Explanation and Fairness in Unsupervised Learning
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.
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 (15 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 (60 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  for classic centroid based methods.
∗ Extensions for efficiency  to allow multi-state , multiple  PSVs.
∗ Fairness methods for graph based  and other clustering algorithms .
∗ Emerging work on individual level fairness  .
∗ Algorithm independent methods of fairness - post-processing to ensure fairness .
– Explanation in Clustering
∗ Explanation - Classic Work (Conceptual Clustering)  .
∗ Explanation using feature spaces .
∗ Explanation using alternative feature sets , , .
– Fairness and Explanation in Emerging Unsupervised Learning Settings.
∗ Outlier detection  . ∗ Nearest neighbor search . ∗ Embedding .
– The Connections Between Fairness and Explanation.
• Future Directions and Applications (15 minutes)
– Applications and Transitions into Industry
∗ IBM’s AI Fairness 360 toolkit
∗ Accenture’s Fairness verification toolkit
– Open Research Questions