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AI-powered tool aims to help reduce bias and racially charged language on websites

22% of more than 500,000 business websites contain some form of racial and gender bias, according to UserWay.

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22% of more than 500,000 business websites contain some form of racial and gender bias, according to UserWay.

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Website accessibility tech provider UserWay has released an AI-powered tool designed to help organizations ensure their websites are free from discriminatory, biased, and racially charged language.

The tool, Content Moderator, flags content for review, and nothing is deleted or removed without approval from site administrators, according to UserWay.

UserWay’s customers are using its AI-powered accessibility widget, an advanced AI-based compliance-as-a-service (CaaS) technology that ensures brands provide an accessible digital experience that meets strict governmental and ADA regulations, the company said.

“Focusing on digital racism and bias is long past due, and our team is eager to contribute to the conversation positively,” UserWay founder and CEO Allon Mason said in a statement.

In June, Google announced that it would be reevaluating what it considers acceptable language, Mason noted. So far, Google has changed terms including “blacklist” to “blocked list,” “whitelist” to “allowed list,” and “master-slave” to “primary/secondary,” among others, he said.

“That was the spark that triggered us to build this tool. At the time, we were enhancing our AI-powered capabilities that supply [alternate] text descriptions of images for screen readers,” Mason said. “We realized that if word choices can make our customers’ digital content inaccessible even without intending to, UserWay should help.”

The goal of the Content Moderator isn’t to censor or silence, he added, but to make web teams aware of problematic language in user-generated content or in content they may have overlooked.

SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic) 

Discriminatory language on websites is pervasive

Before launching Content Moderator, UserWay ran its rule engine across more than 500,000 websites. The findings were concerning, the company said.

Some 22% of the sites scanned contained some form of biased, racially charged, or offensive language, UserWay said. Of those:

  • 52% were sites with instances of racial bias
  • 24% were sites with instances of gender bias
  • 12% were sites with instances of age bias
  • 5% were sites with racial slurs
  • 3% were sites with disability bias

Words that the tool most often flagged for gender bias included “chairman,” “fireman,” “mankind,” “forefather,” and “man-made,” UserWay said.

Many of these terms have only recently been understood to be divisive and prejudicial. It is an enormous task for most site owners to keep track of the latest consensus around culturally sensitive terms, the company noted. The tool aims to make this task simple, centralized, and scalable, UserWay said.

SEE:  Gender Decoder and blind resumes: How to remove bias in your hiring process  (TechRepublic)

How Content Moderator works

Historically, content moderation software using AI to detect racial bias and divisive speech has been site-specific, expensive, and available only within large social media platforms, the company maintained. A website owner can drop in the UserWay widget and will be alerted to divisive or offensive language as it appears, in real time. The widget works in three steps: 

  • Scan: Content Moderator scans all the content on a website, both static and dynamic.
  • Flag: The tool then flags words and phrases that may inadvertently promote stereotypes or prejudice, including text that could be considered racist, sexist, anti-Semitic, homophobic, xenophobic, violent, intolerant, or otherwise offensive.
  • Review: Site administrators review the suggestions and choose the ones they would like to accept. They can also edit the suggestions to flow with the site’s content or recommend alternative replacements that are then fed back into UserWay’s AI.

More inclusive speech is needed now

In the past few weeks, many legacy brands such as Aunt Jemima, Uncle Ben’s, and Eskimo Pie, among others have yielded to mounting pressure from consumers to rid products of racial and ethnic stereotypes. Technology companies have likewise been reevaluating the usage of racialized words like “blacklist” and “whitelist” in favor of more inclusive language

But brand integrity isn’t the sole issue. Civil rights advocates, led by the Anti-Defamation League (ADL), have increased pressure to ensure websites are carefully moderated, and recent calls for repeal of Section 230 of the US Communications Decency Act may expose online publishers to future legal action for defamation based on opinions or reviews created by platform users, according to UserWay.

In tandem with UserWay’s Accessibility Widget, Content Moderator helps organizations mitigate the legal risk of both ADA- and ADL-related violations, the company said

“We all know a list of words that are mocking (to put it mildly) of a variety of racial groups, or a variety of religious groups, or other political or gender persuasions,” UserWay quoted Israel W. Charny, Israeli psychologist, genocide scholar, and executive director of the Institute on the Holocaust and Genocide in Jerusalem, as saying. “UserWay’s … tool flags these words and allows you to change them, an act of voluntary editing with cultural sensitivity. Giving options for improvement reduces the onus of the coerciveness that some people are feeling.”

In the same way that HTML code is remediated, Content Moderator can help users pinpoint and update word choices on their site, Mason said.

“While Google and Apple are approaching the issue as a simple search-and-replace, UserWay looks deeper into the problem of bias,” he said.

