Discrimination in Ontario Automobile Insurance
Tags: Algorithmic Bias, Ontario, Insurance, Discrimination

Source: CityNews Toronto

Background

In 2018, two bills were set out to end territorial ratings — often referred to as “postal code discrimination” — in the automobile industry. Bill 44, titled Ending Automobile Insurance Discrimination in the Greater Toronto Area Act was struck down in the Ontario legislature on November 1st, 2018. Bill 42, Ending Discrimination in Automobile Insurance Act, remains alive. It passed second reading last March and is currently referred to the Standing Committee. Before bill 42 can become a law, postal code, which is highly correlated with income, race, unemployment rates and educational attainment, is legally used in risk classification algorithms and systems in Ontario.

Risk classification was developed to group people together according to the risks they represent, so people are protected from carrying more expense of someone who is more risk-prone. However, the classification relies on algorithmic systems that perpetuate discrimination by assigning levels of risk based on people’s area of residency, gender, marital status and more. Insurance companies justify such discrimination based on an individual’s area of residency, gender, marital status as “fair” because they are statistically significant indicators of risks. But what is “fair”? Actuarial fairness aims to function as a neutral and technical concept that serves the industry market. However, it can conflict with human rights principles when so-called “fair discrimination” perpetuate discrimination among society. Coming back to the automobile insurance example of using postal code as a risk factor in Ontario, premium costs are weighted more on drivers in higher-risk neighbourhoods. Most of these higher-risk neighbourhoods are also underprivileged neighbourhoods. Therefore, algorithms for calculating premiums based on postal codes are likely to result in “digital redlining” — practices that create and perpetuate inequities between marginalized groups through digital technologies. The seemingly “fair” discrimination of postal code failed to justify its validity in a social and civic context.

Postal code or area of residency is not the only problematic risk factor. According to the Financial Services Regulatory Authority of Ontario, presently, a personal automobile insurance profile is created based on the type of vehicle, driving record, annual mileage, area of residency, age, gender, and marital status. Only three out of the seven factors are directly related to “driving.” Age is a biological factor that people have no control with, and marital status largely depends on different views of family values. None of these factors should be burdened with discriminatory insurance pricing and should be removed from the automobile insurance algorithms.

Digital Redlining

In the 1930s, the US government sent appraisers to grade neighbourhoods in 239 cities, colour-coding them green for “best,” blue for “still desirable,” and yellow for “definitely declining.” The lowest grade was “hazardous,” which was marked with red lines on maps. The “redlined” areas were the ones financial service providers diminished as credit risks. Although the assessments were made by accounting for local amenities and home prices, the residents’ minority racial and ethnic demographics were the central part that contributes to a neighbourhood’s undesirability. Redlining today refers to the systematic denial of various services in selected areas by loan and insurance companies either directly or through the selective raising of prices. Because financial resources were harder to get in these areas, the redlines established a vicious circle for the neighbourhoods to stay underprivileged, even until today.

The effect of redlining in the automobile insurance industry in Ontario is perpetuated by modern automobile insurance algorithms. In the age of Big Data, insurance is evaluated by data analytic models and machine learning algorithms that are not as direct but may be even more damaging than colour-coded maps; this is because they are not limited by geographic boundaries and their inner works are often very complex and uninterpretable:

“Software that makes decisions based on data like a person’s ZIP code can reflect,

or even amplify, the results of historical or institutional discrimination.”

— ProPublica.org

We can see the effect in the Greater Toronto Area’s automobile insurance industry, which is the largest and most multicultural city in Canada. According to Kanetix, the average Toronto insurance rate is $1948. The highest estimated rate is $2590, and the lowest estimated rate is $1533, which results in a large gap of $1057.

Most expensive rates for car insurance in Toronto. ( By Kanetix 2019)

The most expensive area consists of four postal codes that cover eight neighbourhoods out of 140 in the City of Toronto. Based on the demographic information of the eight neighbourhoods retrieved from the City of Toronto’s Neighbourhood Profiles, six out of the eight neighbourhoods are designated as Neighbourhood Improvement Areas (NIAs) — vulnerable communities with socio-economic struggles recognized by the City of Toronto. 7 out of the 8 neighbourhoods have a higher than average percentage of residents identified as visible minorities. Half of the neighbourhoods have higher than average percentages of households in poverty. 7 out of the 8 neighbourhoods have higher than average unemployment rates. Finally, all neighbourhoods listed have low higher education attainment compared to the City of Toronto as a whole.