Original article was published by Alex Moltzau 莫战 on Artificial Intelligence on Medium
Watson NLP Key Point Analysis May Exacerbate Social Inequality If Used Wrongly for Social Analysis
A quick look at the split of IBM into two separate public companies and the recent press release Watson NLP Key Point Analysis
A lot seems to be happening with IBM lately. The announcement to split into two separate public companies is one interesting move. This article is a short look at IBM splitting into two public companies and further a longer critical look at the commercialisation of Watson NLP.
Just to be very clear:
In this article I do not state that Watson NLP Key Point Analysis cannot be used for social analysis. I am simply looking at their recent press release with a very critical eye as a social scientist. I think programming can be very useful when used critically to discuss social data.
Splitting IBM & NewCo
Splitting digital infrastructure operations into NewCo (temporary name) and then these other operations (software?) into IBM may put extra pressure on the software division to deliver results.
Earlier this year in April it was written in Business Insider that Amazon’s cloud generated over $10 billion in net quarterly sales for the first time ever — up 33% from a year ago.
Wedbush Security analyst Moshe Katri commented:
“IBM is essentially getting rid of a shrinking, low-margin operation given the cannibalizing impact of automation and cloud, masking stronger growth for the rest of the operation.”
In Forbes it was said by Peter Bendor-Samuel:
“The open source AI operating model and the organization it takes to drive and succeed in that business is a different kind of organization than is necessary for managing and modernizing legacy environments.”
What will this entail? Does it put more pressure on marketing software?
One way to look at it is the recent commercialisation of software products — for example with IBM Watson.
I am interested this time around in a recent press release that IBM plans to commercialise Key Point Analysis inside Watson NLP products including Watson Discovery.
The IBM Press Release is Highly Debatable
The press release was titled:
“IBM Watson Demonstrates New Natural Language Processing Advancement in Premiere of “That’s Debatable”
“That’s Debatable” is a new, limited series presented by Bloomberg Media and Intelligence Squared U.S. sponsored exclusively by IBM.
It was supposed to provide insight into the global public opinion on the motion:
“It’s time to redistribute the wealth.”
It features industry leaders, economists, policy makers and public intellectuals debating some of today’s most pressing issues.
What the people think or what the science says?
It is debatable can be said about a lot of topics.
Is IBM attempting to sell this technology to the U.S. market engaged in campaigning?
What do they want to gain through this promotional campaign?
One of the largest companies in the world sponsoring a debate about redistribution of wealth… Well, I am not sure!
Is aggregating opinion data the best way to decide if redistribution of wealth is great or not?
As a side note Salesforce wanted to use AI to create the best tax policy:
IBM is not the first attempting social commentary through aggregating data on either opinion or artificial decision-making environments.
There is a quote by historian Jill Lepore in Nature that I find fitting:
“Ignorance of history is a badge of honour in Silicon Valley.”
She looks back at propaganda, and how the field with similar processes changed name to mass-communications in the U.S. Alongside the people working for Kennedy in his campaign.
Another fitting sentence is from a title by Pratyusha Kalluri:
“Don’t ask if artificial intelligence is good or fair, ask how it shifts power.”
On that note, enter “That’s Debatable.”
One of the tag-lines from IBM press release was:
“Bringing More Global Voices To The Debate.”
Where are these voices from?
There are no numbers clearly presented to show such an ambition.
The show used Key Point Analysis, NLP from IBM Research, to determine the main points from text submitted by 3,500 submissions people prior to the debate.
“Of the 3,500 submissions, there were 1,600 usable arguments and 20 key points identified.”
This prompted an exchange by debaters.
On another side note, it is possible to contribute to the next debate on this link:
This link may of course disappear once the project is over.
In the first debate mention above (on wealth redistribution) the technology identified 56% were for redistributing wealth and 44% were against redistributing wealth.
Here is an excerpt for information:
- “56 percent of arguments analyzed were for redistributing wealth, with approximately 20 percent of analyzed submissions arguing that there is currently too much wealth inequality in the world. One argument was that income inequality has increased dramatically over the past few decades, causing excessive suffering to large populations, and that if wealth is not redistributed, far greater will suffer.
- The remaining 44 percent of analyzed arguments were against the motion, with 15 percent of those arguing that redistributing wealth would discourage some people from working hard. One example argument in support of this is that redistributing the wealth discourages individual initiative, entrepreneurship, and accountability for choices.”
I find it somewhat troubling that when large generated algorithms with some of the most acknowledge language models in the world cannot differentiate racism very well, then AI based on text analysis is supposed to be helpful in arguing about wealth redistribution.
This is the pipeline presented by IBM.