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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!

Toward Practices for Human-Centered Machine Learning from CACM on Vimeo.

People are excited about human-centered AI and machine learning to make AI more ethical and socially appropriate. AI has captured the popular zeitgeist with promises of generalized artificial intelligence that can solve many complex human problems. These promises of ML, however, have had negative consequences, with both ridiculous and catastrophic failures – they rack up so fast that colleagues are keeping AI Indicents databases, reports of AI ethics failures, and more to boot.

How will ML researchers and engineers avoid these problems and move towards more compassionate and responsible ML? There aren’t many concrete guidelines on what it looks like to do human-centered machine learning in practice. And while there are some pragmatic guides, they often lack the connection between technical and social/cultural/ethical focus.

In my recently published CACM article, I argue that there is a gap in building human-centered systems – the gap between the values we hold but don’t have actionable methods and technical methods that don’t align with our values. The paper argues for practices bridging the ever-significant value and the focus of ever-practical methods.  

This paper synthesizes my CS and Critical Media Studies background in thinking about how we should DO HCML. It also builds on my decade of research experience in human-centered research in a challenging area – predicting and acting on dangerous mental health behaviors discussed on social media data.  It builds on classical definitions of human-centeredness in defining HCML and lays out five practices for researchers and practitioners. These practices ask us to prioritize technical advancements EQUAL TO our commitments to social realities. In doing this, we can make genuinely impactful technical systems that meet people and communities where they’re at.

Here are the five big takeaways from the paper and the practices you can implement immediately.

1. Ask if machine learning is the appropriate approach to take 
2. Acknowledge that ML decisions are “political”
3. Consider more than just a single “user” of an ML system
4. Recognize other fields’ contributions to HCML 
5. Think about ML failures as a point of interest, not something to be afraid of

Let’s dig into one of these that seems – considering more than just a single “user” of an ML system. When considering who “uses” a system, we often only consider the person commissioning or building the system. Even in HCI, we talk about “users” of systems and (if lucky) the people whose data goes into the model. However, many systems have much larger constellations of people “involved” in the ML model. For example, the “user” may be a government or business in facial recognition technology. But the people whose faces are in that system are also “users” of the technology. Likewise, if that facial recognition system is used in an airport to screen passengers for flight identification, everyone who walks by ambiently may interact with it. The existing ML system meaningfully impacts a user who chooses NOT to interact with that system – if opting out means they must spend more time in airport security or have their identity scrutinized more closely. Both examples make it clear that with the consideration of multiple stakeholders involved in the ML model, we should consider all the stakeholders whose data goes into creating the model.

I aim for these principles to inspire action – to encourage more profound research, empirical evaluations, and new ML methods. I also hope the practices make human-centered activities more tractable for researchers AND practitioners. I hope this inspires you and your colleagues to ask hard questions that may mean making bold decisions, taking action, and balancing these competing priorities in our work. 

You can read more about this paper in the recently published Featured Article in the Communications of the ACM here.