Category: Data science

“Dispersion & Disparity” Research Project Results

Mary Pat Campbell

Link: https://3iap.com/dispersion-disparity-equity-centered-data-visualization-research-project-Wi-58RCVQNSz6ypjoIoqOQ/

Graphic:

The same dataset, visualized two different ways. The left fixates on between-group differences, which can encourage stereotyping. The right shows both between and within group differences, which may discourage viewers’ tendencies to stereotype the groups being visualized.

Excerpt:

Ignoring or deemphasizing uncertainty in dataviz can create false impressions of group homogeneity (low outcome variance). If stereotypes stem from false impressions of group homogeneity, then the way visualizations represent uncertainty (or choose to ignore it) could exacerbate these false impressions of homogeneity and mislead viewers toward stereotyping.

If this is the case, then social-outcome-disparity visualizations that hide within-group variability (e.g. a bar chart without error bars) would elicit more harmful stereotyping than visualizations that emphasize within-group variance (e.g. a jitter plot).

Author(s): Stephanie Evergreen

Publication Date: 2 Aug 2022

Publication Site: 3iap

Tiny Python Projects

Mary Pat Campbell

Link: http://tinypythonprojects.com/Tiny_Python_Projects.pdf

Graphic:

Excerpt:


The biggest barrier to entry I’ve found when I’m learning a new language is that small concepts of the language are usually presented outside of any useful context. Most programming language tutorials will start with printing “HELLO, WORLD!” (and this is book is no exception). Usually that’s pretty simple. After that, I usually struggle to write a complete program that will accept some arguments and do something useful.

In this book, I’ll show you many, many examples of programs that do useful things, in the hopes that you can modify these programs to make more programs for your own use.

More than anything, I think you need to practice. It’s like the old joke: “What’s the way to Carnegie Hall? Practice, practice, practice.” These coding challenges are short enough that you could probably finish each in a few hours or days. This is more material than I could work through in a semester-long university-level class, so I imagine the whole book will take you several months. I hope you will solve the problems, then think about them, and then return later to see if you can solve them differently, maybe using a more advanced technique or making them run faster.

Author(s): Ken Youens-Clark

Publication Date: 2020

Publication Site: Tiny Python Projects

Fitting Yield Curves to rates

Mary Pat Campbell

Link: https://juliaactuary.org/tutorials/yield-curve-fitting/

Graphic:

Excerpt:

Given rates and maturities, we can fit the yield curves with different techniques in Yields.jl.

Below, we specify that the rates should be interpreted as Continuously compounded zero rates:

using Yields

rates = Continuous.([0.01, 0.01, 0.03, 0.05, 0.07, 0.16, 0.35, 0.92, 1.40, 1.74, 2.31, 2.41] ./ 100)
mats = [1/12, 2/12, 3/12, 6/12, 1, 2, 3, 5, 7, 10, 20, 30]

Then fit the rates under four methods:

  • Nelson-Siegel
  • Nelson-Siegel-Svennson
  • Boostrapping with splines (the default Bootstrap option)
  • Bootstrapping with linear splines
ns =  Yields.Zero(NelsonSiegel(),                   rates,mats)
nss = Yields.Zero(NelsonSiegelSvensson(),           rates,mats)
b =   Yields.Zero(Bootstrap(),                      rates,mats)
bl =  Yields.Zero(Bootstrap(Yields.LinearSpline()), rates,mats)

That’s it! We’ve fit the rates using four different techniques. These can now be used in a variety of ways, such as calculating the present_value, duration, or convexity of different cashflows if you imported ActuaryUtilities.jl

Publication Date: 19 Jun 2022, accessed 22 Jun 2022

Publication Site: JuliaActuary

Evaluating Unintentional Bias in Private Passenger Automobile Insurance

Mary Pat Campbell

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

A New Estimate of the Average Earth Surface Land Temperature Spanning 1753 to 2011

Mary Pat Campbell

Link: https://static.berkeleyearth.org/papers/Results-Paper-Berkeley-Earth.pdf

Graphic:

Abstract:

