Meaningful standards for auditing high-stakes artificial intelligence Posted on : Mar 14 - 2022

When hiring, many organizations use artificial intelligence tools to scan resumes and predict job-relevant skills. Colleges and universities use AI to automatically score essays, process transcripts and review extracurricular activities to predetermine who is likely to be a "good student." With so many unique use-cases, it is important to ask: can AI tools ever be truly unbiased decision-makers? In response to claims of unfairness and bias in tools used in hiring, college admissions, predictive policing, health interventions, and more, the University of Minnesota recently developed a new set of auditing guidelines for AI tools.

The auditing guidelines, published in the American Psychologist, were developed by Richard Landers, associate professor of psychology at the University of Minnesota, and Tara Behrend from Purdue University. They apply a century's worth of research and professional standards for measuring personal characteristics by psychology and education researchers to ensure the fairness of AI.

The researchers developed guidelines for AI auditing by first considering the ideas of fairness and bias through three major lenses of focus:

How individuals decide if a decision was fair and unbiased

How societal, legal, ethical and moral standards present fairness and bias

How individual technical domains—like computer science, statistics and psychology—define fairness and bias internally

Using these lenses, the researchers presented psychological audits as a standardized approach for evaluating the fairness and bias of AI systems that make predictions about humans across high-stakes application areas, such as hiring and college admissions.

here are twelve components to the auditing framework across three categories that include:

Components related to the creation of, processing done by, and predictions created by the AI

Components related to how the AI is used, who its decisions affect and why

Components related to overarching challenges: the cultural context in which the AI is used, respect for the people affected by it, and the scientific integrity of the research used by AI purveyors to support their claims View more