FAT* 2018 Conference Notes, Day 2

Keynote 2: Deborah Hellman

Deborah Hellman of UVA Law starts the day off with a keynote on justice and fairness. She opens with a quote from Sidney Morgenbesser about what is unfair and what is unjust, asking if fairness is about treating everyone the same. She follows with a quote from Anatole France — “In its majestic equality, the law forbids rich and poor alike to sleep under bridges, beg in the streets and steal loaves of bread.” In practice, policies that formally treat everyone the same affect people in different ways.

Hypothesis 1: Treat like cases alike.
This hypothesis relies on choosing a proxy by which to classify people and decide how to treat them differently. That is, if treating everyone the same is unfair because of the situations they’re in lead to different outcomes, classify them into different cases based on their situations, and treat each case separately. This hypothesis seems to fall apart based on how the classifications are made and the intentions of those classifications in search of certain outcomes. This leads to the next hypothesis…

Hypothesis 2: It’s the thought that counts.
These traits are usually adopted for bad reasons. The classifications are made to impose differing treatments with moral decisions that are misguided or unjust. For example, an employer may avoid hiring women between the ages of 25 and 40 to avoid having to pay women who may have children to take care of. The goal is not to avoid employing women, but to increase productivity. The intent behind the classification is itself misguided or flawed.

Hypothesis 3: “Anti-Classification”
The use of classifications, in particular classifications based on certain traits e.g. race, gender, can lead to unintended effects and denigration.

Hypothesis 4: Bad Effects
Certain classifications themselves can compound injustice — for example, charging higher life insurance rates to battered women.

Hypothesis 5: Expressing Denigration
For example, saying “All teengaers must sit in the back of the bus” vs. “All blacks must sit in the back of the bus” express different ideas. Regardless of the intention, there is denigration inherent in the classification. She cites Justice Harlan’s dissent in Plessy v. Ferguson.

Indirect Discrimination and the Duty to Avoid Compounding Injustice
The Empty Idea of Equality
Even Imperfect Algorithms Can Improve the Criminal Justice System

Discussion: Cynthia Dwork

Session 3: Fairness in Computer Vision and NLP

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (data)

Joy Buolamwini gives a talk on her now infamous paper on the poor performance of facial analysis technologies on non-white, non-male faces. She uses a more diverse dataset to benchmark various APIs. After reporting the poor performance to various companies, some actually improved their models to account for the underrepresented classes.

See also:
The Perpetual Line-Up

Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies

Taneea Agrawaal presents her analysis of gender stereotyping in Bollywood movies. The analysis was done with a database of Bollywood movies going back intio the 1940s, along with movie trailers from the last decade and a few released movie scripts. Syntax analysis is done to extract verbs related to males and females to study the actions associated with each. She argues that the stories told and representations expressed in movies affect society’s perception of itself and subsequent actions. For example, Eat Pray Love caused an increase in solo female travel, and Brave and Hunger Games caused a sharp increase in female participation in archery.

See also:
The Next Bechdel Test (code)

Mixed Messages? The Limits of Automated Social Media Content Analysis

Natasha Duarte presents a talk focused on how NLP is being used to detect and flag content online for surveillance and law enforcement (for example, to detect and remove terrorist content from the internet). She argues that NLP tools are limited because they must be trained on domain-specific datasets to be effective in particular domains, and governments generally use pre-packaged solutions which are not designed for these domains. Manual human effort and language and context-specific work is necessary for any successful NLP system.

Session 4: Fair Classification

The cost of fairness in binary classification

Bob Williamson presents his research which frames adding fairness to binary classification as imposing a constraint. There must be a cost to this constraint, and Williamson presents a mathematical approach to measuring that cost.

Decoupled Classifiers for Group-Fair and Efficient Machine Learning

Nicole Immorlica shows that “training a separate classifier for each group (1) outperforms the optimal single classifier in both accuracy and fairness metrics, (2) and can be done in a black-box manner, thus leveraging existing code bases.” With the caveat that it “requires monotonic loss and access to sensitive attributes at classification time.”

A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions

Alexandra Chouldechova presents a case study in which a model was used to distill information about CPS cases to create risk scores to aid call center workers in case routing. She discusses some of the pitfalls of the model, and how improvements were made to address them along the way. She ends by emphasizing that this model is just one small black box which acts as one signal among many in a larger system of processes and decision-making.

