Explanation and Fairness in
Unsupervised Learning
Overview: Unsupervised learning approaches such as clustering are extensively used in AI with explanation and fairness emerging as important challenges. We cover explainable and fair unsupervised learning from multiple perspectives such as the philosophical under-pinnings, algorithmic details and application areas.
Bio: Ian
Davidson: I am a Professor in the department of computer science at the
University of California, Davis since 2009 (and before that an assistant and
associated Professor). I teach AI, ML and for the past decade a course on
ethics and AI/Technology. My research is a mix of fundamental algorithmic
contributions to unsupervised learning (clustering and outlier detection) and
supervised learning (transfer, active learning) and more practical applications
(with others) in neuroscience and intelligent tutoring. With regard to fairness
and explanation in unsupervised learning, I have published papers at NIPS 2018,
IJCAI 2018, AAAI 2020 and ECAI 2020 on the topic and I have grants from NSF on
core research on the topic and NIH to apply XAI to precision medicine.
My website is here my google
scholar page is here.
Slides To Be Posted
Tutorial Outline and References
The tutorial will consist of
three parts: i) Legal, Philosophical and Ethical
Overviews and Motivations, ii) Algorithmic Details and iii) Applications and
Future Work.
Motivations (25 minutes)
– Legal (in California, USA
and Europe), ethical and philosophical definitions of fairness
and explanations.
– Why is fairness and
explanation so necessary for unsupervised learning.
– The notion of protected
state variables (PSV). Examples of individual level and group level unfairness
– Connection to Utilitarianism and Deontological schools of
normative ethics.
– The need for explanations to algorithm designer, domain scientists
and public.
Algorithmic Details (120 minutes)
– The two approaches for
adding fairness and explanation to algorithms - (apriori)
fairness/explanation by-design and (aposteri)
post-processing to increase fairness/explanation.
– Fairness in
Clustering
∗ Fairness - Seminal paper encoding disparate impact in
clustering [6] for classic centroid based methods.
∗ Extensions for efficiency [1] to allow multi-state [19],
multiple [2] PSVs.
∗ Fairness methods for graph based [16] and other clustering
algorithms [5].
∗ Emerging work on individual level fairness [21] [17].
∗ Algorithm independent methods of fairness - post-processing
to ensure fairness
[12].
Break: 10 minutes
–
Explanation in Clustering
∗
Explanation - Classic Work (Conceptual Clustering) [13] [18].
∗ Explanation using feature
spaces [4].
∗ Explanation using
alternative feature sets [10], [20], [8].
– Fairness and
Explanation in Emerging Unsupervised Learning Settings.
∗ Outlier detection [7] [11].
∗ Nearest neighbor search [15]. ∗ Embedding [3].
– The Connections
Between Fairness and Explanation.
Future
Directions and Applications (15 minutes)
– Applications and Transitions into
Industry
∗ IBMճ AI Fairness 360 toolkit
∗ Accentureճ Fairness verification
toolkit
– Open Research Questions