Teslas running Autopilot involved in 273 crashes reported since last year

Link: https://www.washingtonpost.com/technology/2022/06/15/tesla-autopilot-crashes/

Excerpt:

Tesla vehicles running its Autopilot software have been involved in 273 reported crashes over roughly the past year, according to regulators, far more than previously known and providing concrete evidence regarding the real-world performance of its futuristic features.

The numbers, which were published by the National Highway Traffic Safety Administration for the first time Wednesday, show that Tesla vehicles made up nearly 70 percent of the 392 crashes involving advanced driver-assistance systems reported since last July, and a majority of the fatalities and serious injuries — some of which date back further than a year. Eight of the Tesla crashes took place before June 2021, according to data released by NHTSA on Wednesday morning.

Previously, NHTSA said it had probed 42 crashes potentially involving driver assistance, 35 of which included Tesla vehicles, in a more limited data set that stretched back to 2016.

Of the six fatalities listed in the data set published Wednesday, five were tied to Tesla vehicles — including a July 2021 crash involving a pedestrian in Flushing, Queens, and a fatal crash in March in Castro Valley, Calif. Some dated as far back as 2019.

Author(s): Faiz Siddiqui, Rachel Lerman and Jeremy B. Merrill

Publication Date: 15 Jun 2022

Publication Site: Washington Post

Evaluating Unintentional Bias in Private Passenger Automobile Insurance

Link: https://disb.dc.gov/page/evaluating-unintentional-bias-private-passenger-automobile-insurance

Public Hearing Notice: Evaluating Unintentional Bias in Private Passenger Automobile Insurance, June 29, 2022, 3 pm

Excerpt:

In 2020, Commissioner Karima Woods, Commissioner for the District of Columbia Department of Insurance, Securities and Banking (DISB) directed the creation of the Department’s first Diversity Equity and Inclusion Committee to engage in a wide-ranging review of financial equity and inclusion and to make recommendations to remove barriers to accessing financial services. Department staff developed draft initiatives, including an initiative related to insurers’ use of factors such as credit scores, education, occupation, home ownership and marital status in underwriting and ratemaking. Stakeholder feedback on this draft initiative resulted in the Department concluding that data was necessary to properly address this initiative. Department staff conducted research and contacted subject matter experts before determining that relevant data was not generally available.

The Department is undertaking this project to collect the relevant data. We determined this initiative will be deliberative and transparent to ensure the resultant data would address the issue of unintentional bias. We also decided to initially focus on private passenger automobile insurance as that is a line of insurance that affects many District consumers and has previously had questions raised about the use of non-driving factors. The collected data will build on previous work done by the Department through the 2018 and 2019 public hearings and examinations that looked at private passenger automobile insurance ratemaking methodologies.

For this project to look at the potential for unintentional bias in auto insurance, DISB will conduct a review of auto insurers’ rating and underwriting methodologies. As a first step, DISB will hold a public hearing on Wednesday, June 29, 2022 at 3 pm to gather stakeholder input on the review plan, which is outlined below. The Department has engaged the services of O’Neil Risk Consulting and Algorithmic Auditing (ORCAA) to assist the Department and provide subject matter expertise. Additionally, the Department will hold one or more meetings to follow up on any items raised during the public hearing.

Publication Date: accessed 18 Jun 2022

Publication Site: District of Columbia Department of Insurance, Securities & Banking

NHTSA Releases Initial Data on Safety Performance of Advanced Vehicle Technologies

Link: https://www.nhtsa.gov/press-releases/initial-data-release-advanced-vehicle-technologies

Report for Level 2 ADAS: https://www.nhtsa.gov/sites/nhtsa.gov/files/2022-06/ADAS-L2-SGO-Report-June-2022.pdf

Report for Levels 3-5: https://www.nhtsa.gov/sites/nhtsa.gov/files/2022-06/ADS-SGO-Report-June-2022.pdf

Graphic:

Excerpt:

Today, as part of the U.S. Department of Transportation’s efforts to increase roadway safety and encourage innovation, the National Highway Traffic Safety Administration published the initial round of data it has collected through its Standing General Order issued last year and initial accompanying reports summarizing this data.

