Joanna Bryson will be talking about ethical issues in AI.
Abstract (Part 1)
We didn’t prove prejudice is true: Why and when machines have human bias
Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. In a 2017 article with colleagues Aylin Caliskan and Arvind Narayanan, I showed that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names.
In the abstract to our article, we assert that “Our methods hold promise for identifying and addressing sources of bias in culture, including technology.” In this talk I will first present our results, then discuss what our research on machine bias demonstrates concerning the origins of human biases, stereotypes, and prejudices. Then I will discuss the extent to which implicit and explicit human bias accounts for bias in AI, and how and whether we can address such bias, perhaps using AI.
Abstract (Part 2)
Embodiment versus memetics: Moral responsibility in an era of semantic machines
The last few years have seen a growing public awareness of the pervasiveness of artificial intelligence (AI). Yet somehow this awareness has translated into a fear projected into the (near) future rather than an understanding or concern about how the world is already being changed by our technology. This inaccurate translation is symptomatic of a confusion about the nature of natural and human intelligence, and its role in our species’ success and our societies’structures.
In this talk I will begin by asserting a few definitions that I will then leverage to communicate my group’s findings about semantics, language evolution, and human sociality. I will conclude by discussing the implications of our work on the likely current and future impact of AI.
The tutorials are normally structured in a way that allows non-experts to attend. They will start with a fairly basic introduction to the topic, but may accelerate quite quickly. Find out more details about this tutorial, and upcoming tutorials.
Most of the previous tutorials are available on YouTube.
The machine learning tutorials are sponsored by PROWLER.io, Amazon and Microsoft Research.