Fairer Search Results

 Ranking, Fairness. 

Fairer Ranking System Diversifies Search Results

Cornell Chronicle

Patricia Waldron,  September 19, 2022

Cornell University researchers Yuta Saito and Thorsten Joachims have developed a fairer ranking system for recommendations that prevents search results from only highlighting a few top hits. Conventional recommender systems try ranking items based on what users want to see, so Saito designed the enhanced ranking system according to the economic principles of "fair division." The researchers used synthetic and real-world data to test the system's feasibility. The system returns viable results that rank items' benefits better than random discovery, does not easily improve items' impact, and does not confer an advantage to any item by switching how it is ranked versus other items in a series of searches. Saito said the framework "can be applied to any type of two-sided ranking system."

Government Authority Falls Due to Algorithm

Regulation 0f algorithms causes Dutch Tax Authority to fail.  An unusual event.  Fairness and impacts. 

The Dutch Tax Authority Was Felled by AI—What Comes Next? European regulation hopes to rein in ill-behaving algorithms by RAHUL RAO     in Spectrum IEEE

Until recently, it wasn’t possible to say that AI had a hand in forcing a government to resign. But that’s precisely what happened in the Netherlands in January 2021, when the incumbent cabinet resigned over the so-called kinderopvangtoeslagaffaire: the childcare benefits affair.

When a family in the Netherlands sought to claim their government childcare allowance, they needed to file a claim with the Dutch tax authority. Those claims passed through the gauntlet of a self-learning algorithm, initially deployed in 2013. In the tax authority’s workflow, the algorithm would first vet claims for signs of fraud, and humans would scrutinize those claims it flagged as high risk.

In reality, the algorithm developed a pattern of falsely labeling claims as fraudulent, and harried civil servants rubber-stamped the fraud labels. So, for years, the tax authority baselessly ordered thousands of families to pay back their claims, pushing many into onerous debt and destroying lives in the process.

“When there is disparate impact, there needs to be societal discussion around this, whether this is fair. We need to define what ‘fair’ is,” says Yong Suk Lee, a professor of technology, economy, and global affairs at the University of Notre Dame, in the United States. “But that process did not exist.”

Postmortems of the affair showed evidence of bias. Many of the victims had lower incomes, and a disproportionate number had ethnic minority or immigrant backgrounds. The model saw not being a Dutch citizen as a risk factor.

“The performance of the model, of the algorithm, needs to be transparent or published by different groups,” says Lee. That includes things like what the model’s accuracy rate is like, he adds.  .... '

Search Algorithm Fairness Adjustments

In particular looking at search results.

Algorithm Improves Fairness of Search Results

Cornell Chronicle
Melanie Lefkowitz
August 17, 2020

Cornell University researchers have developed an algorithm to improve the fairness of online search rankings while retaining their utility or relevance. Unfairness stems from search algorithms prioritizing more popular items, which means that the higher a choice appears in the list, the more likely users are to click on and respond to it, reinforcing one item's popularity while others go unnoticed. When seeking the most relevant items, small variations can cause major exposure disparities, because most people select one of the first few listed items. Cornell's Thorsten Joachims said, "We came up with computational tools that let you specify fairness criteria, as well as the algorithm that will provably enforce them." The FairCo tool allocates approximately equal exposure to equally relevant choices and avoids preference for items that are already highly ranked; this can remedy the innate unfairness in current algorithms.  .... "

Google Fairness Gym

A considerable effort reported on here to experiment with the broad idea of fairness in machine learning, via the notion of a 'gym' to exercise choices and results with varying data.    Article below has quite a bit of  detail on what this is trying to be.

