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As machines increasingly make high-risk decisions, a new lab at Texas A&M aims to reduce bias in artificial intelligence and machine learning.

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The algorithms underpinning artificial intelligence and machine learning increasingly influence our daily lives. They can decide everything from which video were recommended to watch next on YouTube to who should be arrested based on facial recognition software.

But the data used to train these systems often replicate the harmful social biases of the engineers who build them. Eliminating this bias from technology is the focus of Code^Shift, a new data science lab at Texas A&M University that brings together faculty members and researchers from a variety of disciplines across campus.

Its an increasingly critical initiative, said Lab Director Srividya Ramasubramanian, as more of the world becomes automated. Machines, rather than humans, are making many of the decisions around us, including some that are high-risk.

Code^Shift tries to shift our thinking about the world of code or coding in terms of how we can be thinking of data more broadly in terms of equity, social healing, inclusive futures and transformation, said Ramasubramanian, professor of communication in the College of Liberal Arts. A lot of trauma and a lot of violence has been caused, including by media and technologies, and first we need to acknowledge that, and then work toward reparations and a space of healing individually and collectively.

Bias in artificial intelligence can have major impacts. In just one recent example, a man has sued the Detroit Police Department after he was arrested and jailed for shoplifting after being falsely identified by the departments facial recognition technology. The American Civil Liberties Union calls it the first case of its kind in the United States.

Code^Shift will attempt to confront this issue using a collaborative research model that includes Texas A&M experts in social science, data science, engineering and several other disciplines. Ramasubramanian said eight different colleges are represented, and more than 100 people attended the labs virtual launch last month.

Experts will work together on research, grant proposals and raising awareness in the broader public of the issue of bias in machine learning and artificial intelligence. Curriculum may also be developed to educate professionals in the tech industry, such as workshops and short courses on anti-racism literacy, gender studies and other topics that are sometimes not covered in STEM fields.

The labs name references coding, which is foundational to todays digital world. Its also a play on code-switching the way people change the languages they use or how they express themselves in conversation depending on the context.

As an immigrant, Ramasubramanian says shes familiar with living in two worlds. She offers several examples of computer-based biases shes encountered in everyday life, including an experience attempting to wash her hands in an airport bathroom.

Standing at the sink, Ramasubramanian recalls, she held her hands under the faucet. As she moved them back and forth and the taps stayed dry, she realized that the sensors used to turn the water on could not recognize her hands. It was the same case with the soap dispenser.

It was something I never thought much about, but later on I was reading an article about this topic that said many people with darker skin tones were not recognized by many systems, she said.

Similarly, when Ramasubramanian began to work remotely during the COVID-19 pandemic, she noticed that her skin and hair color made her disappear against the virtual Zoom backgrounds. Voice recognition software she attempted to use for dictation could not understand her accent.

The system is treating me as the other and different in many, many ways, she said. And in return, there are serious consequences of who feels excluded, and thats not being captured.

Co-director Lu Tang, an assistant professor in the College of Liberal Arts who examines health disparity in underserved populations, says her research shows that Black patients, for example, must have much more severe symptoms that non-Black patients in order to be assigned certain diagnoses in computer software used in hospitals.

She said this is just one instance of the disparities embedded in technology. Tangs research also focuses on how machine learning algorithms used on social media platforms are more likely to expose people to misinformation about health.

If I inhabit a social media space where a lot of my friends hold certain erroneous attitudes about things like vaccines or COVID-19, I will repeatedly be exposed to the same information without being exposed to different information, she said.

Tang also is interested in what she calls the filter bubble the phenomenon of where an algorithm leads a user on TikTok, YouTube or other platforms based on content theyve watched in the past or what other people with similar viewing behaviors are watching at that moment. Watching just one video containing vaccine misinformation could prompt the algorithm to continue recommending similar videos. Tang said the filter bubble is another added layer that influences the content that people are exposed to.

I think to really understand this society and how we are living today, we as social scientists and humanities scholars need to acknowledge and understand the way computers are influencing the way society is run today, Tang said. I feel like working with computer science engineers is a way for us to combine our strengths to understand a lot of the problems we have in this society.

Computer Science and Engineering Assistant Professor Theodora Chaspari, another co-director of Code^Shift, agrees that minds from different disciplines are needed to design better systems.

To build an inclusive system, she said, engineers need to include representative data from all populations and social groups. This could help facial recognition algorithms better recognize faces of all races, she said, because a system cannot really identify a face until it has seen many, many faces. But engineers may not understand more subtle sources of bias, she said, which is why social and life sciences experts are needed to help with the thoughtful design of more equitable algorithms.

The goal of Code^Shift is to help bridge the gap between systems and people, Chaspari said. The lab will do this by raising awareness through not only research, but education.

Were trying to teach our students about fairness and bias in engineering and artificial intelligence, Chaspari said. Theyre pretty new concepts, but are very important for the new, young engineers who will come in the next years.

So far, Code^Shift has held small group discussion on topics like climate justice, patient justice, gender equity and LGBTQ issues. A recent workshop focused on health equity and the ways in which big data and machine learning can be used to take into account social structures and inequalities.

Ramasubramanian said a full grant proposal to the Texas A&M Institute of Data Science Thematic Data Science Labs Program is also being developed. The labs directors hope to connect with more colleges and make information accessible to more people.

They say collaboration is critical to the initiative. The people who create algorithms often come from small groups, Ramasubramanian said, and are not necessarily collaborating with social scientists. Code^Shift asks for more accountability in how systems are created: who has access to the data, whos deciding how to use it, and how is it being shared?

Texas A&M is home to some of the worlds top data scientists, Ramasubramanian said, making it an important place to have conversations about difficult topics like data equity.

