In this episode, we talked to Emily Bender about the ethical considerations in developing NLP models and putting them in production. Emily cited specific examples of ethical issues, and talked about the kinds of potential concerns to keep in mind, both when releasing NLP models that will be used by real people, and also while conducting NLP research. We concluded by discussing a set of open-ended questions about designing tasks, collecting data, and publishing results, that Emily has put together towards addressing these concerns. Emily M. Bender is a Professor in the Department of Linguistics and an Adjunct Professor in the Department of Computer Science and Engineering at the University of Washington. She’s active on Twitter at @emilymbender.
Welcome to the NLP highlights podcast where we talk about interesting work in natural language processing. The hosts are Matt Gardner, Waleed Ammar, and Padeep Dasigi.
So today we’ll be talking about ethics in NLP research with our guest, Emily Bender. Emily is a professor at University of Washington affiliated with the department of linguistics as well as the department of computer science and engineering. She’s also the director of the competitional linguistics laboratory at UW. Welcome to the program Emily.
Thank you so much for having me on.
The reason I know that you’re interested in this topic is I’m following you on Twitter and I keep listening to all your comments about it. So we wanted to talk more. So could you provide an example of what can go wrong when NLP researchers don’t pay attention to ethical consideration? Just to motivate the conversation?
Yeah, I have lots of examples and I think it depends a little bit if we’re thinking about what happens just in the research context or if we’re thinking about when the technology that we’re developing in the research context gets deployed. To pick an example of each of those first in the deployed case, there was a story a couple of years back where a Palestinian man posted a picture of himself with a bulldozer on Facebook and a caption that said something like, “good morning.” And the automatic translation translated it to something either in Hebrew or English, I forget which language they were reading it in, “attack them” or “kill them all” or something like that. And the poor fellow got arrested and detained for a day until the people who had seen this realize their mistake. So that’s already terrible enough. It obviously could have been much worse. And here I think that the thing that was missed on the part of the company producing the translation was an understanding of how their technology was going to be viewed by end users. And there’s something called automation bias where it’s really easy to assume that because machines are not social creatures, they are therefore objective and therefore whatever they say must be true. When in fact we know as developers of the software there’s tremendous amounts of uncertainty. And so we have a responsibility to make that uncertainty visible and actionable at the other end. That’s one I think very well. These examples are all very emotional. But that’s an example.
Yeah, I mean honestly I can relate quite a bit. I have a lot of people, friends who got arrested for like political reasons and it’s really terrible. Right? So I guess one point that is not clear to me here is whenever I see a translation, automatic translation on, for example, Facebook, it always says that it’s automatically translated, doesn’t actually like let you guess. So what do you think the company needed to say or like provide in addition to this translation or this note for the communication to be more ethical?
Yeah, so it’s a hard problem to solve, but I think there’s some things that could be done. And so one thing might be to develop a UI that shows something about confidence, right? We translated this automatically and we have this much confidence in the translation. And then that would be something that users over time would see varying. Now of course that has the danger that you get something that’s translated incorrectly with high confidence, but it does build in this notion that, Oh, this isn’t absolute, this is a guess. Right. Another thing would be more transparency about, you know, what did Facebook think the source language was? And my guess there is that there’s not enough detail around varieties of Arabic, which is actually a huge language family, right? It’s not one language. But if Facebook said this was translated from something they probably just said translated from Arabic and it’s entirely possible that part of the problem was that it was a mismatch between the actual variety that the original person posting was using and what the algorithm detected or, or assumed. So that would be a kind of transparency. And that I think we as a research community have an education obligation to basically let the public know that all of this is fallible. And that’s at odds with the incentives that we have to talk about how cool our research is. Right.
Would it have helped if we also showed, if there’s a company that was doing the translation would have also showed some sort of an accuracy instead of a confidence because if they had a way of estimating how well they would do say on automated data and if we can show some sort of estimate of the accuracy and how good the translation system is, would that have happened?
