Workshop on AI Ethics and Values in Biomedicine - Technical Challenges and Solutions

Pacific Symposium on Biocomputing, Hawaii January 3-7, 2020

Prof. D. Petkovic, SFSU    petkovic@sfsu.edu

Prof. L. Kobzik, Harvard

Dr. R. Ghanadan, Google

Introductioin

Artificial Intelligence (AI) technologies are increasingly impacting biomedicine and healthcare. However AI systems may produce errors, can exhibit overt or subtle bias, may be sensitive to noise in the data, and often lack transparency and explainability.  These shortcomings raise many legal, ethical policy concerns that impede wider adoption of this potentially very beneficial technology. The technical community, the media, as well as political and legal stakeholders have recognized the problem and have begun to seek solutions.

In the highly regarded Asilomar AI Principles established by the Future of Life Institute and recently endorsed by California legislature, the section on AI Ethics and Values includes a number of powerful and well-reasoned principles on great relevance to biocomputing area, such as: safety; failure transparency; judicial transparency; privacy; and human control.  However, developing technical solutions to help implement and verify these and similar AI ethics and values principles  has proven to be a substantial challenge, and we believe that it is imperative for the scientific and research community to focus on this problem.

Workshop goals

The proposed workshop will focus on scientific and technical issues that are central to ensuring better AI ethics and values in health and biomedical fields. It will provide an overview and discussion on the technical challenges.  It will also focus on potential solutions that can enable design, development, evaluation, deployment and maintenance of ethical and value-based AI algorithms and solutions in biomedicine, consistent with Asilomar AI principles outlined above. The workshop will aim to address, among others, the following questions all in the context of biomedicine:

· What are the key measurable and auditable components and issues comprising AI ethics and values?

· What state of the art algorithms and solutions are available today that address AI ethics and value issues in a principled and measurable way? Do we need to develop radically new AI algorithms or simply enhance existing ones?

· Can we develop compliance tools and algorithms to verify for adherence AI ethics and values principles

· How can we make AI solutions easier to understand, to explain (both at model and sample level), and to adopt by experts and non-experts alike?

· How to better leverage currently underutilized data visualization and human-AI user interfaces

· What specific practices can be recommended to ensure design, development, evaluation, testing and verification of ethical and value based AI systems?

Workshop speakers and panelists

Five accomplished speakers with expertise relevant to the workshop questions will share their viewpoints. They are:

· Prof. Su-In Lee (Computer Science & Engineering, U. of Washington)

· Dr. Claudia Perlich, (Senior Data Scientist, Two Sigma; Stern NYU)

· Prof. Chris Re, (Computer Science, Stanford) 

· Prof. Sameer Singh (Information and Computer Science, UC Irvine) 

· Prof. Jessica Tenenbaum (Biostatistics and Bioinformatics, Duke University)

The workshop will end with a moderated discussion with the speakers and the audience with the goal to propose and summarize best ways to move forward on these issues.

We cordially and enthusiastically invite you to participate!