In Economics, the concept of externality refers to any indirect effect resulting from an interaction between players and affecting a third party without compensation. Most of the models within which externality has been studied assume that agents have perfect knowledge of their environment and preferences. This is a major hindrance to the practical implementation of many proposed solutions. To adress this issue, we consider a two-players bandit game setting where the actions of one of the player affect the other one. Building upon this setup, we extend the Coase theorem [Coase, 2013], which suggests that the optimal approach for maximizing the social welfare in the presence of externality is to establish property rights, i.e., enabling transfers and bargaining between the players. Nonetheless, this fundamental result relies on the assumption that bargainers possess perfect knowledge of the underlying game. We first demonstrate that in the absence of property rights in the considered online scenario, the social welfare breaks down. We then provide a policy for the players, which allows them to learn a bargaining strategy which maximizes the total welfare, recovering the Coase theorem under uncertainty.

This is a major hindrance to the practical implementation of many proposed solutions.

Market-based agents refer to reinforcement learning agents which determine their actions based on an internal market of sub-agents. We introduce a new type of market-based algorithm where the state itself is factored into several axes called ``goods'', which allows for greater specialization and parallelism than existing market-based RL algorithms. Furthermore, we argue that market-based algorithms have the potential to address many current challenges in AI, such as search, dynamic scaling and complete feedback, and demonstrate that they may be seen to generalize neural networks; finally, we list some novel ways that market algorithms may be applied in conjunction with Large Language Models for immediate practical applicability.

Fox News chief political anchor Bret Baier has the latest on regulatory uncertainty amid AI development on'Special Report.' An increasing number of companies are using artificial intelligence (AI) for everyday tasks. Much of the technology is helping with productivity and keeping the public safer. However, some industries are pushing back against certain aspects of AI. And some industry leaders are working to balance the good and the bad.

In economic theory, the concept of externality refers to any indirect effect resulting from an interaction between players that affects the social welfare. Most of the models within which externality has been studied assume that agents have perfect knowledge of their environment and preferences. This is a major hindrance to the practical implementation of many proposed solutions. To address this issue, we consider a two-player bandit setting where the actions of one of the players affect the other player and we extend the Coase theorem [Coase, 2013]. This result shows that the optimal approach for maximizing the social welfare in the presence of externality is to establish property rights, i.e., enable transfers and bargaining between the players. Our work removes the classical assumption that bargainers possess perfect knowledge of the underlying game. We first demonstrate that in the absence of property rights, the social welfare breaks down. We then design a policy for the players which allows them to learn a bargaining strategy which maximizes the total welfare, recovering the Coase theorem under uncertainty.

Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues that AI image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft.

There's no reason you should have ever heard of Simon Squibb, the "chief purpose officer of the Purposeful Project". Mr Squibb, who describes himself as an "Elon Musk wanna-be" in his Twitter profile, is one of those tirelessly energetic mid-life influencers who proliferate on the petri dish of LinkedIn. Displaying the sort of enthusiasm that Matt Hancock reserves for a Bushtucker Challenge, Mr Squibb is on a mission. "I want to fix the education system", he says. This fix entails removing something that many of us consider quite an important part of the education system: the learning part.

In our understanding, the question of who owns intellectual property rights on AI-related works is also important when determining who is liable for AI-caused harm. For that reason, further development of legislation in that direction is expected. As we mentioned before, one of the main characteristics of AI is the use (collection, analysis) of data. Personal data is included in this. Some experts have opined that AI systems can develop more quickly in jurisdictions where there is less regulation on the use and protection of personal data--or where it is not regulated at all. This is related to the fact that AI needs to use data to achieve the established tasks. The EU is the realm of the General Data Protection Regulation (GDPR), which aims to protect personal data against illegal use. The EU, in light of the GDPR, has already prepared a list of prohibited practices of AI.

Alongside the rapid increase in the number of facial-recognition video sensors being installed in public locations, the protection of a person's biometric data is now escalating into new legal territory. Earlier this week, Seattle, Washington-based Getty Images, a well-known global visual image creator and marketplace, introduced what it says is the image industry's first enhanced model release form. It's a digital document that considers the growing importance of biometric data used for the training of artificial intelligence (AI) and machine learning (ML) applications. This data, when it falls into the wrong hands, can be sold on the black market and used to facilitate identity theft and in ways that lead to personally targeted ransomware, malware and other types of cyberattacks. Developed with input from the Digital Media Licensing Association (DMLA), which supports business standards in visual content, the new form provides clarity and guidance regarding how data, including visual content, can be tracked and handled appropriately to protect the personal and biometric data captured by content creators.