The use of Artificial Intelligence (AI) is skyrocketing across all industries, and it’s here to stay. Love it or hate it, when used correctly, AI can bring significant benefits to businesses by improving efficiency, reducing costs, and enhancing the customer experience.
However, the financial services sector faces unique challenges when it comes to using AI. One of the major concerns is the potential bias in AI models, which can lead to discrimination against certain groups of people. This not only harms customers but also exposes businesses to legal risks.
Let’s dig deeper into how AI models can become biased. It all comes down to the algorithm that drives the AI model. This algorithm provides instructions to the computer, enabling it to generate output based on the available data. Bias can enter the equation in multiple ways. The algorithm itself may be flawed, giving more importance to certain data and skewing results. Humans can also introduce bias by selecting which datasets to include or exclude from the AI model. However, the most significant opportunity for bias to emerge lies in the content of the datasets used.
AI models are only as good as the data they are trained with. Businesses can obtain datasets in various ways. They may use their own historic data, such as insurance claims information, to improve risk assessments. Alternatively, they can purchase datasets from third parties who gather data from the internet or other sources. However, there is a risk of bias in these datasets, as they often reflect societal inequalities.
For example, if a dataset is trained on biased historical data that favors certain groups, it could lead to discriminatory pricing. A well-known example of this bias was seen in Amazon’s recruitment AI model, which discriminated against women due to being trained on biased employment information.
To combat bias in AI models, businesses can take several precautions. While no method is foolproof, implementing multiple safeguards can mitigate risks. Transparency is key when it comes to datasets. It is important to understand how and when the data was sourced and labeled. Additionally, considering the composition of the individuals in the dataset helps determine if any discriminatory skew exists.
Monitoring and assessing AI models continuously is crucial. Humans play an essential role in stress-testing AI models to ensure they achieve their goals without bias. This involves having an internal audit team that understands the AI model, the datasets used, and the real-time data being added. It is necessary to have policies in place for making effective and timely changes to AI models, as regulators are increasingly focused on transparency and fair outcomes for customers.
In the insurance sector, biased AI models pose risks to individuals and businesses that have insurance contracts. In the UK and the US, we have already witnessed copyright cases against AI giants due to discriminatory practices. Moreover, there is a regulatory aspect to consider. As AI evolves, so does the regulatory landscape. New regulations, like the EU’s AI Act and the UK’s proposed framework, emphasize non-discrimination. Non-compliance with these regulations can be extremely costly, not to mention the potential impact on a company’s reputation.
The rise of AI models in insurance is undeniable, but biased AI models come with genuine legal and reputational risks. To mitigate these risks, businesses must implement policies and procedures to ensure their AI models are fit for purpose.
Remember, I’m just a writer summarizing an article by Jamie Rowlands, a partner at Haseltine Lake Kempner. Keep exploring and stay curious!