ABSTRACT
When determining the solution, AI solution owners and others can benefit from adopting transformative justice principles, including the victims and impacted communities. The implementation of automated algorithmic systems has resulted in numerous legal challenges. In education, courts have been ahead of other industries in setting precedents, where standards of explainability are considered to ensure that laypeople understand how the algorithm came to its conclusion. More importantly, some level of accountability for the harms propagated by AI algorithms is being considered. Algorithmic accountability is “the assignment of responsibility to AI service providers for the impact that biased applications have on society.” Transparency and accountability measures include algorithmic impact assessments, data audits to test for discrimination, and, critically, a set of laws that penalize algorithmic bias, particularly in essential areas like housing, employment, and credit.