We report an estimate of the Earth’s average land surface temperature for the period 1753 to 2011. To address issues of potential station selection bias, we used a larger sampling of stations than had prior studies. For the period post 1880, our estimate is similar to those previously reported by other groups,
although we report smaller uncertainties. The land temperature rise from the 1950s decade to the 2000s decade is 0.90 ± 0.05°C (95% confidence). Both maximum and minimum temperatures have increased during the last century. Diurnal variations decreased from 1900 to 1987 and then increased; this increase is significant but not understood. The period of 1753 to 1850 is marked by sudden drops in land surface temperature that are coincident with known volcanism; the response function is approximately
1.5 ± 0.5°C per 100 Tg of atmospheric sulfate. This volcanism, combined with a simple proxy for anthropogenic effects (logarithm of the CO2 addition of a solar forcing term. Thus, for this very simple model, solar forcing does not appear to contribute to the observed global warming of the past 250 years; the entire change can be modeled by a sum of volcanism and a single anthropogenic proxy. The residual variations include interannual and multi-decadal variability very similar to that of the Atlantic Multidecadal Oscillation (AMO).


Keywords: Global warming; Kriging; Atlantic multidecadal oscillation;
Amo; Volcanism; Climate change; Earth surface temperature; Diurnal
variability

Author(s):

Robert Rohde1
, Richard A. Muller1,2,3
*, Robert Jacobsen2,3
,
Elizabeth Muller1
, Saul Perlmutter2,3
, Arthur Rosenfeld2,3
,
Jonathan Wurtele2,3
, Donald Groom3
and Charlotte Wickham4

Citation:

Rohde et al., Geoinfor Geostat: An Overview 2013, 1:1
http://dx.doi.org/10.4172/2327-4581.1000101

Publication Date: 2013

Publication Site: Geoinformatics & Geostatistics: An Overview

The Berkeley Earth Land/Ocean Temperature Record

Mary Pat Campbell

Link: https://essd.copernicus.org/articles/12/3469/2020/essd-12-3469-2020.html

Graphic:

Abstract:

A global land–ocean temperature record has been created by combining the Berkeley Earth monthly land temperature field with spatially kriged version of the HadSST3 dataset. This combined product spans the period from 1850 to present and covers the majority of the Earth’s surface: approximately 57 % in 1850, 75 % in 1880, 95 % in 1960, and 99.9 % by 2015. It includes average temperatures in 1∘×1∘ lat–long grid cells for each month when available. It provides a global mean temperature record quite similar to records from Hadley’s HadCRUT4, NASA’s GISTEMP, NOAA’s GlobalTemp, and Cowtan and Way and provides a spatially complete and homogeneous temperature field. Two versions of the record are provided, treating areas with sea ice cover as either air temperature over sea ice or sea surface temperature under sea ice, the former being preferred for most applications. The choice of how to assess the temperature of areas with sea ice coverage has a notable impact on global anomalies over past decades due to rapid warming of air temperatures in the Arctic. Accounting for rapid warming of Arctic air suggests ∼ 0.1 ∘C additional global-average temperature rise since the 19th century than temperature series that do not capture the changes in the Arctic. Updated versions of this dataset will be presented each month at the Berkeley Earth website (http://berkeleyearth.org/data/, last access: November 2020), and a convenience copy of the version discussed in this paper has been archived and is freely available at https://doi.org/10.5281/zenodo.3634713 (Rohde and Hausfather, 2020).

Author(s): Robert A. Rohde1 and Zeke Hausfather1,2

Citation:
Rohde, R. A. and Hausfather, Z.: The Berkeley Earth Land/Ocean Temperature Record, Earth Syst. Sci. Data, 12, 3469–3479, https://doi.org/10.5194/essd-12-3469-2020, 2020.

Publication Date: 17 Dec 2020

Publication Site: Earth System Science Data

Big Data, Big Discussions

Mary Pat Campbell

Link: https://theactuarymagazine.org/big-data-big-discussions/

Excerpt:

Why is the insurance industry now facing increased scrutiny on certain underwriting methods?

Insurers increasingly are turning to nontraditional data sets, sources and scores. The methods used to obtain traditional data—that were at one time costly and time-consuming—can now be done quickly and cheaply.

As insurers continue to innovate their underwriting techniques, increased scrutiny should be expected. It is not unreasonable for consumer advocates to push for increased transparency and explainability when insurers employ these advanced methods.

