Design for fairness in AI

Horizon Technical Enablement Sessions
Horizon Technical Enablement Sessions
Public group

Johan Huizingalaan 400

Johan Huizingalaan 400 · Amsterdam

How to find us

Find us at the B3 Building, Johan Huizingalaan 400 Amsterdam, event space 'The Howard'

Location image of event venue


##### WHERE #####
B3. Amsterdam, Johan Huizingalaan 400 Amsterdam
Event space: The Howard

##### AGENDA #####
16.00- Doors open
16.30 - 17.15 Design for fairness in AI - Dasha Simons
17.15 - 18.00 AI Fairness 360 open source toolkit - Damiaan Zwietering
18.00 - 18.30 Drinks and Pizza
18.30 - 19.45 AI Openscale - Willem Henkdriks
19.45: Drinks

##### Session abstracts ####:
Design for Fairness in AI by Dasha Simons
Artificial intelligence is an emerging field which unleashes massive new business opportunities, however, bears (ethical) debate. More decisions are left to machines and algorithms in society, which have consequential outcomes. Companies and humans unfortunately learned by mistakes in practice that AI systems can be terribly unfair... . However, how to support AI teams with the creation of more ethical AI systems, bridging the gap between ethical AI principles and practice?

AI Fairness 360 open source toolkit by Damiaan Zwietering
Guided demo and hands-on on the AI Fairness 360 toolkit. This extensible open source toolkit can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. Containing over 70 fairness metrics and 10 state-of-the-art bias mitigation algorithms developed by the research community, it is designed to translate algorithmic research from the lab into the actual practice of domains as wide-ranging as finance, human capital management, healthcare, and education. We invite you to use it and improve it.

Watson Openscale by Willem Hendriks:
Guided demo & hands-on on Watson openscale. IBM Watson OpenScale tracks and measures outcomes from AI across its lifecycle, and adapts and governs AI to changing business situations — for models built and running anywhere. It will assist in maintaining regulatory compliance by tracing and explaining AI decisions across workflows, and intelligently detect and correct bias to improve outcomes.