See also:

Fairness in Machine Learning: Lessons from Political Philosophy

Reuben Binns takes a mix of philosophy and computer science to nudge the debate around ML fairness from “textbook”/legal definitions of fairness to one that goes back to more philosophical roots. It follows a trend at the conference of focusing on the context in which models are used, the moral goals and decisions of the models, and a re-analysis of concepts of fairness that the rest of the field may consider standard.

Session 5: FAT Recommenders, Etc.

Runaway Feedback Loops in Predictive Policing (code)

Carlos Scheidegger discusses a mathematical method, Polya Urns, that he’s used to discover feedback loops in PredPol. Such systems are based on a definition of fairness which states that areas with more crime should receive a higher allocation of police resources. He discusses the flaws of such methods and suggests some strategies to avoid these feedback loops.

All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness (code)

Michael Ekstrand asks: Who receives what benefits in our recommender systems?

Recommendation Independence (code)

Toshihiro Kamishima

Balanced Neighborhoods for Multi-sided Fairness in Recommendation

Robin Burke

FAT* 2018 Conference Notes, Day 1

This weekend, I am at the Conference on Fairness, Accountability, and Transparency (FAT*) at NYU. This conference has been around for a few years in various forms — previously as a workshop at larger ML conferences — but has really grown into its own force, attracting researchers and practitioners from computer science, the social sciences, and law/policy fields. I will do my best to document the most interesting bits and pieces from each session below.

Keynote 1: Latanya Sweeney

Sweeney has an amazing tech+policy background in this field — the work she did on de-anonymization of “anonymized” data lead to the creation of HIPAA. She has also done interesting work on Discrimination in Online Ad Delivery (article). She argues that technology in a sense dictates the laws we live by. Her work has centered around specific case studies that point out the algorithmic flaws of technologies that seem normal and benign in our daily lives. Technical approaches include an “Exclusivity Index”, which takes a probabalistic approach to defining behavior that is anomalous in particular sub-groups. Two noted examples of unintended consequences of algorithms are discriminatory pricing algorithms in Airbnb and the leaking of location data through Facebook Messenger.

In the subsequent discussion with Jason Schultz, the focus is on laws and regulation. She states that there are 2000+ US privacy laws, but because they are so fragmented, they are rendered completely ineffective in comparison to blanket EU privacy laws. The case is made that EU laws have teeth, and in practice may raise the data privacy bar for users all over the world. She also stresses the need for work across groups, including technologists, advocacy groups, and policy makers. She presents a bleak view of the current landscape, but also presents reasons to be optimistic.

Session 1: Online Discrimination and Privacy

Potential for Discrimination in Online Targeted Advertising

Till Speicher presents a paper on the feasibility of various methods of using Facebook for discriminatory advertising. There are three methods presented:

  • Attribute-based targeting, which lets advertisers select certain traits of an audience they wish to target. These attributes can be official ones tracked by Facebook (~1100), or “free-form” attributes such as a user’s Likes.
  • PII-based targeting, which relies on public data such as voter records. Speicher takes NC voter records and is able to filter out certain groups by race, then re-upload the filtered voter data to create an audience.
  • “Look-alike” targeting, which takes an audience created from either of the above methods and scales it automatically — discrimination scaling as a service!
    These methods make it clear how Facebook’s ad platform could be used to target and manipulate large groups of people. Speicher suggests that the the best methods to mitigate such efforts may be based on the outcome of targeting (i.e. focusing on who is targeted, rather than how).

Discrimination in Online Personalization: A Multidisciplinary Inquiry

Amit Datta and Jael Makagon present this study on how advertising can be used for discriminatory advertising (e.g. to target a specific gender for a job adversiting). See past work here: Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination. Jael has a law background, and walks the audience through different anti-discrimination laws and which parties may be held responsible in different scenarios. He describes a mess of laws that don’t quite apply to any party in the discrimination scenarios. Amit describes cases where advertisers can play active rather than passive roles in discriminatory advertising, and Jael describes the legal implications that can result from that.

They ultimately call out a “mismatch between responsibility and capability” in the advertising world, and they propose policy and technology-based changes that may be effective in preventing such discrimination.

Privacy for All: Ensuring Fair and Equitable Privacy Protections

Michael Ekstrand and Hoda Mehrpouyan ask “Is privacy fair?”. They start by discussing definitions of privacy, including:

  • Seclusion
  • Limitation
  • Non-intrusion
  • Control
  • Contextual integrity

Ekstrand argues that the tools we use to assess fairness of decision-making systems can be used to analyze privacy in systems. He raises three questions:

  1. Are technical or non-technical privacy protection schemes fair?
  2. When and how do privacy protection technologies or policies improve or impede the fairness of the systems they affect?
  3. When and how do technologies or policies aimed at improving fairness enhance or reduce the privacy protections of the people involved?

