In this paper, we argue that AI ethics must move beyond the concepts of race-based representation and bias, and towards those that probe the deeper relations that impact how these systems are designed, developed, and deployed. Many recent discussions on ethical considerations of bias in AI systems have centered on racial bias. We contend that antiblackness in AI requires more of an examination of the ontological space that provides a foundation for the design, development, and deployment of AI systems. We examine what this contention means from the perspective of the sociocultural context in which AI systems are designed, developed, and deployed and focus on intersections with anti-Black racism (antiblackness). To bring these multiple perspectives together and show an example of antiblackness in the face of attempts at de-biasing, we discuss results from auditing an existing open-source semantic network (ConceptNet). We use this discussion to further contextualize antiblackness in design, development, and deployment of AI systems and suggest questions one may ask when attempting to combat antiblackness in AI systems.

Visual media has come a long way since the first proto-human cave dwellers used the flickering of torch light to bring the hand-drawn art on their walls to life. Today, the pixel -- despite its humble, low-resolution origins -- sits as the current pinnacle of digital display technology. In his new book, Biography of the Pixel, Pixar co-founder Alvy Ray Smith examines the fascinating history and development of picture elements (hence "pix"-"el") from their often-contested start in the labs of pioneering computer researchers like Alan Turing to their ubiquitous presence in modern life. In the excerpt below, Smith takes a look at the bad old days before digital displays to explain the science behind our brain's' ability to perceive motion through the rapid flashing of static images. What did the inventors of cinema do (or not) to make the system they gave us so non-ideal?

OpenAI's latest language generation model, GPT-3, has made quite the splash within AI circles, astounding reporters to the point where even Sam Altman, OpenAI's leader, mentioned on Twitter that it may be overhyped. Still, there is no doubt that GPT-3 is powerful. Those with early-stage access to OpenAI's GPT-3 API have shown how to translate natural language into code for websites, solve complex medical question-and-answer problems, create basic tabular financial reports, and even write code to train machine learning models -- all with just a few well-crafted examples as input (i.e., via "few-shot learning"). Soon, anyone will be able to purchase GPT-3's generative power to make use of the language model, opening doors to build tools that will quietly (but significantly) shape our world. Enterprises aiming to take advantage of GPT-3, and the increasingly powerful iterations that will surely follow, must take great care to ensure that they install extensive guardrails when using the model, because of the many ways that it can expose a company to legal and reputational risk.

Cameras are used routinely by police across the US to identify citizens, their faces cross-matched against databases of suspects and past criminals. Yet researchers claim there is too little scrutiny of how these tools work, and have found inherent racial bias in the system. So does a sophisticated, visual analysis tool reflect human prejudice and if so, who does that effect? "Studies indicate there's racial bias in the software," said Jonathan Frankle, staff technologist at Georgetown Law School. Working with law fellow Clare Garvie, Frankle has requested public information from more than 100 police departments across the country.