The tool looks to detect verbalization patterns that consistently and routinely marginalize and disempower specific cohorts, he said. Its dictionary is frequently updated to align with cultural and social changes.

A content owner can choose to agree, modify, or ignore the Content Moderator’s suggestions, Mason added.

“We intend to empower users by making them aware of the content that exists on their site–especially legacy and user-generated text that may not reflect their brand values,” he said. “More importantly, we hope that by removing blatantly and subtly offensive content, we can help these sites become barrier-free and inviting for all users.”

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Source: https://www.techrepublic.com/article/ai-powered-tool-aims-to-help-reduce-bias-and-racially-charged-language-on-websites/#ftag=RSS56d97e7

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How does it know?! Some beginner chatbot tech for newbies.

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Wouter S. Sligter

Most people will know by now what a chatbot or conversational AI is. But how does one design and build an intelligent chatbot? Let’s investigate some essential concepts in bot design: intents, context, flows and pages.

I like using Google’s Dialogflow platform for my intelligent assistants. Dialogflow has a very accurate NLP engine at a cost structure that is extremely competitive. In Dialogflow there are roughly two ways to build the bot tech. One is through intents and context, the other is by means of flows and pages. Both of these design approaches have their own version of Dialogflow: “ES” and “CX”.

Dialogflow ES is the older version of the Dialogflow platform which works with intents, context and entities. Slot filling and fulfillment also help manage the conversation flow. Here are Google’s docs on these concepts: https://cloud.google.com/dialogflow/es/docs/concepts

Context is what distinguishes ES from CX. It’s a way to understand where the conversation is headed. Here’s a diagram that may help understand how context works. Each phrase that you type triggers an intent in Dialogflow. Each response by the bot happens after your message has triggered the most likely intent. It’s Dialogflow’s NLP engine that decides which intent best matches your message.

Wouter Sligter, 2020

What’s funny is that even though you typed ‘yes’ in exactly the same way twice, the bot gave you different answers. There are two intents that have been programmed to respond to ‘yes’, but only one of them is selected. This is how we control the flow of a conversation by using context in Dialogflow ES.

Unfortunately the way we program context into a bot on Dialogflow ES is not supported by any visual tools like the diagram above. Instead we need to type this context in each intent without seeing the connection to other intents. This makes the creation of complex bots quite tedious and that’s why we map out the design of our bots in other tools before we start building in ES.

The newer Dialogflow CX allows for a more advanced way of managing the conversation. By adding flows and pages as additional control tools we can now visualize and control conversations easily within the CX platform.

source: https://cloud.google.com/dialogflow/cx/docs/basics

This entire diagram is a ‘flow’ and the blue blocks are ‘pages’. This visualization shows how we create bots in Dialogflow CX. It’s immediately clear how the different pages are related and how the user will move between parts of the conversation. Visuals like this are completely absent in Dialogflow ES.

It then makes sense to use different flows for different conversation paths. A possible distinction in flows might be “ordering” (as seen here), “FAQs” and “promotions”. Structuring bots through flows and pages is a great way to handle complex bots and the visual UI in CX makes it even better.

At the time of writing (October 2020) Dialogflow CX only supports English NLP and its pricing model is surprisingly steep compared to ES. But bots are becoming critical tech for an increasing number of companies and the cost reductions and quality of conversations are enormous. Building and managing bots is in many cases an ongoing task rather than a single, rounded-off project. For these reasons it makes total sense to invest in a tool that can handle increasing complexity in an easy-to-use UI such as Dialogflow CX.

This article aims to give insight into the tech behind bot creation and Dialogflow is used merely as an example. To understand how I can help you build or manage your conversational assistant on the platform of your choice, please contact me on LinkedIn.

Source: https://chatbotslife.com/how-does-it-know-some-beginner-chatbot-tech-for-newbies-fa75ff59651f?source=rss—-a49517e4c30b—4

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Who is chatbot Eliza?

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Discover the whole story.

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Frédéric Pierron

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Its creator, Joseph Weizenbaum was a researcher at the famous Artificial Intelligence Laboratory of the MIT (Massachusetts Institute of Technology). His goal was to enable a conversation between a computer and a human user. More precisely, the program simulates a conversation with a Rogérian psychoanalyst, whose method consists in reformulating the patient’s words to let him explore his thoughts himself.

Joseph Weizenbaum (Professor emeritus of computer science at MIT). Location: Balcony of his apartment in Berlin, Germany. By Ulrich Hansen, Germany (Journalist) / Wikipedia.

The program was rather rudimentary at the time. It consists in recognizing key words or expressions and displaying in return questions constructed from these key words. When the program does not have an answer available, it displays a “I understand” that is quite effective, albeit laconic.

Weizenbaum explains that his primary intention was to show the superficiality of communication between a human and a machine. He was very surprised when he realized that many users were getting caught up in the game, completely forgetting that the program was without real intelligence and devoid of any feelings and emotions. He even said that his secretary would discreetly consult Eliza to solve his personal problems, forcing the researcher to unplug the program.

