Learning @ Scale 2021 & EMOOCs 2021

Authors:
Motahhare Eslami
Affiliation:
Carnegie Mellon University

Abstract:
The power, opacity, and bias of algorithmic systems have opened up new research areas for bringing transparency, fairness, and accountability into these systems. In this talk, I will revisit these lines of work, and argue that while they are critical to making algorithmic systems responsible, fresh perspectives are needed when these efforts fall short. I particularly discuss the necessity of algorithmic literacy and public education about the shortcomings of existing transparency and fairness efforts in algorithmic systems in order to enable everyday users to make more informed decisions in interactions with these systems.

First, I discuss how algorithmic transparency, when not designed carefully, can be more harmful than helpful, and that we need to inform users about the limitations of transparency mechanisms provided in algorithmic systems. Second, I will talk about the current approaches tackling algorithmic bias in algorithmic systems, including bias detection and bias mitigation, and their limitations. I particularly show that the current algorithm auditing techniques that mainly rely on experts, and are conducted outside of everyday use of algorithmic systems, fall short in detecting biases that emerge in real-world contexts of use, and in the presence of complex social dynamics over time. This leads to the idea of “everyday algorithm auditing” that involves educating and enabling everyday users to understand, detect and/or interrogate biased and harmful algorithmic behaviors via their day-to-day interactions with algorithmic systems. I then take a new perspective on the bias mitigation efforts that endeavor to bring fairness to algorithmic systems, and argue that there are many cases that mitigating algorithmic bias is quite challenging, if not impossible. I propose the concept of “bias transparency” that centers bias awareness in algorithmic systems, particularly in high-stakes decision-making systems, by educating the public about potential biases these systems can introduce to users’ decisions. I will end by discussing the importance of educating youth and fostering literacy around algorithmic systems from K-12 to prepare everyday users in their interactions with algorithmic systems.