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Machines, like people, can be biased. In AI systems, bias occurs when the systems produce results that are prejudiced due to unintended, erroneous assumptions in the machine learning (ML) process.
Fremont, CA: When bias is embedded in AI software, financial institutions can disproportionately reward certain groups over others, make bad decisions, issue false positives, and reduce their opportunities. This will eventually result in poor customer service, reduced sales and increased costs and risks. In order to identify, correct and avoid potential prejudice, banks must recruit the right talent and implement constructive product and platform innovation techniques.
Machines, like people, can be biased. In AI systems, bias occurs when the systems produce results that are prejudiced due to unintended, erroneous assumptions in the machine learning (ML) process. For financial institutions, prejudiced AI algorithms could reward certain groups over others, resulting in biased lending and credit decisioning processes that could limit the market over time and even change the landscape of the economy.
Bias has a particular problem with AI models in emerging markets where data is too frequently skewed to 'unbanked' households. This topic leads to wider concerns of social inclusion and equality.
The more AI is embedded in the bank's core processes and frameworks, the more risk it presents. In addition to alleviating potential harm, the removal of prejudice will enhance customer service, increase market opportunities and provide business insights. It also makes for a more inclusive and diverse outlook and strengthens banking decision-making processes.
Debiasing AI is good for business and good for the environment. As such, the topic is being considered by both government agencies and numerous industry organisations. Last year the House Financial Services Committee (FSC) created the Artificial Intelligence Task Force. In February, the FSC consulted with industry experts and regulators and agreed to endorse a market model governance system. The framework will audit AI systems to seek bias and set up protections and buffers to ensure fairness in algorithm models.
In order to keep up with changing regulations and best practices and to build their own 'anti-bias in AI' initiatives, banks must invest in people with the right skills and apply a multi-disciplinary approach to research, study, product design and creation of platforms through the use of AI/ML.They need teams and collaborators that are familiar with cognitive biases typically found in user experience, as well as factors leading to AI bias in the first place. These include processes that may produce missing data, pose a potential for unpredictable behaviour, or include criteria that are likely to have an effect.
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