Fairness and Bias

Creating Ethical AI with a neuroscientific understanding of mindfulness combined with a critical theory-informed view of causality and neurosymbolic AI.

The discovery of DNA was a scientific accomplishment that undoubtedly changed the world. Disappointingly, one of the three men who received the Nobel prize for the discovery, James Watson, has a long history of racist, sexist, homophobic and anti-Semitic remarks (source). Despite the passage of time, protected by his privilege…


Hands-on Tutorials

Econometric-focused introduction to vector autoregression models and impulse response functions for multivariate macroeconomic analysis.

Consider the difficulty of discovering meaningful patterns in time; for instance, with the evolution of sales, the change in weather, or even one’s own sleep cycle. Compared to other data types, time series data has its own unique considerations. To disambiguate, the stochastic yet cyclical qualities require modeling techniques particularly…


Thoughts and Theory

A data science introduction to econometrics with Python library: DoWhy, including a detailed code walkthrough of a case-study causality paper

Data scientists have a tendency to focus on descriptive and predictive analysis, but neglect causal analysis. Decision making, however, requires causal analysis, a fact well recognized by public health epidemiologists during this Covid-19 pandemic. Due to my background in biology, I had internalized the adage “correlation does not equal causation”…


Deep learning NLP tutorial on analyzing collections of documents with Extractive Text Summarization, utilizing Transformer-based sentence embeddings derived from SOTA language models

Natural language processing (NLP) is a diverse field; the approaches and techniques are as varied as the diversity of textual samples available for analysis (eg. blogs, tweets, reviews, policy documents, new articles, journal publications etc.). Choosing a good approach requires an understanding of the questions being asked of the data…


A practical exploration of the Natural Language Processing technique of Latent Dirichlet Allocation and its application to the task of topic modeling.

Topic modeling is a form of unsupervised machine learning that allows for efficient processing of large collections of data, while preserving the statistical relationships that are useful for tasks such as classification or summarization. The goal of topic modeling is to uncover latent variables that govern the semantics of a…


Potential uses of Causal Inference to improve the robustness, fairness, and interpretability of Natural Language Processing models

Two years ago, Google announced that it was using an AI language model, named BERT, to improve Google Search; back then it was used in roughly 10% of searches. A year later, in a post titled “How AI is powering a more helpful Google”, the company reported that BERT is…


Thoughts and Theory

Estimating causal effects from text variables by applying NLP methods, and its application to social science research.

Recently, I was honoured to be interviewed for an author spotlight by TDS editor, Ben Huberman. I took the opportunity to highlight my connectionist approach to learning data science. In particular, I discussed my desire to continuously connect ideas — that inclination is responsible for this article that combines two…


Fairness and Bias

Applying a critical theory framework to AI Ethics, while using neuroscience to understand unconscious bias with synaptic plasticity.

A year ago when discussing racial bias present in facial recognition, AI pioneer Yan Lecun controversially tweeted, “ML systems are biased when data is biased” (source: Twitter). This provoked a response from AI Ethics researcher, Timnit Gebru, who expressed her frustration at the overly simplistic framing of this issue, an…


Hands-on Tutorials

Combining data science and econometrics for an introduction to the DeepIV framework, including a full Python code tutorial.

Historically, both economists and philosophers have been preoccupied with extracting an understanding of cause and effect from empirical evidence. David Hume, an economist and philosopher, is renowned for exploring causality, both as an epistemological puzzle and as a matter of practical concern in applied economics. In an article titled “Causality…


Thoughts and Theory

Introduction to causal machine learning for econometrics, including a Python tutorial on estimating the CATE with a causal forest using EconML

Equity is not the same principle as equality. Within the social context they both relate to fairness; equality means treating everyone the same regardless of need, while equity means treating people differently depending on their needs. Consider vaccinations, if we based public health policy on equality, perhaps there would be…

Haaya Naushan

Research Consultant and Data Scientist. Enthusiastic about machine learning, social justice, video games and philosophy.

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