ML-fairness-gym: A Tool for Exploring Long-Term Impacts of Machine Learning Systems
Wednesday, February 5, 2020
Posted by Hansa Srinivasan, Software Engineer, Google Research

Machine learning systems have been increasingly deployed to aid in high-impact decision-making, such as determining criminal sentencing, child welfare assessments, who receives medical attention and many other settings. Understanding whether such systems are fair is crucial, and requires an understanding of models’ short- and long-term effects. Common methods for assessing the fairness of machine learning systems involve evaluating disparities in error metrics on static datasets for various inputs to the system. Indeed, many existing ML fairness toolkits (e.g., AIF360, fairlearn, fairness-indicators, fairness-comparison) provide tools for performing such error-metric based analysis on existing datasets. While this sort of analysis may work for systems in simple environments, there are cases (e.g., systems with active data collection or significant feedback loops) where the context in which the algorithm operates is critical for understanding its impact. In these cases, the fairness of algorithmic decisions ideally would be analyzed with greater consideration for the environmental and temporal context than error metric-based techniques allow. ....  " 

Fairness Indicators for AI Machine Learning

Brought to my attention, dealing with a potential solution for a project at hand.   Good technical and non technical coverage.  The approach is now available in Beta. Examining.

Fairness Indicators: Scalable Infrastructure for Fair ML Systems
Wednesday, December 11, 2019
Posted by Catherina Xu and Tulsee Doshi, Product Managers, Google Research

While industry and academia continue to explore the benefits of using machine learning (ML) to make better products and tackle important problems, algorithms and the datasets on which they are trained also have the ability to reflect or reinforce unfair biases. For example, consistently flagging non-toxic text comments from certain groups as “spam” or “high toxicity” in a moderation system leads to exclusion of those groups from conversation.

In 2018, we shared how Google uses AI to make products more useful, highlighting AI principles that will guide our work moving forward. The second principle, “Avoid creating or reinforcing unfair bias,” outlines our commitment to reduce unjust biases and minimize their impacts on people.

As part of this commitment, at TensorFlow World, we recently released a beta version of Fairness Indicators, a suite of tools that enable regular computation and visualization of fairness metrics for binary and multi-class classification, helping teams take a first step towards identifying unjust impacts. Fairness Indicators can be used to generate metrics for transparency reporting, such as those used for model cards, to help developers make better decisions about how to deploy models responsibly. Because fairness concerns and evaluations differ case by case, we also include in this release an interactive case study with Jigsaw’s Unintended Bias in Toxicity dataset to illustrate how Fairness Indicators can be used to detect and remediate bias in a production machine learning (ML) model, depending on the context in which it is deployed. Fairness Indicators is now available in beta for you to try for your own use cases.  ... "

Prove Your Algorithms are Fair

See some previous work on this,.   Proofs in specific goals and context.

To Build Trust In Artificial Intelligence, IBM Wants Developers To Prove Their Algorithms Are Fair
by Dan Robitzski in Futurism.com

We trust artificial intelligence algorithms with a lot of really important tasks. But they betray us all the time. Algorithmic bias can lead to over-policing in predominately black areas; the automated filters on social media flag activists while allowing hate groups to keep posting unchecked.

As the problems caused by algorithmic bias have bubbled to the surface, experts have proposed all sorts of solutions on how to make artificial intelligence more fair and transparent so that it works for everyone.

These range from subjecting AI developers to third party audits, in which an expert would evaluate their code and source data to make sure the resulting system doesn’t perpetuate society’s biases and prejudices, to developing tests to make sure that an AI algorithm doesn’t treat people differently based on things like race, gender, or socioeconomic class. ... "

Fairness in Decision Making: Deferring to Humans

Intriguing effort.  We worked with systems that ultimately required high level executive agreement.  how might those decisions be trained for fairness?   And consider the inherent risk involved in such approaches.   Uncertainty is always inherent in such methods,  how is that integrated?

Predict Responsibly: Fairness Needed in Algorithmic Decision-Making, U of T Experts Say  in U of T News   by Nina Haikara

David Madras at the University of Toronto (U of T) in Canada believes machine learning algorithms could handle uncertainty better by adding fairness in their decision-making processes. Madras worked with U of T professors Toniann Pitassi and Richard Zemel to develop an algorithmic model that includes fairness. The researchers note in situations where there is a degree of uncertainty, an algorithm must have the option to admit its lack of certainty and defer its decision to a human user. "In order to train up our model, we have to use historical decisions that are made by decision-makers," Zemel says. "The outcomes of those decisions, created by existing decision-makers, can be themselves biased or in a sense incomplete." Madras thinks greater concentration on algorithmic fairness alongside issues of privacy, security, and safety will help make machine learning more conducive to high-stakes applications. ... "