In 2022, Politico reported that Crisis Text Line (CTL)–a non-profit SMS suicide hotline–-used one-on-one crisis conversations to train a for-profit customer service chatbot. In response, CTL maintained that they did not violate reasonable expectations—hotline users “consented” to a lengthy Terms of Service (TOS), which specified that CTL could use data for business purposes. 

This uninformed consent procedure and irresponsible use of data is an obviously egregious move in many research circles, such as my own. However, in the eyes of the law, CTL, and many others, CTL didn’t violate any reasonable user expectations. The consensus was that, while TOS consent procedures are imperfect, it’s the cost of having free technology services. In my paper, I ask: are we still okay with paying this price?

The price here is justice—our ability to give everyone their due right. In offline settings, injustice evokes the image of human rights violations and massive inequities. However, the picture is less clear in AI settings. As we all contribute our data, knowledge, and time to AI systems, what do we deserve as a matter of justice? To answer this question, we formulated a precise theory of justice that captures current tensions between users and tech companies in AI/ML settings.

To quote John Rawls, 

“A theory however elegant and economical must be rejected or revised if it is untrue; likewise laws and institutions no matter how efficient and well-arranged must be reformed or abolished if they are unjust.”

In other words, a theory of justice is an attempt to appropriately represent the state of the world, articulate societal values, and inform future change. When we have a true theory of justice, we do not need to rely on subjective moral intuitions. Rather, we can agree upon an ethical compass for building our laws and institutions.

Our paper proposes data agency theory (DAT). Data agency is one’s capacity to shape action around the data they create. For example, individual privacy settings on Google increase one’s data agency by allowing users to opt into (i.e., consent to) data sharing with third-party advertisers. Data agency theory argues two premises. First, consent procedures outline data agency systematically and, therefore, are institutional. Second, inspired by justice scholars such as Rawls and Young, this institutional way of outlining data agency is a matter of justice. In sum, justice in a predictive system demands considering how institutional routines (i.e., consent procedures and terms of services) transform agency at a group level. Concisely, data agency is a contributor to justice and a product of consent policies in a predictive system.  

DAT is a data-centric theory of justice that directly translates to next steps for achieving ML & AI ethics goals. As ML/AI systems need larger datasets, ethicists in the field have made numerous calls for better data management throughout the pipeline—involving questions of consent, data storage, and responsible use of predictive outcomes. However, these calls have been mostly unanswered; many social media sites still use dense Terms of Service agreements that have been criticized for over a decade. Here, we argue that this lack of action to increase a user’s data agency is not just a moral imperfection, it is injustice.

 By raising the stakes of problematic consent procedures, we hope to catalyze action. In the paper, we reimagine consent procedures in two salient ML/AI data contexts: (1) social media sites and (2) human subjects research projects. For example, we imagine affirmative consent on social media sites, sustained efforts by researchers to reaffirm consent, and the ability to withdraw one’s data from benchmark datasets. AI justice demands consent procedures that proactively solve systemic information and power gaps around one’s data. This paradigm shift is crucial to evaluating current consent procedures and generating better ones.

What does a just world with AI look like? How can we evaluate the justice of our AI systems before they cause material harm? Check out our paper for discussions of these questions, or come to my talk at FAccT during the Towards Better Data Practices session at 3:45pm (UTC-3) on June 4th.

The PhD students at GroupLens have a variety of hobbies! From knitting to playing video games, we all have non-research activities that contribute to our lives. In this article, we asked two PhD students, Leah Ajmani and Alexis Tarter, how their hobbies have helped them become more successful researchers. What does distance running and working an escape room have to do with research? Read below to find out

Leah and her dog, Yogi, finishing their first half-marathon

Lessons from Running

As a very unathletic kid, I didn’t pick up running until I was a PhD student. I’ve run numerous community races in the past two years, including a half marathon! Here’s what I’ve learned:

There’s no such thing as “junk miles”

Sometimes our runs suck. Similarly, sometimes our writing sucks, our research is going slow, or we have to miss a deadline. However, there is no such thing as junk mileage in research. All research, even the research that doesn’t end up in papers, builds our capacity to do research. In that sense, it is useful!

You have lots of different paces; the key is to switch between them

You may have heard “it’s a marathon, not a sprint” about your PhD. The advice here is to go slow and not burn out. Running has taught me to extend the metaphor one step further. I have a marathon pace, but I also have a sprinting pace. I even have a 5k and 10k pace for things in the middle! The key to not burning out is to use the right pace at the right moment.

Think about how you would run a marathon. You may have a “marathon pace,” a goal for each mile to run a certain marathon finish time. For example, my goal is to run a half-marathon in under 2 hrs and 15 min. In theory, I would need to run a 10-min mile 13.1 times. In practice, though, my first few miles would be >10 minutes so I can get into a rhythm. Then, each mile gets progressively faster so I can “ramp up.” The idea is not to just run slow. It’s to run slow at the beginning so that you can go ham and truly race in those last few miles. In a PhD, paper deadlines require you to have enough energy left in your tank to race those last few miles, so be judicious with your pacing.

Your mind will want to quit early and often

In running, we say, “Your mind will want to quit before your body does.” Obviously, if you’re injured or battling physical limitations, you should STOP RUNNING. But if the reason you want to stop running is because your mind is telling you to quit, it’s probably best to keep going.

