Trust in Numbers: An Ethical (and Practical) Standard for Identity-Driven Algorithms

Who was the real Tara Simmons? On November 16, 2017, she sat before the Washington State Supreme Court. The child of addicts and an ex-addict and ex-felon herself, she had subsequently graduated near the top of her law school class. She was asking the court to trust her to become an attorney, and the outcome of her case rested whether or not her past could be used to predict her future.

Algorithms that use the past to predict the future are commonplace: they predict what we’ll watch next, or how financially stable we will be, or, as in Tara’s case, how likely we are to commit a crime. Over the last several years, the identity industry noted the influence of algorithms on human well-being and the inherent biases in many of them. How can we as identity practitioners employ algorithms while at the same time ensure that they promote justice and fairness?

As we follow the case of Tara Simmons and others like her, we’ll develop a practical ethical standard for evaluating algorithms from a uniquely identity-centric standpoint. Learn how to ask the right questions, use open-source tools, and develop an assessment model to ensure that your systems prioritize well-being, demonstrate accountability, provide transparency in decision-making, promote fairness, and provide for user data rights.

Key takeaways:

(1) Understand the social and ethical implications of machine learning and algorithms within identity — and the potential false assumptions and biases that they perpetuate.
(2) Develop an ethical framework for the use of machine learning and algorithms that reflects values aligned with international human rights and an understanding of the concept of "well-being."
(3) Learn how to implement this framework in a coherently staged manner, complete with open-source tools, and assessment model, and consistent feedback to evaluate progress.