The SAE Level 2 advanced driver assistance systems summary report is available here, while the SAE Levels 3-5 automated driving systems summary report is available here. Going forward, NHTSA will release data updates monthly.

These data reflect a set of crashes that automakers and operators reported to NHTSA from the time the Standing General Order was issued last June. While not comprehensive, the data are important and provide NHTSA with immediate information about crashes that occur with vehicles that have various levels of automated systems deployed at least 30 seconds before the crash occurred.

“The data released today are part of our commitment to transparency, accountability and public safety,” said Dr. Steven Cliff, NHTSA’s Administrator. “New vehicle technologies have the potential to help prevent crashes, reduce crash severity and save lives, and the Department is interested in fostering technologies that are proven to do so; collecting this data is an important step in that effort. As we gather more data, NHTSA will be able to better identify any emerging risks or trends and learn more about how these technologies are performing in the real world.”

Publication Date: 15 June 2022

Publication Site: NHTSA

Newly Released Estimates Show Traffic Fatalities Reached a 16-Year High in 2021

Link: https://www.nhtsa.gov/press-releases/early-estimate-2021-traffic-fatalities

Report link: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813298

Graphic:

Excerpt:

The National Highway Traffic Safety Administration has released its early estimate of traffic fatalities for 2021. NHTSA projects that an estimated 42,915 people died in motor vehicle traffic crashes last year, a 10.5% increase from the 38,824 fatalities in 2020. The projection is the highest number of fatalities since 2005 and the largest annual percentage increase in the Fatality Analysis Reporting System’s history. Behind each of these numbers is a life tragically lost, and a family left behind. 

“We face a crisis on America’s roadways that we must address together,” said U.S. Transportation Secretary Pete Buttigieg. “With our National Roadway Safety Strategy and the President’s Bipartisan Infrastructure Law, we are taking critical steps to help reverse this devastating trend and save lives on our roadways.” 

The Bipartisan Infrastructure Law places a strong emphasis on improving safety and includes the new Safe Streets and Roads for All program, which opened its first round of applications just this week. The program, the first of its kind, invests up to $6 billion over five years to fund local efforts to reduce roadway crashes and fatalities. The Bipartisan Infrastructure Law now being implemented also advances Complete Streets policies and standards; requires updates to the Manual on Uniform Traffic Control Devices, which defines speeds, lane markings, traffic lights and more on most roads in the country; and sharply increases funding for the Highway Safety Improvement Program, which helps states adopt data-driven approaches to making roads safer. 

Publication Date: 17 May 2022

Publication Site: NHTSA

Geico ordered to pay $5.2M to woman who got HPV in a car

Link: https://www.autoblog.com/2022/06/08/insurance-company-payout-hpv-car-sex/

Excerpt:

Per The Kansas City Star, the woman initiated a claim with Geico in February 2021 after learning that she’d contracted the sexually transmitted infection from a partner who knew but did not disclose his status. Since the incident in question happened in her partner’s car, she argued that his liability insurance was responsible for damages. A settlement was reportedly offered to Geico, whose lawyers declined. As anybody who’s had legal entanglements with an insurance company can probably guess, the case went to arbitration.

In what we’re certain was a surprise to Geico’s legal team, arbitration did not go their way. The woman’s partner was found liable and the arbitrator approved an award of $5.2 million in damages to be paid out by the insurer despite requests by Geico for a new hearing. The insurance company appealed to the courts on several grounds, claiming that the process denied it the ability to have its day in court. The company’s appeal was denied on all points.

Author(s): Byron Hurd

Publication Date: 8 June 2022

Publication Site: Autoblog

A NHTSA official spent years trying to cut road deaths. They jumped last year.

Link: https://www.washingtonpost.com/transportation/2022/05/21/road-deaths-fatalities-safety/

Graphic:

Excerpt:

Before Jeffrey Michael spent three decades in the federal government trying toreduce the nation’s road fatalities, he worked in college as a car mechanic.

He took that love of cars to the National Highway Traffic Safety Administration, where he worked on seat belts, child restraints, drunken driving and emergency medical services, eventually overseeing behavioral research at the agency. At home in the Washington suburbs, he would tinker with the 1987 Porsche 911 he bought as a fixer-upper. After retiring in 2018, he joined the Johns Hopkins Center for Injury Research and Policy.