Yeah, I love that idea. So that it’s not just confidence in this particular example, but over time with this language pair, this is our estimate of accuracy. That sounds like a great bit of transparency to include. I also like sort of in this space, if you’re looking at speech to speech translation or speech to text. So I’ve seen this in the Skype translator. I see it in the Google home device with the speech recognition step. It will update and change as it goes. And I think that’s a wonderful visual clue to the uncertainty of the machine, right. That when you see it changing over time, I think that’s just a very immediate notion that okay, the first thing it wrote isn’t necessarily right. And so therefore maybe everything is writing isn’t necessarily right.
Yeah. And I know that the US designers put a of effort into trying to weigh different considerations, but I imagine the can probably put more weight on the ethical side of things. The conversion I’ve been involved with in the past in a variety of places did not put that much weight on ethics. Yeah. Thank you for bringing this up. So now I think you wanted to provide two examples, one from the industry or like an actual views and another from research.
Yeah. So the research actually have, I think three quick examples that all fall under the heading of setting up a task in such a way that it reifies notions of you can predict X from Y when in fact you can’t. Right. And so these come from published papers and organized shared tasks. So one was looking at predicting the sentence that an accused person was given from their charge seat, what they were charged with. And that’s the only input, right? And if you think about how a judicial process works at least in a fair judicial process, there’s a few steps in between there. There’s additional information that should be coming in, right? But the system is just going from that input to that output. A second one along those lines is a shared task being run this year. That’s looking at, it was initially framed predicting intellectual ability from text snippets and there, what was called intellectual ability was actually IQ scores.
And there’s tremendous amounts of research showing that IQ scores don’t measure what they purport to measure. So there’s a validity problem there between the output label and how it’s being framed. But then there’s also this question of what is the set of reasons that would lead you to believe that in short answer descriptions on some tests, there’s enough information to predict even what IQ tests are actually measuring. And I think that that’s a common problem that we have because in our field we are so hungry for datasets that we find something where there’s, you know, from one source we have data X and data Y. And so we say, okay, well let’s set this up. Let us as a task where X is input and Y is output without thinking through why is that a reasonable task? And I have one more example of that from a published paper that was aiming to predict faces from voices.
So the input is voice recordings and the output is generated images of faces. And again, I want to ask what is it that makes you think that that information is there, why does it make sense to go from X to Y? And part of that is just if you start answering those questions, you can quickly see where the scientific invalidity is. But also it quickly lays out where the various assumptions are that are going to reinforce different kinds of oppression in society. And if we say, Hey, we’re scientists and we’re showing that you can predict X from Y. And in making that jump. We’re relying on all of these, let’s say racist assumptions in some cases. Then we are basically using scientism to reinforce that racism. And that’s a, I think another kind of ethical risk that we need to be aware of, especially as there’s so much media attention to the work that we’re doing.
So I’d like to isolate two aspects here. One, the scientific endeavor of finding whether you can or cannot predict Y from X , from the actual, like the social consequences of having the public believe that this is doable because I think the first one is actually reasonable in my opinion. And I am interested in discussing the difference in opinions here. Why don’t you think this is an interesting research inquiry in its own right to see if we can, if it’s possible to predict faces from voices or the other way around or the IQ score from something that people would write. I would find reading the results of this paper intriguing.
So I think that if people are asking, genuinely asking the question, can we predict X from Y and looking at, well what are the steps? What’s the information in X that would allow us to predict Y, what are the correlations are that we’re building on? That’s a very different thing from I’m going to throw a neural net and give it X as input and Y is output in this one closed dataset where there are undoubtedly artifacts and undoubtedly the neural net can build on those artifacts to do better than baseline.
So those are, it’s sort of a mismatch of methodology to question I think.
Yeah, that makes a lot of sense. I think that bridges the gap in understanding and yeah, if the goal is to understand the correlation, then it doesn’t make sense to throw a neural network at it. So thank you for providing these examples. I think this provides clear motivation for why we are having this episode discussing ethical considerations. Shifting to what NLP researchers who this podcast is targeting. What are day to day? Things that we do commonly, which we should pause and think before we do them.
Yeah, I think we should always pause and think. And I think this is going to be a theme in this discussion that I think things are moving too fast, but there’s lots of different ways of categorizing the risks of the kind of research that we do. And so I’ve put together one but I don’t want to say this is the only way to think about it, but I think we can at a high level separate between risks in the research process. So ways we might be doing harm by carrying out the research and then risks of the resulting technology. So to the first of those there’s things like is our research process exploitative? Is it exploitative of of say crowdworkers is it exploitative of graduate students? What is the work we’re asking of people and are we compensating them fairly? And I should say in the case of working with graduate students, are we setting reasonable expectations of the amount of time someone’s actually doing work in a week?
