Businesses are fast adopting Artificial Intelligence (AI) to automate the decision-making processes of various functions – from recruitment to credit appraisal. However, biases can creep in AI algorithms, and can end up enabling discriminatory decisions – unknowingly. As a result companies can incur ‘silent failures’, a situation when AI produces unwanted outcomes.
In the case of banks and financial institutions, AI bias can result in unfair financial decisions that can adversely impact segments of people based on their race, caste, religion, language, gender, or region. The business cost of such AI biases can easily outweigh the benefits of increased productivity, and scale.
The primary cause of AI bias is people’s bias finding its way into AI algorithms. Another reason concerns the inadequate, improper or corrupted data and data sources. Hence, companies invest in training people on bias, and best practices in dataset curation.
However, it is inevitable for FIs to automate oversight around the algorithmic systems to minimise, remove, and instantly respond to AI biases. AI Observability and Data Hygiene can help FIs collect data responsibly, and evaluate the performance of algorithms, and create a feedback loop for companies.
Better observability, monitoring and dataset hygiene across AI frameworks can help FIs foster human oversight, accountability, and adaptability. This enables better predictability and can improve trust across processes.
The above article is authored by Shoaib Mohammad, Founder and CEO, LUMIQ
