Artificial Intelligence

2 statements.

Author	Tags	Primary Session	Secondary Sessions	Position Statement
Aaron Halfaker	Artificial Intelligence	Research, Analytics, and Machine Learning		The future of responsible AI design is auditing systems. When we deploy AIs that make inherently subjective judgments that affect people and their work, we must also provide a means for them to audit and critique the AI. Did the AI mark the wrong thing as vandalism? Then it can silence a contribution. Did the AI fail to note a high quality article? Then we might direct traffic away from good content? Did the AI recommend the wrong type of thing? Then we might keep people in a filter bubble rather than helping them broaden their knowledge. There's a lot of conversation in the public sphere about how AIs cause ethical and social problems. Google's image search suggests that all CEOs are men. Facebooks feed filter reduces the visibility of conflicting opinions. The general call among researchers and ethicists is for transparency. At Wikimedia, transparency is an old idea. We've always developed our technologies transparently. But this hasn't made us immune from the problems that AIs wreck. Auditing systems are the future. They are a means towards giving users power over the AIs that govern our experience. We should be talking about how to build them.
Moritz Schubotz	Artificial Intelligence, Code Review, Testing, Volunteer Developers	Growing the MediaWiki Technical Community		Title Developing software in a wiki way Background Over the last 15 years, MediaWiki evolved from a simple PHP script to a complex and highly integrated family of products and services, serving knowledge to billions of humans. Every change might cause an instability or a complete failure of the system. Thus, measures including code review, automated unit testing and code/ product ownership, have been established to guarantee the stability of the software. The drawback of this approach is that improving the software became very challenging for volunteer contributors. This proposal seeks to lower the barriers for volunteer contributors while maintaining the stability of the system. Advice (1) Reduce the effort of code review by applying Artificial Intelligence methods. Thus, reviewers can focus on non-formal comments. (2) Develop a dialog platform that ensures that volunteer contributors are aware of the next steps and the roadmap for their change on the way to production. (3) Establish a team that supports volunteer developers, who want to make a difference that is not listed in the annual plan by providing temporary code or product ownership. (4) Improve testing and evaluation to measure the effect of every single change and to identify code or even whole services that are no longer neccary and can be switched off.