In many applications of societal concern, algorithmic outputs are not decisions, but only predictive recommendations provided to humans who ultimately make the decision. However, current technical and ethical analyses of such algorithmic systems tend to treat them as autonomous entities. In this talk, I draw on works in group dynamics and decision-making to show how viewing algorithmic systems as part of human-AI teams can change our understanding of key characteristics of these systems—with a focus on accuracy and interpretability—and any potential trade-off between them. I will discuss how this change of perspective (i) can guide the development of functional and behavioral measures for evaluating the success of interpretability methods and (ii) challenge existing ethical and policy proposals about the relative value of interpretability.

There has been a strong intuition in the Machine Learning community that interpretability and causality ought to have a strong connection. However, the community has not arrived at consensus about how to formalize this connection. In this talk, I will raise questions about conceptual and technical ambiguities that I think make this connection hard to specify. The goal of the talk is to raise points for discussion, expressed in causal formalism, rather than to provide answers.

In this talk, I will discuss the connections between physical nonequilibrium systems and common algorithms employed in machine learning. I will report how machine learning has been used to expand the scope of physical nonequilibrium systems that can be effectively studied computationally. The interpretation of the optimization procedure as a nonequilibrium dynamics will be also examined. Specific examples in reinforcement learning will be highlighted.

The human genome sequence contains the fundamental code that defines the identity and function of all the cell types and tissues in the human body. Genes are functional sequence units that encode for proteins. But they account for just about 2% of the 3 billion long human genome sequence. What does the rest of the genome encode? How is gene activity controlled in each cell type? Where do the gene control units lie in the genome and what is their sequence code? How do variants and mutations in the genome sequence affect cellular function and disease? Regulatory instructions for controlling gene activity are encoded in the DNA sequence of millions of cell type specific regulatory DNA elements in the form of functional sequence syntax. This regulatory code has remained largely elusive despite exciting developments in experimental techniques to profile molecular properties of regulatory DNA. To address this challenge, we have developed high performance neural networks that can learn de-novo representations of regulatory DNA sequence to map genome-wide molecular profiles of protein DNA interactions and biochemical activity at single base resolution across 1000s of cellular contexts while accounting for experimental biases. We have developed methods to interpret DNA sequences through the lens of the models and extract local and global predictive syntactic patterns revealing many causal insights into the regulatory code. Our models also serve as in-silico oracles to predict the effects of natural and disease-associated genetic variation i.e. how differences in DNA sequence across healthy and diseased individuals are likely to affect molecular mechanisms associated with common and rare diseases. Our predictive models serve as an interpretable lens for genomic discovery.

Making machine learning systems interpretable can require access to information and resources. Whether that means access to data, to models, to executable programs, to research licenses, to validation studies, or more, various legal doctrines can sometimes get in the way. This talk will explain how intellectual property laws, privacy laws, and contract laws can block the access needed to implement interpretable machine learning, and will suggest avenues for reform to minimize these legal barriers.