What Is Beyond the “Human-Centered” Design of AI-driven Systems?

Challenges in current AI-driven Systems

Typical ML-Pipeline

Case 1: Using ML in the Public Agency

Chances for employment
Translated features (Source: AMS Documentation)

Bias in Software Systems

Sources of bias, inspired by (Baeza-Yates, 2019)

Point of Participation in ML Pipelines

Points of participation inspired by (Dudley & Kristensson, 2018)

Case 2: Using ML in Open Peer Production

Weighting the costs of false positives and false negatives

What is needed?

--

--

--

Researcher in the area of Human-Centered Computing, Special focus on Human-Machine Collaboration, Advocates Open Knowledge in Science and beyond

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Artificial Intelligence review

Will AI-Powered Chatbots Transform The Customer Support?

Three Rules of AI-First

Dactyl The Robot: A Step Towards Artificial General Intelligence

Anticipation Algorithm

Breaking through the glass ceiling of NLU with Upper Ontology

AI Takes to Trolling Twitter: Plays Dumb, Plans Murder

Aurélien Géron Deep Learning crash-course & bonus interview (part 3/3)

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Claudia Müller-Birn

Claudia Müller-Birn

Researcher in the area of Human-Centered Computing, Special focus on Human-Machine Collaboration, Advocates Open Knowledge in Science and beyond

More from Medium

Why Classical Persona segmentation is useless for operational Marketing

Intro to Academic Research: Notes and Reflections from a Research Mentor

University-Business Collaboration

The Value of Robotic Process Automation For Traditional Insurance Companies