Right? So these are related, right? If you have a salary graduate student and you expect 80 hour work weeks of them, you are not compensating them fairly. But it’s another important angle on it. The second thing is we should think about privacy as we’re collecting data. So where are we getting the data from? And I think that oftentimes people coming from, not fields like anthropology or sociology or psychology where you start by working with humans and seeing them as humans. But computer science or linguistics falls into this too where you start by working with speech and text produced by humans and you think of it as just data. We need more practice in thinking about the people whose speech and text we are analyzing and you know, are we doing this in a way that’s consistent with those individuals notions of what they thought was going to happen when they said that thing. So the third thing was what, just what I said before, but in our research process, the PR step is a part of the research process and that’s where those examples that I was coming up with before about, you know, are we doing societal harm by claiming to be able to predict Y from X is a consideration under the research process heading.
Got it. So going back to exploiting people who are involved in the research, right. I think this is an important problem and it’s not clear to me for someone who actually wants to do the right thing, how can we do that? It’s not like straight forward. For example, if I’m hiring crowd workers to do some annotations, it’s not as simple as paying them the minimum wage right? In the U S because they may come from different countries and we don’t have data about what is the minimum wage in each of these countries. Is the minimum wage, is the right thing, the target or not. Some of these tasks require more skill. I don’t know. Do you have any thoughts on that? If someone wants to, you know, be on the right, how can they do this without spending, you know, all their time thinking about it.
Exactly. You’re absolutely right that there’s hard questions there. And the last thing you said I think puts a pin in it that we don’t want to have to keep solving these problems ourselves over and over and over again. So I am not, I don’t work with crowdworkers. So I don’t have that expertise. But the idea would be to look to somebody who has thought this through and said, here’s what we developed for our system, we worked with crowdworkers, we know which countries they’re coming from. And we through this reasoning figure that this task was actually going to have to be more than minimum wage for that country. And here’s how we worked it out. And so basically instead of reinventing the wheel, go look to best practices and then build on those and then always report out and say, you know, we use crowdworkers for this and following so-and-so methodology, we made sure that we were doing X, Y, and Z to compensate them fairly. And then that becomes part of the ongoing practice of the field. And it’s not this insurmountable problem each time we start to do it.
So what’s a good venue to put together something like this? These guidelines. Do you think it’s like something that we should be publishing about or is it just like one of the research groups put together methodology and just put it on the internet and invite other people to comment on it?
So it’s definitely important that it’d be out there and accessible. And I think that actually having a peer review step would be even better. And so, you know, a good venue well possibly, so now in our, you know, the ACL conferences now had just have ethics as a track, right? That’s just an ordinary kind of paper you can submit. I think it could fit under that. And then there’s also conferences in the broader AI ethics space and the flagship in one of those I think is called FAT*: fairness, accountability and transparency. It was FAT ML in machine learning and then they said, actually it’s broader than that and that’s going on this week actually in Barcelona. And so a conference like that might also be a venue for that kind of a publication.
Beyond submitting to a specific track would it makes sense to do, I mean I have seen many dataset papers in recent times include information about how exactly they got it this information from crowd workers and how much they paid them and how long, if it is on Mechanical Turk, how long each head took. Would it make sense for reviewers to actually to actually look for this information and if they, is it a responsibility of the reviewers to look into this information and figured out if there are any ethical considerations here and include that in the review process?
Yes, absolutely. So I think there’s, there’s two different questions here. One is, you know, someone could write a best practices paper about here’s how you do it and that should be published somewhere and accessible. And then anytime someone’s producing a new dataset that uses crowd workers, they should be giving information about how the crowd workers were recruited, who they were, right. Are you, is this, are you collecting Singaporean English? And so you made sure that you found speakers who are in Singapore. Is that enough because Singapore has lots of, you know, migration in and so on. And then also how did you compensate them? And yes, absolutely the reviewers should be looking for that. And in fact I just wrote a review where there was some crowd worker driven research and it said nothing about compensation. So I asked that, I said instead of saying were they compensated fairly, I said, how did you make sure they were compensated fairly presupposing that that’s obviously what you would do, right?
