Author Archives: Stevie Chancellor

Wordy Writer Survival Guide: How to Make Academic Writing More Accessible

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As GroupLensers received CHI reviews back, many of us were told our papers were “long,” “inaccessible,” and even “bloated.” These critiques are fair. Human-Computer Interaction (HCI) research should be written for a broad and interdisciplinary audience. However, inaccessible writing can be hard to fix, especially if it is your natural writing style. Here’s some advice from GroupLens’s very own Stevie Chancellor (computer science professor, PhD advisor, and blogger about everything writing-related)

Sentence Structure

  • Sentence Length: How long are your sentences, and how many comma-dependent clauses are going on per paragraph? Long sentences are more complicated to read and, therefore, harder to parse. Some people say to split any sentence with more than 25 words. Eh. 30-35 should be fine for academic writing, but longer is worser. 
  • Commas, Commas, Commas: Comma-separated clauses are painful to follow. A comma is a half-stop in writing and momentarily pauses trains of thought. While some commas are grammatically necessary (see the one that follows this parenthesis), too many commas chop your sentences into pieces. Therefore, too many commas interrupt your reader’s comprehension of your idea.
  • Sentence Cadence: How are you varying your cadence of the writing? Do you use short sentences, then longer sentences, and vary the structure and placement of comma clauses? Using ONLY long sentences gets repetitive and, therefore, more challenging to read.
  • Topic Sentence and Transition Clarity: Topic and “transition” sentences should be crystal clear in their simplicity. Interior sentences can be more elaborate/have more “meat.”

Word Choice

  • Simple Words are Better: Are we using as simple words as possible to describe what we mean? For example: do not write “utilize” as a synonym for “use”. Just say “use”. 
  • Active vs. Passive Voice: Are you overly using the passive voice and not active? Passive voice is occasionally correct, especially when needed to soften a claim (e.g., “Research has suggested that….”). But too much passive voice is hard to read.
  • Filler Words: Look for words that contribute nothing to the idea but make your sentence longer. Adverbs and fluffy adjectives are common culprits of this. Adverbs like “very”, “fairly”, and “clearly” provide almost NO substance to writing but lengthen the sentence.
  • Weasel Words: Inspired by Matt Might, check your writing for “weasel words” that augment the clarity of your sentence. Do you need to say an experiment was “mostly successful, but had limitations?” Or can you say, “The experiment was successful in X and Y with less success in Z”?
  • Citations vs. Names: Be judicious with \citet{} in your writing. Invoking someone’s name is equivalent to inviting that person to a dinner party and forces the reader to pay attention to the “who’s who” of your writing. Who do you want to invite to your home? Remember, you’re in charge of maintaining conversation during the party and providing food for everyone, so be careful who you invite.

Pragmatic Decisions/Actions

  • Read Aloud: Read “dense” or “inaccessible” sections out loud. Say them with your mouth. Long, poorly structured paragraphs become obvious when read out loud.
  • Use a Friend or Colleague To Kill Your Darlings: Friends and colleagues with no emotional connection to the paper are great for removing self-indulgent yet non-essential writing. Ask a friend to read a section to go in and “kill your darlings.”
  • Use AI Tools Judiciously: Tools such as Grammarly Pro, Writefull, or ChatGPT/Bard/LLM du jour can do first passes for wordiness and phrasing. For example, Grammarly Premium provides swaps for too-long phrases (and is free if you have a SIGCHI membership). LLMs can trim your writing by 10%. Just be cautious in the accuracy of the edits and maintain the same tone and argumentation.
  • Ctrl + F Is Your Friend: Recognize your writing “quirks” and ctrl + f to search for and cut them. Stevie’s writing quirks include using adverbs in initial drafts, meaning that searching for “very” and “ly” returns many words to cut.

From managing sentence structure to choosing simple words, these tips can take your writing from “in the clouds” to a reader-friendly and enjoyable experience. Remember, the goal is not just brevity but clarity, ensuring that our work resonates with a broad and interdisciplinary audience. So, let’s embrace these tips, Ctrl + F our way through, and invite our readers to a well-organized and engaging intellectual dinner party. Cheers to more accessible and impactful HCI research!

Towards Practices for Human-Centered Machine Learning

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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.