Why AI Owners and AI Users Have Conflicting Interests?

If you make and commercialize high quality #AI, you are also likely to have conflicting interests with users. Here’s why. #MachineLearning #AIeconomics #incentives #economics pic.twitter.com/J8oorXo1dh
— ivanjureta (@ivanjureta) February 22, 2018
Section 5 specifies the content of the summary report to be submitted about an automated decision system. This text follows my notes on Sections 1 and 2, Section 3 and Section 4 of the Algorithmic Accountability Act (2022 and 2023). This is the fourth of a series of texts where I’m providing a critical reading…
This short interview on my research on decision making and use of it in companies, was done in 2018 with fnrs.tv, part of the Belgian Fonds de la Recherche Scientifique – FNRS, in Brussels. Each of my first two academic books led to the founding of a spin-off; see the books here.
Understanding how individuals choose goals can provide insight into human decision-making and inform strategies for influencing the choice of goals through decision governance.
IP compliance requirements on generative AI reduce the readily and cheaply available amount of training data, with a few consequences on how product development and product operations are done.
If competence shortens learning, then its value is proportional to the cost of learning, that is, of iterations that would have been needed to achieve the effects of competence, but without having access to it.
Let’s assume that we need an incentive mechanism to stimulate employee performance by allocating bonuses based on peer opinion. How do we design it?