Michael saw the abilityof federal programs to influence safety and cites a gradual reduction in road deaths over 50 years. But in an interview with The Washington Post — daysafter new NHTSA figures showed fatalities hitting a 16-year high — Michael pointed to the nation’s failure and potential fixes.

Author(s): Michael Laris

Publication Date: 21 May 2022

Publication Site: Washington Post

UNDERSTANDING POTENTIAL INFLUENCES OF RACIAL BIAS ON P&C INSURANCE: FOUR RATING FACTORS EXPLORED

Link: https://www.casact.org/sites/default/files/2022-03/Research-Paper_Understanding_Potential_Influences.pdf?utm_source=III&utm_medium=Issue+Brief&utm_campaign=RIP

Graphic:

Excerpt:

Insurance rating characteristics have come under scrutiny by legislators and
regulators in their efforts to identify and address racial bias in insurance
practices. The goal of this paper is to equip actuaries with the information
needed to proactively participate in industry discussions and actions related
to racial bias and insurance rating factors. This paper uses the following
definition of racial bias:
Racial bias refers to a system that is inherently skewed along racial lines.
Racial bias can be intentional or unintentional and can be present in the
inputs, design, implementation, interpretation, or outcomes of any system.
This paper will examine four commonly used rating factors in personal
lines insurance — credit-based insurance score, geographic location, home
ownership, and motor vehicle records — to understand how the data
underlying insurance pricing models may be impacted by racially biased
policies and practices outside of the system of insurance. Historical issues
like redlining and racial segregation, as well as inconsistent enforcement of
policies and practices contribute to this potential bias. These historical
issues do not necessarily change the validity of the actuarial approach of
evaluating statistical correlation of rating factors to insurance loss overall.
Differences in the way individual insurers build rating models may produce
very different end results for customers. More data and analyses are
needed to understand if and to what extent these specific issues of racial
bias impact insurance outcomes. Actuaries and other readers can combine
this information with their own subject matter expertise to determine if and
how this could impact the systems for which they are responsible, and what
actions, if any, could be taken as a result.

Author(s): Members of the 2021 CAS Race and Insurance Research Task Force

Publication Date: March 2022

Publication Site: CAS

Biological and Psychobehavioral Correlates of Credit Scores and Automobile Insurance Losses: Toward an Explication of Why Credit Scoring Works

Link: https://www.jstor.org/stable/4138424

Graphic:

Abstract:

The most important new development in the past two decades in the personal lines of insurance may well be the use of an individual’s credit history as a classification and rating variable to predict losses. However, in spite of its obvious success as an underwriting tool, and the clear actuarial substantiation of a strong association between credit score and insured losses over multiple methods and multiple studies, the use of credit scoring is under attack because there is not an understanding of why there is an association. Through a detailed literature review concerning the biological, psychological, and behavioral attributes of risky automobile drivers and insured losses, and a similar review of the biological, psychological, and behavioral attributes of financial risk takers, we delineate that basic chemical and psychobehavioral characteristics (e.g., a sensation-seeking personality type) are common to individuals exhibiting both higher insured automobile loss costs and poorer credit scores, and thus provide a connection which can be used to understand why credit scoring works. Credit scoring can give information distinct from standard actuarial variables concerning an individual’s biopsychological makeup, which then yields useful underwriting information about how they will react in creating risk of insured automobile losses.

Author(s): Patrick L. Brockett and Linda L. Golden

Publication Date: originally 2007

Publication Site: jstor, The Journal of Risk and Insurance

Cite: Brockett, Patrick L., and Linda L. Golden. “Biological and Psychobehavioral Correlates of Credit Scores and Automobile Insurance Losses: Toward an Explication of Why Credit Scoring Works.” The Journal of Risk and Insurance, vol. 74, no. 1, 2007, pp. 23–63. JSTOR, http://www.jstor.org/stable/4138424. Accessed 22 May 2022.