In research, I use the “quit-three rule.” If I’m reading a paper, writing, or even running, I tell myself that I have to think, “I should quit doing this,” three distinct times before I’m allowed to give up on the task at hand. This rule gives me the ability to pivot off of things that simply will not happen at the moment while still building the resilience to do the things I want to. It’s not perfect! Sometimes, I’m phoning in the task, but it’s a good way to practice focus. 

Alexis and friends finishing an escape room!

Lessons from Escape Rooms

I once worked at an escape room, and it turns out it was helpful for my PhD (and it was more entertaining than watching TV). 

Ask for help earlier than you think you need to.

Often, groups would come to complete an escape room and be overconfident about their abilities. Maybe they had completed plenty of rooms in the past. Maybe they really enjoyed solving puzzles. Maybe they just thought they were particularly smart. But more often than not, those groups would perform worse than others. Why? Because they didn’t ask for help early and burned precious time on simple problems. 

The same flawed thinking can occur in a PhD program. Rather than admitting to your advisor or peers that you are stuck, you may be tempted to battle against a problem all by yourself. Don’t be like those overconfident escape room groups! Asking for help and being vulnerable with others can help you tackle a problem and connect with those around you. 

Answers can come from unlikely places.

“Wait…I think I’m Janet!” said my colleague. Turns out a fantastic group had been calling the escape room employee “Janet”, an all-knowing being from the show The Good Place. While, unfortunately, none of us can ask a not-a-girl, not-a-robot character the answer to any question in the universe, we can find answers in places we least expect them. 

The same is true during a PhD program. While courses and your advisors are key sources for support, engaging with experiences that bring you joy is also vital. Maybe the family member you are trying to describe your project to can help you find a way to frame your research question. Maybe the crafting event on campus introduces you to another student with whom you can collaborate. Maybe an intramural pickleball game clears your mind, and you discover the next direction for your dissertation topic. A PhD program is a time to explore not only intellectually but also personally.

Communication is key.

Escape rooms are all about effective communication, whether it is joining hands to close a circuit, yelling out numbers from around a corner, or describing what’s inside a hidden room. It is astounding how many times I’ve seen a teammate find the key to solving a puzzle and put it silently in their pocket. As one of my favorite characters states, you have to “talk it through, as a crew.” – Stede Bonnet, Our Flag Means Death. 

And, as I am sure you’ve noticed the trend by now, the same applies to a PhD program! Unfortunately, some lab and campus cultures discourage meaningful connections and collaborations. It is crucial in those situations to find people you can communicate with such as a support office or friends and loved ones. However, in all situations, it is how we talk to ourselves and with others that often determines how successful we can be.

Whether it’s running, escaping, or even knitting, inspiration is everywhere for being a successful researcher! Which hobbies have helped you as a PhD?

image by Freepik

In the era of internet research, everyone is a participant. Picture this…

A PhD stood at the front of a crowded conference hall.  They’d just presented their paper on social capital in distributed online communities. As the applause settled, an audience member scuttled to the microphone, eager to ask the first question.

A professor from University College. Thank you for the great talk. It was refreshing to attend a talk with such rigorous methods. You scrapped data from so many different subreddits and made such a compelling argument for how these results will generalize to other online spaces. My question is less about the research and more about your experiences with data contributors. How did the various subreddit community members react when you talked to them about this exciting work?

What kind of question is this? The PhD thinks to themself. It’s not feasible to get consent from every user. We got an IRB exemption, got approval from subreddit moderators, and followed all the API terms of use and regulations for researcher access. Do other researchers really ask for consent at scale? Did I get consent…?

You may be in a similar situation now! Using social media data for research is a common method that has massive potential for large-scale analyses in both quantitative and qualitative research. However, it can be frustrating to simultaneously hold individual, affirmative consent as the golden standard and recognize its limitations as a viable option for many researchers. To that end, we’ve made a reading list about getting individual consent at scale, particularly in research settings. We hope this reading list serves as a provocation for discussion rather than a list of solutions to this problem.

Normative Papers

1. The “Ought-Is” Problem: An Implementation Science Framework for Translating Ethical Norms into Practice. Our resident ethicist (Leah Ajmani) loves this paper so much! It basically uses informed consent as a case to describe the larger translational effort needed to move from normative prescriptions to actual implementation.

2. Yes: Affirmative Consent as a Theoretical Framework for Understanding and Imagining Social Platforms. A contemporary classic in CHI,  this paper does a really good job of describing affirmative consent as the ideal situation but then using the “ideal” for explanatory and generative purposes. There is merit to having an ideal, even if it is not perfectly attainable!

HCML Papers

We’re obviously biased because she’s a GroupLenser, but Stevie Chancellor does a great job at describing consent at scale as an ethical tension rather than a “must-have.” It is something researchers need to navigate with justified reasoning.

1. A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media

2. Toward Practices for Human-Centered Machine Learning

Design Papers

These papers are both critical of current consent design and do a great job of discussing alternatives, even if it is outside of a research context.

1. (Un)informed Consent: Studying GDPR Consent Notices in the Field

2. Limits of Individual Consent and Models of Distributed Consent in Online Social Networks

From grappling with moral nuance to designing better consent procedures, these readings can take our discussions of individual consent at scale from a theoretical ideal to an operationalizable goal. So, let’s embrace difficult discourse about how to move forward and continue to traverse the space between the idyllic and the feasible. Comment or tweet which papers you would add to this list!