinted at the fact that research does not play a role is setting up norms,” he told The Verge.

On the other hand, in "There Is No (Real World) Use Case for Face Super Resolution" Dr. Fabian Offert argues that the model could have been evaluated without using this dataset in the first place:

the problem is this (from the Duke press release):
While the researchers focused on faces as a proof of concept, the same technique could in theory take low-res shots of almost anything and create sharp, realistic-looking pictures, with applications ranging from medicine and microscopy to astronomy and satellite imagery […].
Why faces then? Nothing good ever comes from face datasets, as Adam Harvey’s megapixels project reminds us. Deep learning has opened up a plethora of amazing possibilities in computer vision and beyond. None of them absolutely depend on (real world) face datasets. Yes, faces can be nicely aligned. Yes, faces are easy to come by. Yes, generating realistic faces is more impressive than generating realistic, I don’t know, vaccuums (to just pick a random ILSVRC-2012 class). The responsibility, however, that comes with face datasets, outweighs all of this. Malicious applications will always be, rightfully, presumed by default.

Based on the above, as well as building on Lesson 2, we can conclude:

Lesson 5: the actions of AI researchers help set the norms for the use of AI beyond academia. They should therefore be mindful with regards to which datasets ought be used to test their models, and when making use of flawed datasets can still take concrete actions in their research to minimize harm caused by doing so.

On the Value of Carefully Phrasing Claims

In response to a question regarding the motivation for the initial tweet, Dr. LeCun expressed that his aim with the initial tweet was to inform people of the issue that caused the bias in the model:

Which again led to questions regarding the validity of the initial claim:

Based on all the discussion that arose from Dr. LeCun's first statement, including Dr. LeCun's own set of clarifying follow up tweets, I think it's fair to say that it addressed a complex topic in an overly simple way and resulted in more confusion than clarification. The follow up statement regarding the responsibilities of AI researchers likewise addressed a complex topic in a very simple way, resulting in this reply:

Therefore, it seems appropriate to conclude with this:

Lesson 6: when addressing a complex topic, try to be mindful of your wording and message, especially if you are leader in the field and your statements will be read by many.  An ambiguous statement can result in people taking away the wrong conclusions rather than gaining greater understanding.

Conclusion

These are the lessons I believe can be drawn from this set of events, and that I think are important to take note of going forward. Although the discussion was messy, perhaps it resulting in these lessons could mean that it was still fruitful.





Author Bio
Andrey is a grad student at Stanford that likes do research about AI and robotics, write code, appreciate art, and ponder about life. Currently, he is a PhD student with the Stanford Vision and Learning Lab working at the intersection of robotics and computer vision advised by Silvio Savarese. He is also the creator of Skynet Today, and one of the editors of The Gradient. You can find more stuff by him at his site, and follow him on Twitter.


Acknowledgements
I would like to thank several friends and my fellow Gradient editors for offering their thoughts and suggestions for this piece. Also, thank you to anyone who took part in the discussion that led to this post; if anyone prefers their contribution be removed, please contact me on Twitter @andrey_kurenkov .


Citation
For attribution in academic contexts or books, please cite this work as

Andrey Kurenkov, "Lessons from the PULSE Model and Discussion", The Gradient, 2020.

BibTeX citation:

@article{kurenkov2020lessons,
author = {Kurenkov, Andrey},
title = {Lessons from the PULSE Model and Discussion},
journal = {The Gradient},
year = {2020},
howpublished = {\url{https://thegradient.pub/pulse-lessons/ } },
}


If you enjoyed this piece and want to hear more, subscribe to the Gradient and follow us on Twitter.