Conversing with a computer thinking it is a human being is one of the criteria of Turing’s famous test. Artificial intelligence is said to exist when a human cannot discern whether or not the interlocutor is human. Eliza, in this sense, passes the test brilliantly according to its users.
Eliza thus opened the way (or the voice!) to what has been called chatbots, an abbreviation of chatterbot, itself an abbreviation of chatter robot, literally “talking robot”.

Source: https://chatbotslife.com/who-is-chatbot-eliza-bfeef79df804?source=rss—-a49517e4c30b—4

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FermiNet: Quantum Physics and Chemistry from First Principles

Weve developed a new neural network architecture, the Fermionic Neural Network or FermiNet, which is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.

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Unfortunately, 0.5% error still isn’t enough to be useful to the working chemist. The energy in molecular bonds is just a tiny fraction of the total energy of a system, and correctly predicting whether a molecule is stable can often depend on just 0.001% of the total energy of a system, or about 0.2% of the remaining “correlation” energy. For instance, while the total energy of the electrons in a butadiene molecule is almost 100,000 kilocalories per mole, the difference in energy between different possible shapes of the molecule is just 1 kilocalorie per mole. That means that if you want to correctly predict butadiene’s natural shape, then the same level of precision is needed as measuring the width of a football field down to the millimeter.

With the advent of digital computing after World War II, scientists developed a whole menagerie of computational methods that went beyond this mean field description of electrons. While these methods come in a bewildering alphabet soup of abbreviations, they all generally fall somewhere on an axis that trades off accuracy with efficiency. At one extreme, there are methods that are essentially exact, but scale worse than exponentially with the number of electrons, making them impractical for all but the smallest molecules. At the other extreme are methods that scale linearly, but are not very accurate. These computational methods have had an enormous impact on the practice of chemistry – the 1998 Nobel Prize in chemistry was awarded to the originators of many of these algorithms.

Fermionic Neural Networks

Despite the breadth of existing computational quantum mechanical tools, we felt a new method was needed to address the problem of efficient representation. There’s a reason that the largest quantum chemical calculations only run into the tens of thousands of electrons for even the most approximate methods, while classical chemical calculation techniques like molecular dynamics can handle millions of atoms. The state of a classical system can be described easily – we just have to track the position and momentum of each particle. Representing the state of a quantum system is far more challenging. A probability has to be assigned to every possible configuration of electron positions. This is encoded in the wavefunction, which assigns a positive or negative number to every configuration of electrons, and the wavefunction squared gives the probability of finding the system in that configuration. The space of all possible configurations is enormous – if you tried to represent it as a grid with 100 points along each dimension, then the number of possible electron configurations for the silicon atom would be larger than the number of atoms in the universe!

This is exactly where we thought deep neural networks could help. In the last several years, there have been huge advances in representing complex, high-dimensional probability distributions with neural networks. We now know how to train these networks efficiently and scalably. We surmised that, given these networks have already proven their mettle at fitting high-dimensional functions in artificial intelligence problems, maybe they could be used to represent quantum wavefunctions as well. We were not the first people to think of this – researchers such as Giuseppe Carleo and Matthias Troyer and others have shown how modern deep learning could be used for solving idealised quantum problems. We wanted to use deep neural networks to tackle more realistic problems in chemistry and condensed matter physics, and that meant including electrons in our calculations.

There is just one wrinkle when dealing with electrons. Electrons must obey the Pauli exclusion principle, which means that they can’t be in the same space at the same time. This is because electrons are a type of particle known as fermions, which include the building blocks of most matter – protons, neutrons, quarks, neutrinos, etc. Their wavefunction must be antisymmetric – if you swap the position of two electrons, the wavefunction gets multiplied by -1. That means that if two electrons are on top of each other, the wavefunction (and the probability of that configuration) will be zero.

This meant we had to develop a new type of neural network that was antisymmetric with respect to its inputs, which we have dubbed the Fermionic Neural Network, or FermiNet. In most quantum chemistry methods, antisymmetry is introduced using a function called the determinant. The determinant of a matrix has the property that if you swap two rows, the output gets multiplied by -1, just like a wavefunction for fermions. So you can take a bunch of single-electron functions, evaluate them for every electron in your system, and pack all of the results into one matrix. The determinant of that matrix is then a properly antisymmetric wavefunction. The major limitation of this approach is that the resulting function – known as a Slater determinant – is not very general. Wavefunctions of real systems are usually far more complicated. The typical way to improve on this is to take a large linear combination of Slater determinants – sometimes millions or more – and add some simple corrections based on pairs of electrons. Even then, this may not be enough to accurately compute energies.

Source: https://deepmind.com/blog/article/FermiNet

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