How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

The Assistive Multi-Armed Bandit

Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they want. In this work, we introduce the assistive multi-armed bandit, where a robot assists a human playing a bandit task to maximize cumulative reward. In this problem, the human does not know the reward function but can learn it through the rewards received from arm pulls; the robot only observes which arms the human pulls but not the reward associated with each pull. We offer sufficient and necessary conditions for successfully assisting the human in this framework. Surprisingly, better human performance in isolation does not necessarily lead to better performance when assisted by the robot: a human policy can do better by effectively communicating its observed rewards to the robot. We conduct proof-of-concept experiments that support these results. We see this work as contributing towards a theory behind algorithms for human-robot interaction.Comment: Accepted to HRI 201

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

A (Dangerous) New Normal—Public Safety Power Shutoffs (PSPS): A Look into California Utility De-energization Authority and the Potential for its Abuse

Over the past few years, the State of California has endured some of the worst fire seasons on record. In 2020, over one million acres burned across the San Francisco Bay Area, Northern California, and the Central San Joaquin Valley—conditions which created a public safety and health emergency in the midst of an ongoing pandemic. In 2019, the Kincade Fire set ablaze nearly 78,000 acres in Northern California, but coincided with widespread power shutoffs impacting millions of customers throughout the state. In 2018, we learned of the devastation in Butte County, where the Camp Fire destroyed the community of Paradise, California, and claimed the lives of eighty-six people. To confront wildfire threats of such magnitude, the three largest energy providers in California—Pacific Gas & Electric, Southern California Edison, and San Diego Gas & Electric—have executed Public Safety Power Shutoffs (de-energization) as one response to mitigate public safety concerns. The California Public Utilities Commission (CPUC) oversees all utilities operating within the state. The CPUC authorizes PG&E, SCE, SDG&E, and all other intrastate utilities to de-energize their power lines, but only as a measure of last resort when dangerous fire conditions present an imminent threat to public safety. While de-energization serves as a useful tool, it equally carries the potential for abuse. PG&E stands as a notable example. The CPUC evaluates de-energization execution for reasonableness but does not inquire into other critical areas of relevant information: the condition of electrical infrastructure, utility infrastructure repairs or investments performed, or the financial status of the de- energizing utility. To ensure utilities remain committed to their regulatory duties of promoting public safety by delivering safe and reliable power to the public, this Note recommends the CPUC incorporate an infrastructure investment inquiry into its de-energization reasonableness review. As our electrical grid deteriorates, environmental conditions worsen, and PG&E (the largest of the three utility providers in California) emerges from bankruptcy, the danger of de-energization becoming a general utility wildfire response continues to increase. Without closer utility infrastructure scrutiny public safety stands at risk—and at the whim of utility discretion

A Literature Survey on Web Content Mining

Web is an accumulation of inter related documents on one or more web servers while web mining implies extricating important data from web databases. Web mining is one of the data mining spaces where data mining methods are utilized for extricating data from the web servers. The web information incorporates site pages, web links, questions on the web and web logs. Web mining is utilized to comprehend the client behavior, assess a specific site in view of the data which is stored in web log documents. Web mining is assessed by utilizing data mining strategies, specifically Association Rules, Classification and Clustering. It has some helpful regions or applications, for example, Electronic trade, E-learning, E-government, E-arrangements, E-majority rules system, Electronic business, security, crime examination and computerized library. Recovering the required web page from the web productively and adequately becomes a challenging task since web is comprised of unstructured information, which conveys the substantial measure of data and increment the unpredictability of managing data from various web service providers. The accumulation of data turns out to be elusive, extract, channel or assess the significant data for the clients. In this paper, we have considered the essential ideas of web mining, classification, procedures and issues. Notwithstanding this, this paper likewise broke down the web mining research challenges