They mention an example where Muslim taxi drivers are outed in anonymized NYC TLC data, and where James Comey’s personal Twitter account was discovered using public data. They discuss the cost of guarantees of privacy for certain schemes and definitions of privacy, and how that affects “fairness” for different definitons of fairness.

Relevant work:

Session 2: Interpretability and Explainability

“Meaningful Information” and the Right to Explanation

Andrew Selbst starts his talk asking why explainability is important, saying “what is inexplicable is unaccountable”. In his eyes, explainability brings a chain of decision-making that leads to accountability. He then explains some aspects of GDPR and asks if it contains an implicit “right to explanation” in some of its provisions. He cites current legal arguments that discuss whether or not such a right exists:

Notably, Selbst says that deep learning isn’t actually at risk of being banned, in particular becuase such a requirement is against completely automated systems, implying that deep learning systems are fine to use as long as they are just one factor in a larger explainable system with a human in the loop.

Interpretable Active Learning (code)

Richard Philips gives a talk on using LIME for active learning. By applying LIME to assess which features cause certainty in model classifications during active learning, their method can be used across populations to show if models are biased for or against certain subgroups.

Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment

Chelsea Barbaras argues that the debate around pre-trial risk assessment tools is shaped by old assumptions about the role risk assessment plays in these trials. Old risk-based systems considered factors that were drawn from social theories of criminal behavior at the time, that have since changed. They also focused on traits of the individual, which neglected to consider broader social factors in these cases. She also criticizes regression-based risk assessment in particular, due to the pitfalls of drawing conclusions from correlation vs. causation. She advocates for seeing risk not as a static thing to be predicted, but as a dynamic factor to be mitigated. She also discusses how we can use a causal framework of statistics and experiment design to ask better questions about risk assessment.

She also points to the recent work of Virginia Eubanks and Seth Prins:

  • Can we avoid reductionism in risk reduction?
  • An Investigation of the Causal Association between Changes in Social Relationships and Changes in Substance Use and Criminal Offending During the Transition from Adolescence to Adulthood

Tutorials 1

Quantifying and Reducing Gender Stereotypes in Word Embeddings

Understanding the Context and Consequences of Pre-trial Detention

21 Fairness Definitions and Their Politics

Arving Narayanan gives a “survey of various definitions of fairness and the arguments behind them” which can act as “‘trolley problems’ for fairness in ML”.

Algorithmic decision making and the cost of fairness
Rather than maximizing accruacy, the goal should be about “how to make algorithmic systems support human values”.

Tutorials 2

Auditing Black Box Models

People Analytics and Employment Selection: Opportunities and Concerns

A Shared Lexicon for Research and Practice in Human-Centered Software Systems

PlaNet: Classifying Planets with Neural Networks

After completing Jeremy Howard’s Deep Learning course, I wanted to put my skills to the test on something fun and interesting, so I set out to train a neural network that classified planets. I’m happy with the end result (and its cheeky name): plaNet.

I wanted to classify major solar system planets based on salient features. The issue with this approach is that there isn’t very much data to train a neural network on. I scraped AstroBin for amateur photos of planets, but I found that most of them simply looked like smudges, and the outer planets were either unrecognizable or missing entirely.

Some of the unaugmented training data used for Jupiter, mostly from NASA.

To get around these issues, I based my approach on two methods: data augmentation on my small dataset, and fine-tuning an existing neural network. Data augmentation is simple in Keras, so I dramatically increased my dataset size simply by applying transformations to my initial images. I fine-tuned my network on VGG’s ImageNet convolutional layers (a classic approach to transfer learning). I dropped out the last fully-connected layer, which was trained to classify everyday objects, and kept the convolutional layers. These layers are great for identifying features — edges, shapes, and patterns — that could still be found in my images of planets. At this point, I pre-calculated the output of the convolutional layer on the initial and augmented datasets in order to easily combine them into one feature set, then I was able to train with a relatively solid test accuracy (~90%). I used a high dropout rate in order to avoid overfitting to my small training dataset, and it seems to have worked.

I want to highlight the simplicity of this approach. Because we’re simply fine-tuning a pre-trained neural network, we can access what is essentially the state of the art in deep learning with just a few lines of code and a small amount of computing time and power (compared to training an entire network from scratch). My work was mostly in preparing the datasets and fine-tuning different parameters until I was happy with the results. If you haven’t already, I encourage you to take a look at the course online. Many thanks to Jeremy Howard for giving me a practical approach to something I’ve only had theoretical backing for so far.

