Near-Term Artificial Intelligence and the Ethical Matrix

By Cathy O'Neil, H. Gunn. 2020

26 citations recorded by the source corpus.

This chapter takes up the issue of near-term artificial intelligence, or the algorithms that are already in place in a variety of public and private sectors, guiding decisions from advertising and to credit ratings to sentencing in the justice system. There is a pressing need to recognize and evaluate the ways that structural racism, sexism, classism, and ableism may be embedded in and amplified by these systems. The chapter proposes a framework for ethical analysis that can be used to facilitate more robust ethical reflection in AI development and implementation. It presents an ethical matrix that incorporates the language of data science as a tool that data scientists can build themselves in order to integrate ethical analysis into the design process, addressing the need for immediate analysis and accountability over the design and deployment of near-term AI.

Subfields: Cognitive Neuroscience, Safety Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety Research

Citations in the corpus

Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “Often there are genuinely different and conflicting goals, which are not resolved by more data [34, 99].”
Algorithmic Fairness: Choices, Assumptions, and Definitions (2021, background): “Often, different stakeholders have genuinely different and conflicting goals, which cannot be resolved by more data or different modeling choices (Eubanks 2018, O’Neil & Gunn 2020).”
Addressing Bias in Artificial Intelligence in Health Care. (2019, background): “48 Microsoft has also published its own matrix approach, Harms Modeling, which attempts to address harms that could arise from a given application across stakeholder groups.”