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 :



Interview with Mike Lee: Communicating AI decision-making through demonstrations

We hear from AAAI/SIGAI Doctoral Consortium participant Mike Lee about his research on explainable AI.
23 April 2024, by

Machine learning viability modelling of vertical-axis wind turbines

Researchers have used a genetic learning algorithm to identify optimal pitch profiles for the turbine blades.
22 April 2024, by

The Machine Ethics podcast: What is AI? Volume 3

This is a bonus episode looking back over answers to our question: What is AI?
19 April 2024, by

DataLike: Interview with Tẹjúmádé Àfọ̀njá

"I place an emphasis on wellness and meticulously plan my schedule to ensure I can make meaningful contributions to what's important to me."

Beyond the mud: Datasets, benchmarks, and methods for computer vision in off-road racing

Off-road motorcycle racing poses unique challenges that push the boundaries of what existing computer vision systems can handle
17 April 2024, by

Interview with Bálint Gyevnár: Creating explanations for AI-based decision-making systems

PhD student and AAAI/SIGAI Doctoral Consortium participant tells us about his research.
16 April 2024, by




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association