Observations from the New York Scientific Data Summit

Deep learning impresses and disappoints

Multiple talks discussed results from deep learning techniques, especially convolutional neural networks, and the effectiveness of the methods varied wildly. Some experiments yielded only 50% classification accuracy, which doesn’t ultimately seem helpful or effective at all. I’m unsure whether other techniques were attempted or considered, but it’s clear that deep learning isn’t the most effective approach for every single problem. It’s a shiny new hammer that makes every problem look like a nail. Libraries like TensorFlow make it more accessible, but there is still a visible gap between those who can implement it and those who can implement it effectively.

Re-inventing the wheel

A few groups demonstrated tools that were developed in-house that already have excellent open source alternatives. I’m not sure whether they were unaware of the existing libraries or just wanted something more finely-tuned for their own purposes, but it seems that a lot of scientific time is spent coming up with solutions for problems that are already solved. Regardless, there were plenty of examples of people who did use open source libraries effectively, so the progress there is something to be proud of.

Sagan Exoplanet Workshop Day 1


Eric Feigelson (Penn State) discusses Statistics and the Astronomical Enterprise, and why statistics are so essential to the discovery and study of Exoplanets. The history and development of statistics in relation to astronomy as well as the present state of the field are covered. Key examples of essential statistical applications in astronomy and astrophysics are discussed, as well as an outlook on potential future developments and applications. Practical computing implementations with R are discussed.

Jessi Cisewski (Yale) goes over Bayesian Methods. She covers the basics of Bayesian analysis (Bayes’ Theorem, prior and posterior distributions, and inference with posteriors). Examples of different analyses using different models and distributions are given. Classical/Frequentist approaches are contrasted with the Bayesian approach, and best practices are covered.

Xavier Dumusque (Universite de Geneva) and Nikole Lewis (STScI) introduce the hands-on sessions of the week. Xavier discussed differentiating Radial Velocity signals from planetary signals, and Nikole discussed detecting exoplanets in upcoming JWST data and analyzing their spectra.


David Kipping (Columbia) gives A Beginner’s Guide to Monte Carlo Markov Chain (MCMC) Analysis. MCMC examples are covered as a way to find a posterior distribution. Metropolis and Metropolis-Hastings algorithms are discussed, and situations where other algorithms may be more or less effective are presented.

POPs Session I

Slides here.

  • Ines Juvan (Space Research Institute Graz) – PyTranSpot – A tool for combined transit and stellar spot light-curve modeling
  • Anthony Gai (Univ. at Albany) – Bayesian Model Testing of Models for Ellipsoidal Variation on Stars Due to Hot Jupiters
  • Emiliano Jofre (OAC-CONICET) – Searching for planets in southern stars via transit timing variations
  • Sean McCloat (Univ. of North Dakota) – Follow-up Observations of Recently Discovered Hot Jupiters
  • Romina Petrucci (OAC-CONICET) – A search for orbital decay in southern transiting planets
  • Luke Bouma (MIT) – What should we do next with the Transiting Exoplanet Survey Satellite?
  • Akshata Krishnamurthy (MIT) – A precision optical test bench to measure the absolute quantum efficiency of the Transiting Exoplanet Survey Satellite CCD detectors

David Kipping (Columbia) teaches us about using Bayesian Priors for Transits and RVs. Selection, implementation, strengths and weaknesses, and ideal use cases for different types of priors are discussed.

POPs Session II

Slides here.

  • Allen Davis (Yale) – Assessing the information content of spectra using PCA
  • Matteo Pinamonti (Univ. of Trieste) – Searching for planetary signals in Doppler time series: a performance evaluation of tools for periodogram analysis
  • Keara Wright (Univ. of Florida) – Stellar Parameters for FGK MARVELS Targets
  • Richard Hall (Univ. of Cambridge) – Measuring the Effective Pixel Positions of the HARPS3 CCD
  • Sarah Millholland (UCSC) – A Search for Non-Transiting Hot Jupiters with Transiting Super-Earth Companions
  • Tarun Kumar (Thapar Univ.) – Radial Velocity Curves of Polytropic Models of Stars of Polytropic Index N=1.5
  • Fei Dai (MIT) – The K2-ESPRINT Collaboration

Eric Feigelson (Penn State) covers Statistical Approaches to Exoplanetary Science. The talk focuses on time series analysis. Parametric and nonparametric methods are shown for time domain and frequency domain  problems. Code examples and other potentially useful methods are given.