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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
Examples of AI Gone Astray:
- Apple Card’s credit acceptance algorithm failed to recognize the creditworthiness of many females.
- It’s possible that Apple Card’s training data used was weighted too heavily towards men as a representative sample.
- A court in Broward County, Florida used AI to predict parole violations, resulting in risk scores barely better than a coin flip.
- The AI powering Facebook & YouTube are optimized by psychographic models designed to trigger the end user, driving extremism.
- Mercator speculates that AI mistakes will increase for 3 reasons:
- 1) It’s harder to find AI talent, especially for regulated markets.
- 2) Data collected to train AI and the use case for its implementation do not necessarily correlate.
- 3) Automated platforms are taking the place of data scientists to collect training data, select appropriate features, and automate the training process.
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.”