What is the latest regulatory activity on this topic in the various states and at the NAIC?

Activity in the states has been minimal. In 2021, Colorado became the first (and so far, only) state to enact legislation requiring insurers to test their algorithms for bias. Legislation nearly identical to the Colorado law was introduced in Oklahoma and Rhode Island in 2022, and it is likely other states will consider similar legislation. Connecticut is finalizing guidance that would require insurers to attest that their use of data is nondiscriminatory. Other states have targeted specific factors, but most have adopted a wait-and-see approach.

The NAIC created a new high-level committee to focus on innovation and AI, but it has become clear that a national standard is not likely at this time.

Author(s): INTERVIEW BY STEPHEN ABROKWAH, Interview with Neil Sprackling, president of Swiss Re Life & Health America Inc.

Publication Date: March 2022

Publication Site: The Actuary

CAS Releases Two Additional Papers in Race and Insurance Pricing Series

Mary Pat Campbell

Link: https://www.casact.org/article/cas-releases-two-additional-papers-race-and-insurance-pricing-series

Excerpt:

Arlington, VA – Two new research reports designed to guide the insurance industry toward proactive, quantitative solutions to identify, measure and address potential racial bias in insurance pricing were published by the Casualty Actuarial Society (CAS) today.

“These two new reports in our CAS Research Series on Race and Insurance Pricing continue to provide additional insight into industry discussions on this topic,” said Victor Carter-Bey, DM, CAS chief executive officer. “We hope with this series to serve as a thought leader and role model for other insurance organizations and corporations in promoting fairness and progress.”

As the professional society of actuaries specializing in property and casualty insurance, the CAS is committed to diversity, equity and inclusion in actuarial work. To this end, the Society is releasing a series of four CAS Research Papers, which support the CAS’s Approach to Race and Insurance Pricing. This approach was adopted by the CAS Board of Directors in December 2020 and includes four key areas of focus and goals: basic and continuing education, research, leadership and influence, and collaboration. Each paper in the series addresses a different aspect of race and insurance pricing as viewed through the lens of property and casualty insurance.

Two of the four reports in the CAS Research Paper Series on Race and Insurance Pricing, Understanding Potential Influences of Racial Bias on P&C Insurance: Four Rating Factors Explored and Defining Discrimination in Insurance, are being released today. Here is a more detailed description of the two reports published today:

Defining Discrimination in Insurance. This report examines terms that are being used in discussions around potential discrimination in insurance, including protected class, unfair discrimination, proxy discrimination, disparate impact, disparate treatment, and disproportionate impact. The paper provides historical and practical context for these terms and illustrates the inconsistencies in how different stakeholders define them. It also describes the potential impacts of these definitions on actuarial work.

Understanding Potential Influences of Racial Bias on P&C Insurance: Four Rating Factors Explored. The paper examines four commonly used rating factors to understand how the data underlying insurance pricing models may be impacted by racially biased policies and practices outside of insurance. The goal is to highlight the multi-dimensional impacts of systemic racial bias, as it may relate to insurance pricing. The four factors included in the report are: Credit-Based Insurance Score (CBIS), geographic location, homeownership and Motor Vehicle Records.

The other two reports, Methods for Quantifying Discriminatory Effects on Protected Classes in Insurance, and Approaches to Address Racial Bias in Financial Services: Lessons for the Insurance Industry, were released March 10, 2022 during a virtual briefing.  

These four research reports are just one way the CAS supports evolving actuarial practices and strengthens the knowledge of its members. The papers demonstrate the Society’s recognition that actuaries—who are responsible for setting insurance rates—must be a voice in an ever-evolving dialogue. The CAS understands that this work is critical to maintaining the Society and its members’ public trust.

