Machine Learning X Doing

Where AI meets ROI in healthcare.

How can leading soccer video game franchises have dramatically improved player experiences with economics? Read our .
Machine Learning X Doing proposal and get in touch: https://machinelearningxdoing.com/insights/video-game/soccer-video-game-economics/

Soccer Video Game Economics – Machine Learning X Doing Soccer Video Game Economics Proposal for Enhancing Soccer Sports Game Design Through Randomized Controlled Trials and Economic Theory Introduction The gaming industry is rapidly evolving, and with it, the expectations of players are also rising. In the realm of soccer sports games, major franchises,...

Don’t know what to do when facing data that almost looks like a waterfall? Not sure how to make sense of it all when the data is constantly coming in? The good news is, linear contextual bandits can be used to design randomized controlled trials. Unfortunately, the existing methods have limitations when analyzing live-streamed data. At Machine Learning X Doing, we have created a more robust approach for linear contextual bandits in real-time data applications, using regression discontinuity design for estimation & exploration.

Read our paper:
https://machinelearningxdoing.com/livestream-contextual-bandits-meet-regression-discontinuity-designs/

Important research by Kweku Opoku-Agyemang: thousands of patients are in need of kidney transplants, and thousands of individuals are willing to donate kidneys (sometimes on the condition that kidneys are allocated a certain way). However, kidneys can only be allocated to compatible patients, and there are always more people in need of kidneys than willing donors. How should kidneys be allocated with algorithms?

He proposes a framework – computational ethics – that specifies how the ethical challenges of AI can be addressed better by incorporating the study of how humans make moral decisions.

The driver of this framework is a computational version of reflective equilibrium.

The goal of this framework is twofold: (i) to inform the engineering of ethical AI systems, and (ii) to characterize human moral judgment and decision-making in computational terms.

Working jointly towards these two goals may prove to be beneficial in making progress on both fronts. Read the paper:

https://www.sciencedirect.com/science/article/pii/S1364661322000456 #:~:text=The%20proposed%20framework%20of%20computational,computational%20models%20to%20represent%20moral