(Machine) Learning to Be Like Thee? For Algorithm Education, Not Training

Algorithms reflect human biases rather than operating independently: The paper argues that ML algorithms are "an enactment of our most implicit prejudices and biases," meaning they don't introduce new problems but rather encode existing societal prejudices into automated decision-making systems that affect human lives at scale.

Current training approaches are insufficient for ethical AI: The paper distinguishes between "training" and "education" for algorithms, suggesting that simply training ML systems on data is inadequate. Instead, algorithms need to be "educated" to make moral decisions, similar to how we educate children for social responsibility rather than just training them for specific tasks.

Data quality directly impacts algorithmic ethics: The paper identifies that ML algorithms operate with "data incomplete, un-inclusive, or biased," highlighting that poor data foundations undermine ethical decision-making. This suggests that responsible AI requires careful attention to data collection and representation.

Responsibility assignment alone won't solve AI ethics problems: While some researchers focus on assigning responsibility when algorithms make harmful decisions, this paper argues that approach is insufficient—the real solution lies in fundamentally changing how we design and develop algorithms from the ground up.

Algorithm education offers a path toward sustainable, responsible AI: By drawing parallels to child education for social responsibility, the paper proposes that intentional, values-based algorithm design can create AI systems capable of ethical decision-making rather than simply reactive accountability measures.