Yeah. Also to applaud the efforts at AI2. So I used to work at AI2 and at some point we realize that we’re doing a lot of work collecting a lot of data from crowdsourcing and we put together a committee to set such guidelines. I don’t think it’s public. Maybe we should try to see if AI 2 can make it more public.
That would be tremendously valuable.
Okay. So let’s move on to the next, I think you want it to provide common risks in both the research process and the outcomes of the research. Do you want to shift gears to the second?
Sure. Yeah. So this is risks of the resulting technology. So here this is, we’re building something. And we are motivating it in the introduction to the paper by saying we’re going to solve this problem in the world, right? So we’re certainly imagining that it’s going to be deployed and out there. And I think there’s some questions that we can ask ourselves along the way to help guide our valuable research time to technology that’s going to have positive benefits or on balance positive benefits rather than just, Oh well this looks like a fun thing. There’s some data here. So I’m going to work on that. And then stumbling into problematic things. So we should ask ourselves, if we deploy this, do we run the risk of amplifying existing biases in society? And the answer is almost always yes. So then the question is, well, what can we do about it?
How can we mitigate that? And so some examples here are in Safiya Umoja Noble’s work Algorithms of Oppression. It’s a 2018 book. She documents how search engines, which sort of present themselves as being an impartial window into the internet, actually reinforce systems of oppression. And her starting example is she had her stepdaughter and nieces over and was looking for some activities for them to do and typed in the key phrase “black girl”s thinking to look for you know, activities that black girls might be interested in and of course landed on a bunch of porn sites that’s been fixed since she started documenting it and pointing it out. But it came about because probably the way language is used on the internet and Robin Spear points out that a lot of the web is porn correlates words with other things that you aren’t necessarily thinking about as you are developing the software.
And so it’s really important to test for things like this. Another early example is Latanya Sweeney documents in a communications of the ACM paper from 2013 called Discrimination in Online Ad Delivery. How if you search at that point for names that sound African-American as opposed to names that sound like they belonged to white Americans, you get different versions of this ad about background checks. And so she discovered this when someone was searching for her to find her research and put in her name and up on the sidebar up popped has Latanya Sweeney been convicted of a crime, which you can imagine would be a terrible thing if you are applying for a job somewhere. And so someone types in your name to try to find your research and they get the subliminal message that maybe you’re a criminal. So these are examples of amplifying existing biases in society that you know, if the technology is broadly used, you have to be very careful about.
Yeah, these are really hard questions because the incentives that come into play for advertisers and have little to do with ethics. Right. And I wonder like I don’t, I don’t expect you to give us like a solution for all the ethical problems. So I’m not gonna ask this question. Yeah.
The first step is to be aware of them and then to not look away. Right? So this is the first step of solving things. If you’re not even willing to look, then you can’t help at all right. So that was the first category. The first category was amplifying existing biases society. And the second one is can be used unintentionally in harmful ways. And I think my example about the translation failure is an example of that, right? Nobody was trying specifically to use the machine to weaponize the machine translation. You can argue about the motivations of the people who read that translation then took action based on it, right? But don’t necessarily support those motivations, but they weren’t deliberately trying to hurt someone with machine translation. Another example is these companies that are proporting to provide virtual interviewers that make decisions about people based on their voices.
And this is, you know, meant to solve the problem of there being too many applicants. And so the poor screeners can’t get through them fast enough. Or maybe it’s trying to say, well, the machine would be more objective than a human. But you can imagine if you are being interviewed by this and your language variety doesn’t match the one that the system was trained on, which is likely to be, you know, one of a small handful of standardized varieties of English or maybe a couple of other languages, then you’re going to get knocked out of the running right away cause it can’t even understand you. Right. And similarly, if you’ve got systems where you have to go through a voice dialogue system and your speech patterns don’t match it’s range, then you’re not gonna be able to have access. So what if, and this hasn’t happened to my knowledge, but what if someone puts ASR in the loop in the 911 system, right? And you’ve got someone who, like my in-laws for example, speaks Indian English and the nine one one system is not ready for Indian English. Right. I definitely do not want them unable to contact 911 because a stupid machine can’t understand them, you know?
