Making AI-based Finance Work for Women

In this session, you will hear from executives at Women's World Banking, Tyme, Kaleidofin and Africa Fintech Foundry, about the risks of algorithm-based underwriting to women customers – fueled by conscious and unconscious bias. Finding bias is not as simple as finding a decision to be “unfair.” There are dozens of definitions of gender fairness, from keeping gendered data out of credit decisions to ensuring equal likelihood of granting credit to men and women. It is important to start with defining fairness because financial services providers need to start with an articulation of what they mean when they say they pursue gender fairness. Defining and pursuing fairness is then followed by a recognition of where biases emerge. Finally, there are many implementable bias mitigation strategies relevant to financial institutions. Mitigating bias requires intentionality at all levels. These strategies are equally relevant for algorithm developers and decision makers alike.

What you will learn:

• What is algorithmic bias?
• Why does it matter especially now?
• Where does it emerge?
• How might it be mitigated?
• How do we make AI-based finance work for women?