Concern 397 · Working draft · editorial review in progress
Complexity & jargon defaults
Jargon-heavy AI output excludes people when specialised words or complicated phrasing are used without useful explanation.
Understanding the concern
A model may imitate expert writing even when the reader is new to the topic. The answer can sound impressive while leaving the person unable to explain the idea or act on it. This is different from giving necessary detail: the problem is failing to connect that detail to what the reader already understands.
A good explanation introduces the familiar idea first, defines a new term and shows why it matters. It is like labelling the parts of a tool while showing how to use it, rather than handing someone a list of technical names. Plain language should preserve important uncertainty and avoid talking down to adults.
An example
An answer says a system has poor calibration without explaining that its confidence does not reliably match how often it is correct.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
The guidance supports clear language and structure. The example is an original illustration, not a finding about one model.
World Wide Web Consortium · Accessibility guidance
Making Content Usable for People with Cognitive and Learning Disabilities ↗ (opens in a new tab)Design, language, structure and user testing; supplemental guidance rather than a single diagnostic rule.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Could a new reader explain the main idea in their own words and know what question to ask next?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Justice
Justice means fair treatment, fair procedures, and attention to who receives benefits and burdens. It asks whether people with less power can be heard and seek repair.
Look at outcomes across different groups and individual cases. Provide a usable way to challenge decisions and correct harm. Equal wording in a policy does not guarantee equal treatment in practice.
Ask together: Who benefits, who bears the cost, and who can challenge the decision?
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Listening
Listening means trying to understand someone's experience before preparing an answer. People who use a service, work with it, or are judged by it may notice problems its designers miss.
Invite people to describe what happened in their own words. Repeat back what you understood and let them correct it. A quiet person or a complaint that is hard to measure may still reveal something important.
Ask together: Whose experience have we not heard yet?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
Search for more writing
For more specific or recent material, open a search below or ask for a web summary. AI-generated summaries can contain mistakes; open the original articles to check them.