Totally. Yeah. And I honestly, I can relate to this because like many people who are not native English speakers already have trouble communicating with people who are taking the phone calls even without having any inflammation. So it’s only, it can only get worse. Well that’s not true. I think it may be better, but only if people who are building the N>P technology actually have this in mind to your point.
Exactly. Exactly. It needs to be tested. And then a fourth example there is if you’ve got hate speech filters built in to say social media, right? That’s definitely coming from positive intentions. We want to reduce toxic discourse online. So we’re going to put in some automatic detection for hate speech and then enable the software to take some action based on that. Maybe it’s just flagging it, maybe it’s saying, no, you can’t post this, you have to rephrase or whatever. And if that is designed in such a way that it catches, for example, a lot of false positives that are actually people talking about their own experiences as the victims of hate speech, then it’s going to have some negative side effects that are particularly harmful to already marginalized groups. So this is all examples of something being used widely and causing harm, but it’s not being used maliciously.
It’s just sort of a side effect. Right? Third category is technology that can be used on purpose in harmful ways. So you might remember the Tay chatbot that Microsoft deployed and then had to take down in 24 hours because users trained it to be spewing all of this hate speech. So that was definitely intentional. I think that the automatic prediction of sentences from charge seats example from before could easily be used with you know, malicious intent surveillance systems based on sentiment analysis of social media. We’re going to go find people who are unhappy and unhappy with the government and you know, try to oppress them further. You could imagine systems that are trained to detect different styles associated with LGBTQ identities. And then running that over text and outing people who didn’t think they were performing that identity in say the Twitter public space, but only over in some close chat rooms say. And then finally you could imagine people using the very powerful, very fluent sounding language models to produce fake news and then distribute it as if it were real news. And there I think the main harm is not so much that people are going to be taken in by any particular example because they are basically random. But it could lower people’s overall ability to go find reliable information by just flooding the zone with fake news. So there’s lots of potential for actual deliberate harm as well.
I think I mentioned in our conversation, right, that’s not clear to me how any one person or any one group decide if a methodology they’re taking or the actions they’re taking to avoid some of these biases is working. So there is this distinction like in NLP at least we tend to do like empirical work because you know, it’s easy to evaluate and a lot of the problems that you’re discussing are not easy to evaluate because the effects are very nuances and there are so many factors that come into play. It’s not easy to measure the outcome. It’s not clear to me what is a measurable outcome that we can agree on.
Yeah. I think that we are never going to be able to claim that we have solved any of these problems, right? It doesn’t, it doesn’t fit that sort of a mindset. And so what do we do? Well, the first thing I think is this really helpful slogan to hold onto that I learned, from the value sensitive design researchers here at UW. So Batya Friedman is the leader of that, which is progress, not perfection. That we have to be comfortable that we can never fully solve any of this, but it is still worthwhile to try to make it better. And honestly, that’s true in everything we work on in NLP, right? When we say a problem is solved, it’s because we’ve gotten to, you know, 99% accuracy on the standard test set. Right.
So I think what you’re saying is currently you think the researchers working in NLP don’t spend enough time thinking about this problem in the first place and we’re not trying to solve the problem necessarily, but we’re trying to get more of us think about it and spend more time thinking about it and you don’t want it to be, you don’t think it should be 100% of our time. Obviously it should be something that we deliberately discuss and think about.
Yes. And that we plan time to think about that when we’re planning the next research project. We actually build in time to think about, okay, who are we going to talk to to understand how this technology interacts with society? Who are we going to talk to to understand how we get the best practices for working with the crowdworkers? Who are we going to talk to to understand what’s actually in the dataset that we’re building on so that we responsibly talk about how it could generalize. Like there’s also a scientific validity thing in there, right when we have, we train and test systems based on specific datasets and then we sort of claim that we’ve solved the problem in general or that we’ve done this well and the problem in general when in fact we’ve done this well on the problem in English for this genre, for speakers from this era talking about these topics and we don’t really know that it generalizes beyond that.
