ΑΙhub.org
 

How to avoid hype when promoting your AI research


by
25 October 2021



share this:
microphone in front of a crowd

Hype around AI sets inflated expectations about the technology, drives unnecessary fears and detracts from the meaningful discussions that need to happen now, about the technology actually being developed today.

The AIhub trustees have compiled a handy guide to help you avoid hype when communicating your research. Here are their 10 tips:

1. Be specific about the science and achievements

What problem is your research trying to solve? Provide context.

2. Don’t make exaggerated claims

Try to avoid unnecessary superlatives such as: “general, best, first” unless you can provide supporting context.

3. Be clear about the limitations of your experiments

Did your demonstration require external instruments that made the real world “more digital” (for example, external sensors/motion capture)?

4. Explain how things work

What data was used, what type of algorithms, what hardware? Be upfront about the computational cost.

5. Has your research been validated by the community?

Does the community support your findings, through peer-reviewed research or other means?

6. Make your headline catchy but accurate

Prioritise scientific accuracy.

7. Keep any debates scientific

Don’t bring personalities/personal attacks into the debate.

8. Don’t anthropomorphize

Avoid anthropomorphism unless the subject of the research is people.

9. Use relevant images

Use images from your research to illustrate your news. Avoid generic or stereotypical AI images (such as imaginary robots from science fiction).

10. Be open and transparent

Disclose conflicts of interest and/or funding especially if industry or personal interests are involved.

You can find all of the guidelines in this pdf document.



tags: ,


AIhub is dedicated to free high-quality information about AI.
AIhub is dedicated to free high-quality information about AI.




            AIhub is supported by:



Related posts :



We asked teachers about their experiences with AI in the classroom — here’s what they said

  05 Dec 2025
Researchers interviewed teachers from across Canada and asked them about their experiences with GenAI in the classroom.

Interview with Alice Xiang: Fair human-centric image dataset for ethical AI benchmarking

  04 Dec 2025
Find out more about this publicly-available, globally-diverse, consent-based human image dataset.

The Machine Ethics podcast: Fostering morality with Dr Oliver Bridge

Talking machine ethics, superintelligence, virtue ethics, AI alignment, fostering morality in humans and AI, and more.

Interview with Frida Hartman: Studying bias in AI-based recruitment tools

  02 Dec 2025
In the next in our series of interviews with ECAI2025 Doctoral Consortium participants, we caught up with Frida, a PhD student at the University of Helsinki.

Forthcoming machine learning and AI seminars: December 2025 edition

  01 Dec 2025
A list of free-to-attend AI-related seminars that are scheduled to take place between 1 December 2025 and 31 January 2026.
monthly digest

AIhub monthly digest: November 2025 – learning robust controllers, trust in multi-agent systems, and a new fairness evaluation dataset

  28 Nov 2025
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

EU proposal to delay parts of its AI Act signal a policy shift that prioritises big tech over fairness

  27 Nov 2025
The EC has proposed delaying parts of the act until 2027 following intense pressure from tech companies and the Trump administration.



 

AIhub is supported by:






 












©2025.05 - Association for the Understanding of Artificial Intelligence