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Data for today’s episode is provided by Mercator Advisory Group’s report – Tracking Mistakes in AI: Using Vigilance to Avoid Errors
Defining 5 Key Artificial Intelligence Terms:
- Artificial Intelligence (also called Machine Learning): A technique that ingests data and creates an algorithm that generates the desired output.
- Big Data: A collection of large data structured to support analysis that reveals patterns, trends, and associations.
- Metadata: Information about the collected data that may be descriptive, structural, or statistical information or support data administration.
- Training Data: Trains the AI algorithm thus must accurately reflect data seen in production and be tagged with the expected algorithmic output.
- Fair Use: FIs must adhere to a range of government and contractual data rights, which include consumer consent and GDPR limitations
About Report
AI models reflect existing biases if these biases are not explicitly eliminated by the data scientists developing the systems. Constant monitoring of the entire operation is required to detect these shifts. The remedy for such lack of focus is training.
Mercator Advisory Group’s latest research Report, Tracking Mistakes in AI: Use Vigilance to Avoid Errors, discusses modes in which data models can deliver biased results, and the ways and means by which financial institutions (FIs) can correct for these biases.
“AI solutions can unwittingly go astray,” comments Tim Sloane, the Report’s author and director of Mercator Advisory Group’s Emerging Technology Advisory Service and its VP Payments Innovation. “Applying AI to issues that can have large negative social consequences should be avoided. One example of this is using AI to implement the business plan of social networks Facebook, You Tube, and others, as presented in the documentary “The Social Dilemma.” The documentary contends that social networks have optimized AI to drive advertising revenue at the expense of the individual and society. To drive revenue, social networks build psychographic models for each user to predict exactly which content will best engage that user.”