So by building in time to ask those questions I think is important. And then also allocating time for talking to the public, which is why I’m, part of why I’m delighted to be doing this podcast. I know that this is the main audience is people in our community, but I think that it is very worthwhile for us to put time into talking to the broader public because in many cases what’s needed is not just, you know, the Goodwill and the hard work of all of the researchers. And companies, but actual regulation and we aren’t going to get that regulation unless we have an educated public that knows to push for it and is able to educate their legislators as well.
Yeah totally, so I’d like to make sure that we have time to discuss a set of guidelines that you compiled and shared few months ago for directing the efforts of research groups.
I think I want to sort of put a little bit of context around them, which is that these questions alone, they’re focused very narrowly on task development and there’s some other things that they should go side by side with and in particular, so I’ve got three high level questions. Am I treating the people involved in the research process fairly? That’s the crowdworkers and privacy considerations that we talked about. The second one is am I accurately representing the range of speakers that my system should generalize to or whose biases my system might have picked up? And this is coming out of a whole bunch of work that happened largely in 2018 around documenting datasets and models trained on datasets so that people who pick up those models know what they’re getting. So I wrote a paper with Batya Friedman and we called it data statements and it was focused on NLP datasets Timnit Gebrui, et al. at roughly the same time developed something called data sheets that was looking across machine learning more broadly and Margaret Mitchell and others published in FAT* 2019 developed something called model cards. And these are very similar ideas of basically. When we hand along a dataset or a model trained on a dataset, there’s a whole bunch of detailed metadata that should go with it. So with those things in context, then there’s also this question of when I am designing a task or what I’m choosing to work on a task, does it make sense? And that’s what these questions are about.
So basically these are a set of questions that if I’m going to propose a shared task in some workshop or create a dataset and put it in a paper, I should try to address both while designing the data and when I’m disseminating the work.
Yeah, exactly. And we should expect to when we’re reviewing work that people have done this. Right.
Right. And in your opinion it should be ground for rejection if some of these are not addressed.
Yeah, exactly. Just like used to be in ACL papers that you didn’t have to have evaluation on held out data or you didn’t have to have a standard metric and then we came around to, well no actually we can’t really learn from what’s going on here if we don’t have held out data can see it generalizes and metrics that are either, you know, well motivated elsewhere and just picked up where the paper’s motivating the metric is. I see this as similar.
No, I agree. I think one challenge here is that it’s very subjective. Like whether someone is actually addressing any of these concerns or not. And I think it will largely depend on whether the person reviewing the work actually spends themselves enough time thinking about it. Right?
Yeah, but that’s true for everything about what we review. Right. You can say that certain parts of it are more objective, right? If someone has made a mistake in one of their equations, you would expect multiple people who know enough to pick that up the same way. Right. But I think also if someone has, you know, completely glossed over the fact that there is socio-linguistic variation and the system that they’ve set up where they think it’s detecting intelligence is actually detecting language variety based on socioeconomic status. That’s not a subjective thing. That’s a question of knowing something about socio-linguistics and knowing about power and financial capital and social capital all correlate with ways of speaking that relate to the education system and on and on and on. This isn’t subjective. It’s just not taught in classes that are typically taken by computer science students.
Yeah. Fair. I want to focus on one distinction between trying to be fair and addressing the full spectrum of people who may benefit from a particular technology and making a proof of concept or doing a step in research where you know like any one group or any one researcher is not expected to solve the problem. Right? I think we kind of started with a narrow problem statement and in the future other people, if this turns out to be an interesting and useful outcome, other people can build on it and build a wider scope.
You and I are both looking at these questions but we haven’t said them out loud yet. So for the listening audience would probably go through the questions. I think they fit in well in both of those contexts, right? So the first question is: How does the output of the machine learning task relate to the information it’s framed as predicting? And a lot of this has to do with, you know, that motivation that goes in the introduction. This is an interesting thing to try and do with machine learning because we want to be able to predict X. Right? But the actual output label frequently isn’t directly X, it’s some proxy for X. And so it’s worth sort of stating explicitly what that connection is. Why is this a reasonable proxy for X? Second question is: Does the input to the machine learning task plausibly contain enough information to predict the output? And this is, am I doing thoughtful science here or am I just picking up a set of numbers, calling it data and throwing a neural net at it? And I think that, you know, in our guise as reviewers, we want to be valuing and rewarding the thoughtful science
We should probably also be concerned about whether the input has any more information then we want. Right? I mean, sometimes it’s possible that the inputs have unintended correlations with things we are not interested in or that we actually avoid the model from being classified. Right? So that’s probably, is it?
