I’m a lifelong Dodgers fan and I waited for 32 years for the team to win another World Series. But during this period of time, the sport has certainly seen much change. With the availability of huge amounts of data, sophisticated computers and advanced analytics, the strategies have become increasingly based on the numbers. It seems that AI (Artificial Intelligence) has dominated the decision making process.
We got an example of this in the crucial Game 6 of the World Series. Tampa Bay Rays manager Kevin Cash took out pitcher Blake Snell from the game, even though he was nearly flawless. Read More
Tag Archives: Bias
Algorithms Are Making Economic Inequality Worse
The risks of algorithmic discrimination and bias have received much attention and scrutiny, and rightly so. Yet there is another more insidious side-effect of our increasingly AI-powered society — the systematic inequality created by the changing nature of work itself. We fear a future where robots take our jobs, but what happens when a significant portion of the workforce ends up in algorithmically managed jobs with little future and few possibilities for advancement?
… How many Uber drivers do you think will ever have the chance to attain a managerial position at the company, let alone run the ride-sharing giant? … There’s a “code ceiling” that prevents career advancement — irrespective of gender or race. Read More
GPT-3’s bigotry is exactly why devs shouldn’t use the internet to train AI
It turns out that a $1 billion investment from Microsoft and unfettered access to a supercomputer wasn’t enough to keep OpenAI’s GPT-3 from being just as bigoted as Tay, the algorithm-based chat bot that became an overnight racist after being exposed to humans on social media. Read More
An extensible, interactive visualization framework to measure gender bias in the news
How we successfully integrated Dash (by Plotly) into our NLP and linguistics research to study women’s portrayal in mainstream Canadian news Read More
Democratization of AI
When company leaders talk about democratizing artificial intelligence (AI), it’s not difficult to imagine what they have in mind. The more people with access to the raw materials of knowledge, tools, and data required to build an AI system, the more innovations that are bound to emerge. Efficiency improves and engagement increases. Faced with a shortage of technical talent? Microsoft, Amazon, and Google have all released premade, drag-and-drop or no-code AI tools that allow people to integrate AI into applications without needing to know how to build machine learning models.
But as companies move toward democratization, a cautionary tale is emerging. Even the most sophisticated AI systems, designed by highly qualified engineers, can fall victim to bias, explainability issues, and other flaws. Read More
Assessing Demographic Bias in Named Entity Recognition
Named Entity Recognition (NER) is often the first step towards automated Knowledge Base (KB) generation from raw text. In this work, we assess the bias in various Named Entity Recognition(NER) systems for English across different demographic groups with synthetically generated corpora. Our analysis reveals that models perform better at identifying names from specific demo-graphic groups across two datasets. We also identify that debiased embeddings do not help in resolving this issue. Finally, we observe that character-based contextualized word representation models such as ELMo results in the least bias across demographics. Our work can shed light on potential biases in automated KB generation due to systematic exclusion of named entities belonging to certain demographics. Read More
Characterizing Implicit Bias in Terms of Optimization Geometry
We study the implicit bias of generic optimization methods, such as mirror descent, natural gradient descent,and steepest descent with respect to different potentials and norms, when optimizing under determined linear regression or separable linear classification problems. We explore the question of whether the specific global minimum (among the many possible global minima) reached by an algorithm can be characterized in terms of the potential or norm of the optimization geometry, and independently of hyperparameter choices such as step-size and momentum. Read More
We Have Already Let The Genie Out of The Bottle
How will we make sure that Artificial Intelligence won’t run amok and will be a force for good?
There are many areas where governance frameworks and international agreements about the use of artificial intelligence (AI) are needed. For example, there is an urgent need for internationally shared rules governing autonomous weapons and the use of facial recognition to target minorities and suppress dissent. Eliminating bias in algorithms for criminal sentencing, credit allocation, social media curation and many other areas should be an essential focus for both research and the spread of best practices. Read More
Why it matters that IBM is getting out of the facial recognition business
The news that IBM will no longer produce facial recognition technology might not sound huge at first. The company’s commitment to opposing this type of racially biased surveillance technology fits into a welcome trend of actions being taken after anti-police brutality protests have swept the nation. Although some are already warning that IBM’s move won’t end the age of facial recognition, others say it’s a significant step in the right direction. Read More
23 sources of data bias for #machinelearning and #deeplearning
In the paper A survey on bias and fairness in machine learning.- the authors outline 23 types of bias in data for machinelearning. The source is good – so below is an actual representation because I found it useful as it is. Read More
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