The Space Science in Context 2020 is a virtual conference bringing space and science and technology studies (STS) scholars together for an interactive event on 14 May 2020. The conference […]
The Space Science in Context 2020 is a virtual conference bringing space and science and technology studies (STS) scholars together for an interactive event on 14 May 2020.
The conference will have three sessions–Decolonizing Space; Computing, Technology and Space; and Space for Society and will bring together 12 speakers and 30 poster presenters at different times in a video-chat hybrid format.
Artash Nath will be presenting his poster “Will Machine Learning Algorithms Take their Bias to Space?” at the conference.
Machine Learning algorithms are data intensive. They need lots of data to learn before they can perform tasks without human intervention. Earth-based machine learning models, be it autonomous cars, facial detection, or emotions assessment algorithms have biases based on age, gender, citizenship, and race.
The bias may occur because of:
1. Choice of training data that serves as input to the machine learning models
2. Biases in labeling or interpretation of the data
3. Biases in the design of machine learning algorithms
4. Biases in supervision or reinforcement of results produced
As more countries accelerate their space exploration programs to bring more humans to the lower earth orbit, back to the Moon, to Mars and beyond, we are seeing a rise in the use of machine learning in space-based applications.
Artificial intelligent robots (CIMON and Kirobo) have already made trips to the International Space Station. Over time, we will see more rovers, spacecraft operations, scientific experiments, and astronaut health and safety systems embedding artificial intelligence and turn autonomous.
With the rise in machine learning applications in the space sector including in robot autonomy and human space exploration will these earth-based biases persist in space? If so, how to reduce bias in the machine learning applications?
Find out by registering for this event for free and viewing the poster.
The conference is being organised by Eleanor Armstrong and Divya M. Persaud from the University College of London. They have made inclusiveness and accessibility a part of the conference. All invited speakers’ videos will have closed-captioning accompanied by a full transcript. Poster speakers will be required to have screen-reader friendly posters that include full image descriptions.