Yeah, absolutely. And when we’re talking about language data, that is always going to be the case. Language carries so much information. So if we’re claiming that our model is able to predict Y from X because of a certain thing in X, we have to be able to make the argument that it’s not all those other things in X, right? Then: What are the intended uses of this technology? And you know, depending on what you’re working on, it can be very, very far away, right? If you’re doing sentiment analysis, you can pretty quickly talk about uses real world deployment of sentiment analysis. If you’re talking about, let’s say, parsing to predicate argument structure, that’s got an enormous range of uses. And so this question becomes, I think less pointed, you know, this is at first in the reviewing process, I could imagine that there’s going to be some misses where people say, well, you didn’t tell me how your predicate argument structure parser was going to be worked.
You know, you’re going to be used. And then it’s like, okay, yeah, but that’s less directly relevant here. But what are possible misuses of this technology and how can they be minimized? And that might be a question of we build this thing that allows us to detect hate speech and it could be misused to curate and promote hate speech as opposed to trying to minimize the amount of hate speech in some platform. And there sometimes the solution, the how do we minimize these risks isn’t actually in the technical stuff. It’s in regulation. Right? And it’s saying if this becomes robust, widespread technology, then in order for it to have benefits to society, it’s going to need to be regulated along these lines. And that, the people who have expertise to build those regulations are lawyers, but the lawyers don’t necessarily know what the technology can do.
And it’s on us to say this is what can happen. Now let’s try to build the societal structures that prevent that from happening because we also want these beneficial applications of this technology. So if the technology is working as intended, who might be harmed and how, and if it’s not working as intended, who might be harmed and how. And then here are the example of a hate speech filter, sort of suppressing the voices of people who are trying to talk about their own experiences as victims of hate speech is a good example. And then finally looping back to my point above about you know, what is this based on? Let’s document the data thoroughly so that people know when they’ve picked it up and they go work on the next thing. Have they taken a dataset that was collected from college students in the United States and then they’re trying to deploy a system based on it in, I don’t know, Lagos to do named entity recognition in tweets or something. You need to have good matches between training data and deployment and you’re only going to be able to get those good matches if there’s documentation about where the data came from.
Yeah, that makes a lot of sense. I think all of these points are important to mention in our proposal to think about. The thing that I’m struggling with a little bit here is how can we make it interesting for people to actually care, right. I think like so from the perspective of someone who’s actually doing the work and who realizes the social responsibility, I think it’s important, but in order for this to be done at scale, I think we want to align the incentives somehow. And I think this goes back to your point earlier, that if the reviewers of our peer reviewed work care it enough then they would have a stronger incentive for everyone to adopt it.
Yeah, absolutely. So I think bringing it into reviewing is important. And I think also, not necessarily just focusing on, but highlighting the aspects of the discussion where it really isn’t just about, you know, social justice, which is really important, but it’s actually about scientific validity as well. All right. We’re talking about how well do systems generalize, we’re talking about the actual validity of the tasks and the applicability of system output in many of these cases. And it’s not just you know, this set of concerns that might seem peripheral or outside of the purview for someone whose primary expertise is in model building say all right, it’s, it’s about, you know, is this experiment that you’re doing actually showing what you think it’s showing and is it working the way you think is going to work? And one of the things that I’m really interested in doing, and I’m, we haven’t gotten started yet, we’re still seeking funding, but I’m with some colleagues including Bernease Herman, Brandeis Marshall and Hal Daumé III.
I’m working on designing shared tasks with evaluations that take into consideration not only the percent match against the gold standard, but actual things like differential impacts of different kinds of errors and who’s most likely to be harmed and rolling that into the evaluation metrics so that when somebody is working on this and they are in the thick of the hard work of saying, well, let’s see if I improve my model in this way or I changed the training regime in that way, how does that affect my results? It’s right there in the mix as you go. Rather than being a separate thing that you think about maybe before and after.