Publication Date: 31 Mar 2022

Publication Site: CAS

The EEOC wants to make AI hiring fairer for people with disabilities

Mary Pat Campbell

Link: https://www.brookings.edu/blog/techtank/2022/05/26/the-eeoc-wants-to-make-ai-hiring-fairer-for-people-with-disabilities/

Excerpt:

That hiring algorithms can disadvantage people with disabilities is not exactly new information. In 2019, for my first piece at the Brookings Institution, I wrote about how automated interview software is definitionally discriminatory against people with disabilities. In a broader 2018 review of hiring algorithms, the technology advocacy nonprofit Upturn concluded that “without active measures to mitigate them, bias will arise in predictive hiring tools by default” and later notes this is especially true for those with disabilities. In their own report on this topic, the Center for Democracy and Technology found that these algorithms have “risk of discrimination written invisibly into their codes” and for “people with disabilities, those risks can be profound.” This is to say that there has long been broad consensus among experts that algorithmic hiring technologies are often harmful to people with disabilities, and that given that as many as 80% of businesses now use these tools, this problem warrants government intervention.

….

The EEOC’s concerns are largely focused on two problematic outcomes: (1) algorithmic hiring tools inappropriately punish people with disabilities; and (2) people with disabilities are dissuaded from an application process due to inaccessible digital assessments.

Illegally “screening out” people with disabilities

First, the guidance clarifies what constitutes illegally “screening out” a person with a disability from the hiring process. The new EEOC guidance presents any disadvantaging effect of an algorithmic decision against a person with a disability as a violation of the ADA, assuming the person can perform the job with legally required reasonable accommodations. In this interpretation, the EEOC is saying it is not enough to hire candidates with disabilities in the same proportion as people without disabilities. This differs from EEOC criteria for race, religion, sex, and national origin, which says that selecting candidates at a significantly lower rate from a selected group (say, less than 80% as many women as men) constitutes illegal discrimination.

Author(s): Alex Engler

Publication Date: 26 May 2022

Publication Site: Brookings

DEFINING DISCRIMINATION IN INSURANCE

Mary Pat Campbell

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

Graphic:

Excerpt:

This research paper is designed to introduce various terms used in defining
discrimination by stakeholders in the insurance industry (regulators, consumer
advocacy groups, actuaries and insurers, etc.). The paper defines protected class,
unfair discrimination, proxy discrimination, disproportionate impact, disparate
treatment and disparate impact.
Stakeholders are not always consistent in their definitions of these terms, and
these inconsistencies are highlighted and illustrated in this paper. It is essential to
elucidate key elements and attributes of certain terms as well as conflicting
approaches to defining discrimination in insurance in order to move the industry
discussion forward.
While this paper does not make a judgment on the appropriateness of the
definitions put forth, nor does it promulgate what the definitions should be,
readers will be empowered to understand the components of discrimination terms
used in insurance, as well as be introduced to the potential implications for
insurers.
Actuaries who have a strong foundational knowledge of these terms are likely to
play a key role in informing those who define and refine these terms for insurance
purposes in the future. This paper is not a legal review, and thus discusses terms
and concepts as they are used by insurance stakeholders, rather than what their
ultimate legal definition will be. However, it is important for actuaries to
understand the point of view of various stakeholders, and the potential impact it
could have on actuarial work. As the regulatory and legislative landscape
continues to shift, this brief should be considered a living document, that will
periodically require update.

Author(s): Kudakwashe F. Chibanda, FCAS

Publication Date: March 2022

Publication Site: CAS

Actuarial Group Takes Steps to Identify Racial Bias in Insurance Rates

Mary Pat Campbell

Link: https://www.investopedia.com/race-and-insurance-5224764

Excerpt:

Two new papers from property casualty actuaries delve into issues of historical and ongoing bias in insurance pricing.

These papers are on potential Influences among four rating factors and attempts to actually define discrimination in insurance.

Factors such as geography, credit scoring, home ownership, and motor vehicle records affect homeowners and auto insurance rates and can cause Black consumers to pay higher premiums.

Actuaries and regulators are trying to untangle factors from societal prejudice for fairer pricing

AI or machine learning can augment or amplify these biases with their vast inputs, and data scientists will be analyzing outcomes for discriminatory pricing effects.

States have been taking action through regulation or pending legislation to extinguish some factors that can lead to racial bias or to examine data modeling to check for discriminatory effects.

Author(s): ELIZABETH FESTA

Publication Date: 5 April 2022

Publication Site: Investopedia

METHODS FOR QUANTIFYING DISCRIMINATORY EFFECTS ON PROTECTED CLASSES IN INSURANCE

Mary Pat Campbell

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