How Machine Learning Mitigates Racial Bias in the U.S. Housing Market

Since July 2007, the world economy has experienced a severe financial crisis that originated in the U.S. housing market. Subsequently, the crisis has spread to financial sectors in European and Asian economies and led to a severe worldwide recession. The existing literature on financial crises rarely distinguishes between factors that create the original strain on the financial sector and factors that explain why these strains lead to system-wide contagion and a possible credit crunch. Most of the literature on financial crises refers to factors that cause an original disruption in the financial system. We argue that a financial crisis with its contagion within the system is caused by failures of legal, regulatory, and political institutions.

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Real-Time V2V Communication With a Machine Learning-Based System for Detecting Drowsiness of Drivers

International Journal of Interdisciplinary Telecommunications and Networking ◽

10.4018/ijitn.2021100104 ◽

2021 ◽

Vol 13 (4) ◽

pp. 35-50

Author(s):

Ahmed Y. Awad ◽

Seshadri Mohan

Keyword(s):

Machine Learning ◽

Traffic Accidents ◽

Ad Hoc ◽

Economic Losses ◽

Drowsy Driving ◽

The Road ◽

V2v Communication ◽

Physical Injuries ◽

On The Road ◽

The U.S

This article applies machine learning to detect whether a driver is drowsy and alert the driver. The drowsiness of a driver can lead to accidents resulting in severe physical injuries, including deaths, and significant economic losses. Driver fatigue resulting from sleep deprivation causes major accidents on today's roads. In 2010, nearly 24 million vehicles were involved in traffic accidents in the U.S., which resulted in more than 33,000 deaths and over 3.9 million injuries, according to the U.S. NHTSA. A significant percentage of traffic accidents can be attributed to drowsy driving. It is therefore imperative that an efficient technique is designed and implemented to detect drowsiness as soon as the driver feels drowsy and to alert and wake up the driver and thereby preventing accidents. The authors apply machine learning to detect eye closures along with yawning of a driver to optimize the system. This paper also implements DSRC to connect vehicles and create an ad hoc vehicular network on the road. When the system detects that a driver is drowsy, drivers of other nearby vehicles are alerted.

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Predatory Inclusion

Race for Profit ◽

10.5149/northcarolina/9781469653662.003.0008 ◽

2019 ◽

pp. 253-262

Author(s):

Keeanga-Yamahtta Taylor

Keyword(s):

African Americans ◽

Real Estate ◽

Housing Market ◽

Black People ◽

Home Ownership ◽

Rental Market ◽

American Society ◽

The U.S

Homeownership in the U.S. is often touted as a means to escape poverty, build wealth, and fully participate in American society. However, racism in the broader American society ultimately resulted in a racist housing market that excludes Black people from homeownership and depresses the value of property inhabited by African Americans. The perception that Black buyers are risky has continued to fuel predatory practices in real estate. The author notes that African Americans should not be limited to the rental market because of inequality in the housing market. Instead, she suggests people should question American society, a society in which full citizenship is reliant upon home ownership.

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Comparing a Machine Learning Predictive Model with Federal Transit Administration (FTA)’s Default Useful Life Benchmark to Predict Replacement Costs for Revenue Vehicles

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120906410 ◽

2020 ◽

Vol 2674 (2) ◽

pp. 181-190

Author(s):

Dilip Mistry ◽

Jill Hough

Keyword(s):

Machine Learning ◽

Service Life ◽

Predictive Model ◽

Learning Algorithm ◽

The State ◽

Decision Makers ◽

Machine Learning Algorithm ◽

Useful Life ◽

Transit Agencies ◽

The U.S

A predictive model is developed that uses a machine learning algorithm to predict the service life of transit vehicles and calculates backlog and yearly replacement costs to achieve and maintain transit vehicles in a state of good repair. The model is applied to data from the State of Oklahoma. The vehicle service lives predicted by the machine learning predictive model (MLPM) are compared with the default useful life benchmark (ULB) of the U.S. Federal Transit Administration (FTA). The model shows that the service life predicted by the MLPM provides relatively more realistic predictions of replacement costs of revenue vehicles than the predictions generated using FTA’s default ULB. The MLPM will help Oklahoma’s transit agencies facilitate the state of good repair analysis of their transit vehicles and guide decision makers when investing in rehabilitation and replacement needs. The paper demonstrates that it is advantageous to use a MLPM to predict the service life of revenue vehicles in place of the FTA’s default ULB.

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Liquidity Constraints in the U.S. Housing Market

10.3386/w23345 ◽

2017 ◽

Cited By ~ 6

Author(s):

Corina Boar ◽

Denis Gorea ◽

Virgiliu Midrigan

Keyword(s):

Housing Market ◽

Liquidity Constraints ◽

The U.S

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