Yeah, that sounds really exciting and it will directly address this point of incentive here.
But then the other thing I want to say is that we should look to all of the people in the humanistic fields. So everyone doing digital humanities, everyone doing you know, psychology, sociology who think of themselves primarily as interested in people and systems of people, but have had to learn statistics for running experiments and they’ve had to learn, you know, programming to be able to do their digital humanities methods. I think conversely, people who start in the programming and math world need to learn some of the skills from these other fields about evaluation methodologies that don’t necessarily result in numbers or numbers compared to a baseline. And that’s scary, right? We all have expertise. We all have our training and we’re sort of in our comfort zone, but sometimes the tools that we need come from someone else’s expertise and there I think we will make true progress faster as opposed to lots of publications faster if we do more interdisciplinary collaboration, for example, with colleagues in human computer interaction, human centered design and engineering, science, technology and society, sociology, anthropology, psychology, et cetera. I think that de siloing is going to be an important part of this as well.
Yeah, that makes sense. There’s also like, since you’re mentioning how other fields are like addressing these questions, you know in medical research they have IRB boards, I don’t remember what it stands for, but essentially their job is to review both the ethical and scientific validity of the research that’s being done and it’s like it’s a systematic solution, every like larger institution would have an IRB board and the work needs to pass IRB review before it’s published. Do you think it makes sense to implement something like this and it’ll be, or an ML research,
So it might, I think it’s worth stepping back a little bit to see you. Why institutions have those IRBs, and it’s partially because of, you know, really harmful work that was done in early medical science, right? And society as a whole said, we need to make sure that this doesn’t happen. And so we have IRBs at universities in the United States because if we don’t, we can’t get federal research funding, right? There’s some regulation that’s behind that. It’s not just that everyone at the university feels particularly strongly about this, but there’s, there’s regulation and there’s a really interesting disconnect between the way people in computer science do research and the way most of the other sciences do, right? If you think about what it takes to actually carry out an experiment in biology or particle physics or if we move to the social sciences, psychology or sociology, you have to come up with your careful plan.
And if there are people involved, then there’s the IRB and then you do the actual, you know, sampling of your cultures or you know, harvesting of plants or observing part of the night sky or whatever it is. And that takes time. And then you analyze the results in computer science. We have this really strange and linguistics also because of the way we collect our data. Some of us, we have this really strange scenario where we can say, huh, well what if I just threw that model at that dataset and you can do an experiment on this timescale that is ridiculously fast and it right now brings money and prestige to the field because it’s making a certain kind of progress look fast, but it also makes it even harder to imagine making room for some of these things. And I think that it is important to build in the time for carefully thought out experiments that are then reviewed somewhere. Maybe it’s in task design, maybe if you’re just picking up a task that someone else’s designed, you can rely on them having gone through the review process for example, but building it in somewhere and I think it’ll make us feel better about building it in if we look to our colleagues in other fields and see what doing science looks like for them as opposed to what it looks like for us.
Yes. Well thank you. I think this has been a very interesting conversation. We’re almost out of time. Is there anything else that you wanted to bring up that we didn’t already discuss?
I think we managed to cover it. I guess the remark that I’d like to end with is that we should slow down the pace and it will be good for everyone. It’ll be good for the society that benefits because we will have done more thoughtful software creation and it will be good for us as researchers because we’ll have better work life balance.
Yeah, thank you. I don’t know if all the grad students out there will find it easy to change their mindset. I know that I was much more in a rush when I was a grad student because you know, fears, competition and there’s a need to publish often I think it’s gonna take time for this culture of change to happen. But I do agree with you. I, and I hope a field will slowly go a little bit slower.
Yeah, I hope so too. And I hope that the people who are established take the lead in making that change rather than pushing their grad students. And adding the stress, basically finding ways to make space for their grad students to do more thoughtful work and more impactful work.
Yeah. I do want to thank you on this note because I know several of your students who when you’re not around, would commend you and how much care you put in designing the deliverables so that people don’t have to work in holidays and things like this. So I think it’s very well perceived. Thank you for taking the lead on this and being more proactive than most.
Oh, I’m glad to hear that and I hope that, I hope that more will follow suit.
All right. Thank you, Emily. It has been a pleasure having you.
Thank you very much.