METHODS FOR QUANTIFYING DISCRIMINATORY EFFECTS ON PROTECTED CLASSES IN INSURANCE

Link: https://www.casact.org/sites/default/files/2022-03/Research-Paper_Methods-for-Quantifying-Discriminatory-Effects.pdf

Graphic:

Excerpt:

This research paper’s main objective is to inspire and generate discussions
about algorithmic bias across all areas of insurance and to encourage
actuaries to be involved. Evaluating financial risk involves the creation of
functions that consider myriad characteristics of the insured. Companies utilize
diverse statistical methods and techniques, from relatively simple regression
to complex and opaque machine learning algorithms. It has been alleged that
the predictions produced by these mathematical algorithms have
discriminatory effects against certain groups of society, known as protected
classes.
The notion of discriminatory effects describes the disproportionately adverse
effect algorithms and models could have on protected groups in society. As a
result of the potential for discriminatory effects, the analytical processes
followed by financial institutions for decision making have come under greater
scrutiny by legislators, regulators, and consumer advocates. Interested parties
want to know how to quantify such effects and potentially how to repair such
systems if discriminatory effects have been detected.


This paper provides:


• A historical perspective of unfair discrimination in society and its impact
on property and casualty insurance.
• Specific examples of allegations of bias in insurance and how the various
stakeholders, including regulators, legislators, consumer groups and
insurance companies have reacted and responded to these allegations.
• Some specific definitions of unfair discrimination and that are interpreted
in the context of insurance predictive models.
• A high-level description of some of the more common statistical metrics
for bias detection that have been recently developed by the machine
learning community, as well as a brief account of some machine learning
algorithms that can help with mitigating bias in models.


This paper also presents a concrete example of an insurance pricing GLM
model developed on anonymized French private passenger automobile data,
which demonstrates how discriminatory effects can be measured and
mitigated.

Author(s): Roosevelt Mosley, FCAS, and Radost Wenman, FCAS

Publication Date: March 2022

Publication Site: CAS

Are Seat Belts Making You Less Safe?

Link: https://fee.org/articles/are-seat-belts-making-you-less-safe/

Excerpt:

In the 1960s, the federal government—in its infinite wisdom—thought that cars were too unsafe for the general public. In response, it passed automobile safety legislation, requiring that seat belts, padded dashboards, and other safety measures be put in every automobile.

Although well-intended, auto accidents actually increased after the legislation was passed and enforced. Why? As Lansburg explains, “the threat of being killed in an accident is a powerful incentive to drive carefully.”

In other words, the high price (certain death from an accident) of an activity (reckless driving) reduced the likelihood of that activity. The safety features reduced the price of reckless driving by making cars safer. For example, seatbelts reduced the likelihood of a driver being hurt if he drove recklessly and got into an accident. Because of this, drivers were more likely to drive recklessly.

The benefit of the policy was that it reduced the number of deaths per accident. The cost of the policy was that it increased the number of accidents, thus canceling the benefit. Or at least, that is the conclusion of University of Chicago’s Sam Peltzman, who found the two effects canceled each other.

His work has led to a theory called “The Peltzman Effect,” also known as risk compensation. Risk compensation says that safety requirements incentivize people to increase risky behavior in response to the lower price of that behavior.

Author(s): Joshua Anumolu

Publication Date: 13 July 2017

Publication Site: FEE

Seat Belt Usage and Risk Taking in Driving Behavior

Link: https://www.jstor.org/stable/44633774

Graphic:

Abstract:

This study tested the hypothesis that seat belt usage is related to driver risk taking in car-following behavior. Individual vehicles on a Detroit area freeway were monitored to identify seat belt users and nonusers. Headways between successive vehicles in the traffic stream were also measured to provide a behavioral indicator of driver risk taking. Results showed that nonusers of seat belts tended to follow other vehicles closer than did users. Users were also less likely than nonusers to follow other vehicles at very short headways (one second or less). The implications of these findings for occupant safety in rear end collisions are discussed.

Author(s): Buseck, Calvin R. von, Leonard Evans, Donald E. Schmidt, and Paul Wasielewski

Publication Date: 1980

Publication Site: jstor, originally published in SAE Transactions, vol 89

Cite:

von Buseck, Calvin R., et al. “Seat Belt Usage and Risk Taking in Driving Behavior.” SAE Transactions, vol. 89, 1980, pp. 1529–33. JSTOR, http://www.jstor.org/stable/44633774. Accessed 21 May 2022.