Call For Papers

The 1st NIPS Workshop on Ethical, Social and Governance Issues in AI invites paper contributions on theory, methodology, tools and applications related to the ethics of algorithmic decision making, as well as its policy and governance aspects.

Description

Ethics is the philosophy of human conduct: It addresses the question “how should we act?” Throughout most of history the repertoire of actions available to us was limited and their consequences constrained in scope and impact through dispersed power structures and slow trade. Today, in our globalised and networked world, a decision can affect billions of people instantaneously and have tremendously complex repercussions. Machine learning algorithms are replacing humans in making many of the decisions that affect our everyday lives. How can we decide how machine learning algorithms and their designers should act? What is the ethics of today and what will it be in the future?

We would like to invite contributions on the following three general themes:

Advancing and Connecting Theory and Methodology:
- How do fairness, accountability, transparency, interpretability and causality relate to ethical decision making?
- How can such principles be built into machine learning systems and what are the trade-offs involved?
- What can we learn from economics and social science to understand the implications of the widespread application of machine learning algorithms on markets and society?
- How can ethical principles established in philosophy and law be applied to questions about the ethics of machine decisions? Are these principles still relevant today?
- What are the fundamental principles on which we might base an ethical approach to AI?

Tools and Applications:
- Real-world examples of how ethical considerations are affecting the design of ML systems and pipelines.
- Applications of algorithmic fairness, transparency or interpretability to produce better outcomes. Approaches to designing systems to be auditable by 3rd parties whilst maintaining privacy of user data. Tools that aid in identifying and or alleviating issues such as bias, discrimination, filter bubbles, feedback loops etc. and enable actionable exploration of the resulting trade-offs.

Policy and Governance:
- How can regulatory, legal or policy frameworks be designed to continue to encourage innovation, so society as a whole can benefit from AI, whilst still providing protection against its harms?
- What are the implications of the shift to algorithmic decision making for existing laws, social policy and governance structures?
- How are organizations, institutions and industry responding to regulatory efforts such as the GDPR?

Instructions for Authors

Submissions must be short papers in NIPS format and consist of a maximum of 4 pages (excluding references). Author names do not need to be anonymized. Submissions must represent new work, i.e., work that has not been previously published or accepted for publication. However, papers that expand previous related work by the authors and papers that have appeared on non peer-reviewed websites (such as arXiv) or that have been presented at workshops (i.e., venues that do not have a publication proceedings) are acceptable. Accepted papers will be presented as posters or contributed talks. Authors of accepted papers will be asked to post their submissions on arXiv. The workshop website will provide links to the accepted papers on arXiv. Accepted papers will be considered non-archival, and may be submitted elsewhere (modified or not). The submission process will be conducted using an online submission system: https://easychair.org/conferences/?conf=wesgai18

Invited Speakers

Hannah Wallach, Microsoft Research
Manuel Gomez-Rodriguez, Max Planck Institute
Jason Schultz, NYC Law and AI Now Institute
Jon Kleinberg, Cornell
Deirdre Mulligan, UC Berkeley
Rich Caruana, Microsoft Research

Organizers

Chloé Bakalar
Sarah Bird
Tiberio Caetano
Edward Felten
Dario Garcia-Garcia
Isabel Kloumann
Finn Lattimore
Sendhil Mullainathan
D. Sculley

Important Dates