Hi Aristea, The following may help: - Leidner, J. L., & Plachouras, V. (2017, April). Ethical by design: ethics best practices for natural language processing. In *Proceedings of the First ACL Workshop on Ethics in Natural Language Processing* (pp. 30-40). - Hovy, D., & Spruit, S. L. (2016, August). The social impact of natural language processing. In *Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)* (pp. 591-598). Best, Ali Aristea Fotopoulou <A.Fotopoulou@brighton.ac.uk>, 27 Eyl 2019 Cum, 14:43 tarihinde şunu yazdı:
Dear all,
Just revisiting this thread, and noticed some folk raised ethical issues in relation to sentiment analysis and emotional AI. I am looking for literature on the ethics of research using sentiment analysis tools and social media data (but also other types of data) and can¹t find much out there - have you come across anything or, better even, are you working on these issues?
Andrew, I had a look at your website and approach - the critical issues you raise are interesting (summed up as 'is this OK?¹) and I¹d like to know more about how your team answers these questions but I can¹t find any answers. Are there any papers on methodologies and ethics at all coming out of the project?
Many thanks, Aristea
-----------------------------
Dr Aristea Fotopoulou UKRI-AHRC Innovation Leadership Fellow PI ART/DATA/HEALTH: Data as creative material for Health & Wellbeing 2019-2021 University of Brighton, School of Media Watts Building, Lewes Road, Brighton BN2 4GJ
A.Fotopoulou@brighton.ac.uk |@aristeaf | https://aristeafotopoulou.org ART/DATA/HEALTH Research project: http://artdatahealth.org <http://artsdatahealth.org>
On 12/09/2019 11:01, "Air-L on behalf of Charles M. Ess" <air-l-bounces@listserv.aoir.org on behalf of c.m.ess@media.uio.no> wrote:
Dear all - a belated but most sincere thanks for all of this!
I'll bundle up the thread, forward it to my student, and see what sense we might be able to make of it all.
Again, many thanks indeed and all best, - charles
On 07/09/2019 13:53, Shulman, Stu wrote:
Sally,
Machine generated sentiment analysis scores are sometimes abused as a shortcut to avoid certain forms of manual/mental labor in a variety of commercial and academic contexts. Language tools are in this scenario treated as a magic buttons to be deployed against corpora in the name of charts untouched by serious validation. I prefer it when humans are in-the-loop, which itself is recursive (meaning you repeat until there is no room to improve), using tools as filters to generate purposive samples that humans annotate and collectively validate using a systematic process.
Sentiment problems range from hard to harder and hardest, where hardest means you cannot do it in a manner that can be validated by any means. There is no easy on this scale of tasks if false positive or negatives could cost a life or some other serious consequence, but to make it easier, requires a process, grossly boiled down below:
1. Collect a relevant and representative corpus of data, 2. Build a SPAM detection classifier to remove non-relevant data (ex., wrong language OR no discernible sentiment), 3. Build a topic classifier and focus on one key topic first (not all topics at once), 4. Solve the Rubik's cube of how many codes and what they really mean (ex., happy/sad OR angry/frustrated/both/neither...), 5. Test the topic-specific annotation scheme with a group of no less than five independent annotators (not just two), 6. Crowd source the task to larger groups when possible, using memo writing to identify boundary cases that kill/modify models, 7. Use iteration to identify elite annotators through recursive validation, memo reviews, and scoring against a gold standard.
The goal is to build task- and language-specific machine classifiers using the best possible human experts in the process. The main idea, however, is to keep a critical role for humans.
~Stu
On Thu, Sep 5, 2019 at 4:11 PM Dr. S.A. Applin <sally@sally.com <mailto:sally@sally.com>> wrote:
Dear Charles (and List),
I see this as an ethics issue.
How reliable are ³emotion analysis² tools? How would outcomes from them be used?
As you say, there is a lack of clarity in some in terms of ³explaining emotional categories.² To me, this signals (along with obvious knowledge about the limitations and problems with algorithms), that there is opportunity here to be very, very, very wrong about people¹s opinions, and any algorithmically interpreted ³emotional² state.
For example, how would one interpret or finesse ³frustration,² vs ³anger²? The written word is contained within a language. Not all commenters will be native speakers to that language, and not all native speakers have the language tools required (even within their own language) to adequately express themselves, even in the best of times. What makes anyone think an algorithm would do better at this than a human trained in qualitative methods and with cultural and media and language knowledge?
There is way too much margin of potential error here for this to be automated, or ³useful.² It is much more likely that things will be assumed incorrectly by limited algorithms in the first place.
Furthermore, does your student see any problem with this exercise? That their tool analysis might get it very wrong? That the wrong might lead to assumptions or outcomes that are harmful to entities, people, governments?
What safeguards are in place for wrong assumptions and outcomes?
Kind regards,
Sally
Sally Applin, Ph.D. .......... Research Fellow HRAF Advanced Research Centres (EU), Canterbury Centre for Social Anthropology and Computing (CSAC) .......... Research Associate Human Relations Area Files (HRAF) Yale University .......... Associate Editor, IEEE Consumer Electronics Magazine Member, IoT Council Executive Board Member: The Edward H. and Rosamond B. Spicer Foundation .......... http://www.posr.org http://www.sally.com I am based in Silicon Valley .......... sally@sally.com <mailto:sally@sally.com> | 650.339.5236
> On Sep 5, 2019, at 3:52 AM, Charles M. Ess <c.m.ess@media.uio.no <mailto:c.m.ess@media.uio.no>> wrote: > > Dear colleagues, > > One of our students is wanting to analyze emotional content in in the comment fields of a major newspaper vis-a-vis specific hot-button issues. > > She has a good tool (I think) for scrapping the data - but she is stymied over the choice of an emotion analysis tool. She has looked at Senpy (http://senpy.gsi.upm.es/#test) and Twinword <https://www.twinword.com/api/emotion-analysis.php> - the latter seems the most accurate, but it is also expensive. > She has recently discovered DepecheMood emotion lexicons (Staiano, J., & Guerini, M. (2014). Depechemood: a lexicon for emotion analysis from crowd-annotated news. arXiv preprint arXiv:1405.1605.) - but this suffers from a lack of clarity in terms of explaining its emotional categories: awe, indifference, sad, amusement , annoyance, joy, fear and anger. > > For my part, I am entirely clueless. Any suggestions that she might pursue would be greatly appreciated. > > best, > - charles ess > -- > Professor in Media Studies > Department of Media and Communication > University of Oslo > <http://www.hf.uio.no/imk/english/people/aca/charlees/index.html
> > Postboks 1093 > Blindern 0317 > Oslo, Norway > c.m.ess@media.uio.no <mailto:c.m.ess@media.uio.no> > _______________________________________________ > The Air-L@listserv.aoir.org <mailto:Air-L@listserv.aoir.org> mailing list > is provided by the Association of Internet Researchers http://aoir.org > Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org > > Join the Association of Internet Researchers: > http://www.aoir.org/ >
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