Time-Proportion Value Increase of the Journalist

See [[Week 1 - Introduction]] or the [[Main AI Page]] Mainly relevant to the [[Master of Philosophy - Main Page]]

#AIBusinessCase

tl;dr AI + MR applied to news media could vastly increase the number of journalists

If currently a journalist's time is divided among 10 tasks per day:

• 6 are administrative or production = A
• 3 are purely investigative = B
• 1 is miscellanious = C

Or 6A + 3B + 1C = 1J

And the weighted value of those tasks are:

• A =2
• B = 5
• C = 1

Then currently a journalist equals 23 units of value per day:

(6*2) + (2*5) + (1*1) = 23

If we could turn 3 of those admin tasks into 3 purely investigative tasks of the same time allotment by using AI and ML, what would be the impact on the journalist's value per day?

3A + 6B + 1C = 1J (3*2) + (6*5) + (1*1) = 37

Each journalist would become worth 37 units of value per day.

Further analysis - The difference in diminishing marginal returns between A and B

There's a possibility that the point of diminishing marginal returns for each new journalist hired becomes significantly impacted by the change from primarly administrative tasks to investigative tasks, as the former is less likely to gain revenue compared to the latter.

It is therefore possible, upon investigating the microeconomic reality, to discover that the number of journalists employed could rise in situations where AI and ML technologies are employed, as the number of new journalists increases revenue for the news media outlet for longer.

Also Reduced training costs as journalists need to know less about the mechanics of the CMS - the CMS is handling itself just fine, thank you.

An example with Engineers

[[Woodside - AI causes growth in jobs]]

To give an example of say machine learning in action today, how companies have actually implemented it, there's one example that I always love to go back to, and it is the example of Woodside Energy, a company in the Australia New Zealand region.
Now originally, they actually contacted IBM because they wanted the IBM to be able to create essentially a system that can understand the different documents and the research that they're engineers come up with, and have Watson and understand that, and essentially replace some of the engineers on their team. IBM actually went ahead and build the application that worked to Watson was able to understand that unstructured content, but they never ended up replacing any of their engineers. Instead, they actually ended up hiring more engineers, because now they realized that two things. First of all, the barrier of entry for each engineer is now lower and knowledge can now be shared more effectively among the teams. 

Time gains even within the core competencies

According to Saad and Issa (2020), the Associated Press claims AI-assisted workflows have freed up 20% fact-checking time for use in content and story-telling.