RE: Latest editor's draft of "Ethical Principles for Web Machine Learning"

Thank you, Steve. I have proposed to include a general AI-related agendum
for next week's meeting, alongside a more specific topic to discuss the Web
Machine Learning draft.

 

From: Steve Noble <steve.noble@pearson.com> 
Sent: Thursday, 9 February 2023 10:36
To: public-rqtf@w3.org
Subject: Re: Latest editor's draft of "Ethical Principles for Web Machine
Learning"

 

Thanks for sending this link along, Jason. By curious happenstance, I ended
up in a meeting late yesterday with one of the authors cited in the draft,
Ninareh Mehrabi (co-author of "A Survey on Bias and Fairness in Machine
Learning" https://arxiv.org/pdf/1908.09635.pdf). Looks like he has also
co-authored other publications along such lines, for instance "Ethical
Considerations in AI-Based Recruitment" (DOI:
10.1109/ISTAS48451.2019.8937920
<https://doi.org/10.1109/ISTAS48451.2019.8937920> ). [We may want to add
that to the bibliography, by the way.]

 

Ninareh Mehrabi currently has a newly funded National Science Foundation
project
<https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235916&HistoricalAwards=f
alse>  which he was discussing. Here's a summary paragraph about their
project:

"Disfluencies in speech are common artifacts in conversation, but they are
especially prevalent in individuals who stutter, a community of more than 70
million people worldwide. It is well-documented that people who stutter
consistently experience employment discrimination, diminished labor market
outcomes, and societal stigma. The increasingly pervasive use of
exclusionary voice-activated artificial intelligence (AI), which are
designed, trained, and tested without considering communication that varies
from societal norms, can act as a barrier to daily life participation and
employment access for communities such as individuals who stutter. Worse,
such technology can actively discriminate against people with speech
differences in employment contexts. Therefore, there is an immediate and
compelling need for efforts to reduce these barriers and empower people with
communication differences and disorders to fully and equitably access all
forms of speech recognition systems, including personal voice assistants,
automated phone interfaces, and job-preparation and hiring software."

 

He had some great observations about the very real ethical considerations of
the societal barriers that current AI technology is placing for people with
speech related disabilities. It made for an interesting connection to the
great presentation that Lionel provided in our RQTF meeting earlier the same
day.

 

--Steve 

 

                             

 


Steve Noble


Principal Researcher, Accessibility
Psychometrics & Testing Services

Pearson



502 969 3088
steve.noble@pearson.com <mailto:steve.noble@pearson.com> 

	



 <https://beta.nsf.gov/funding/initiatives/convergence-accelerator> 

	
	
	
	
  _____  


From: Jason White <jason@jasonjgw.net>
Sent: Wednesday, February 8, 2023 11:53 AM
To: public-rqtf@w3.org <public-rqtf@w3.org>
Subject: Latest editor's draft of "Ethical Principles for Web Machine
Learning" 

 

https://webmachinelearning.github.io/webmachinelearning-ethics/

(Revised as of November last year.)