To me, we should also be leaders in thinking about the ethical, social, health and other impacts of data, she said.

To join the Code^Shift mailing list or learn more about collaborating with the lab, contact Ramasubramanian at srivi@tamu.edu.

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Code^Shift Lab Aims To Confront Bias In AI, Machine Learning - Texas A&M Today - Texas A&M University Today

Jonathan Jadali, Founder and CEO of Ascend

The jury is still out on what makes Gen Zers tick, but while the research is still ongoing there is much evidence to suggest that a marketing strategy utilizing machine learning is exponentially more effective with the next generation.

One thing is abundantly clear to every marketer worth his salt; Gen Z customers are "ninja-level" efficient at swatting away regular ads and pop-ups. They are strongly immune to hard sales and obvious sales content.

Despite all the difficulties that marketers are facing in reaching a wide Gen Z audience, Jonathan Jadali, CEO and Founder at Ascend Agency has found great success in leading Gen Z-focused startups to victory in this marketing struggle.

So what makes the typical Gen Z customer tick and how can businesses and startups build a brand that is appealing to them, utilizing cutting edge technologies?

Jadali shares the ways in which he has used a data and machine-learning strategy in getting many of his clients from obscurity to domination of the Gen Z market.

Content, as they say, is king, but the wrong kind of content isnt even fit to be a pawn in this game. To get startups headed in the right direction, Jonathan often helps direct his clients at Ascend Agency on creating the right type of content for the right type of client.

While most brands are focused on putting out well-curated video and image content in a bid to drive engagement on their social media platforms, Jadali advises that this might not be the best way to go if Gen Zers are your target audience.

The ideal Gen Z customer thrives on spontaneous and messy content. As Jadali states, Gen Z customers are all about being realthey connect well with unfiltered and unedited content because it tends to feel less salesy than others.

For instance, a makeup brand is better off posting a video of a makeup session, in front of a cluttered vanity table, than a photoshoot with a perfectly made-up face.

This is important to keep in mind when implementing any machine learning into your marketing strategy. Whether you are creating a chat bot, or building a data-driven marketing campaign - its important that your system learns to be imperfect.

When AI or Machine Learning is used in marketing, sometimes it can come off as, well, robotic. Gen Z will be an important moment for machine learning marketing as it will help us get closer to contextual AI - machines that more accurately predict and reflect human behavior.

Gen Z wants to see the messiness of life and its process reflected in your content. Brands that do this, are the brands that they are drawn to and often build loyalty for.

How does it look? How effective is it? How satisfying is your service? All these are valid marketing questions and things that in the past had been asked by your millennial customer base.

According to Jadali, these questions do not matter nearly as much to a Gen Z audience.

Clearly, customers want products that work and businesses that deliver, but with a Gen Z audience, that doesnt seem to be the right way to lead in marketing to them.

Having worked with both Fortune 500 companies and smaller startups alike in the last 3 years since Ascend Agency launched, Jadali is fairly certain that Gen Z customers are way more attracted to how your business makes them feel.

This is where machine learning can really come in handy. Understanding your customers' moods and habits can help you tap into what makes them feel great about themselves and the products in their lives.

Gen Z customers are tired of hearing about how amazing your product is, businesses have been hyping up their products for as long as businesses have existed and Gen Zers arent having any more of it. In Jonathans words, Sell experiences, not products, and your products will head out of your door as well.

According to Mention, 25% of what you sell is your product. The additional 75% is the intangible feeling that comes with said product.

What dominant feeling do you want to evoke with your content? A question that is popularly asked at the Ascend Agency office, is one that has helped brands build consistency in their content style and delivery and that has brought the Gen Z customers in their droves.

This question can be answered through aggregated customer data that helps you better understand the emotions from brands that they also engage with.

Red Bull is a great example of a brand that utilizes data and machine learning in this manner. Their video content covers high-risk sports, like Skydiving, Bungee jumping, etc. From customer data processed by predictive analytics and machine learning systems, the dominant feeling Red Bull chose to evoke is one of courage and strength.

What is yours, Happiness, Reflection, or Prestige? The sooner you can answer that, the sooner you can get your gen Z audience to really pay attention. Machine learning can help you answer this question faster and more accurately.

Did you know that once an Influencers followership crosses the 100k mark, their engagement drops drastically? When did you last get an Instagram reply from Selena Gomez or Christiano Ronaldo? Never I presume. I will get back to this point in a bit.

While Guest Posting and proper ad placement might still work rather well for Millennials, Social media is clearly the major frontier for Gen Zers. This is why Influencer Marketing has risen to the fore in the last 6 years.

However, nothing is more important to this generation than being seen and heard. This is why Gen Z customers rate a brands authenticity by how well the brands engage with them online.

If a customer posts a tweet asking you for information or laying down a complaint, the first thing to do is to respond publicly before directing to their inbox as opposed to solely responding to them privately. If they send in a review, respond and thank them. Call them by name, engage with them personally in a way that doesnt feel rehearsed, says Jadali.

It goes without saying that brands should be more intentional with engaging their Gen Z audience personally. However, this is hard to scale.

Machine learning is helping brands go beyond the typical automated response we often see in DM and SMS replies. As this technology becomes more advanced, you will be able to engage with hundreds of thousands of customers at once at a deeply personal level.

Micro-influencers drive 60% higher engagement levels and 22.2% more weekly conversions coupled with the fact that they are considerably cheaper. However, their secret sauce is the fact that they are still able to engage with their followers directly far more than celebrities like Cristiano Ronaldo or Selena Gomez ever can.

Soon, machine learning will allow for this type of personal engagement at scale. It will also allow for small brands and businesses to authentically engage with customers without having to spend hours of their day on replies and comments.

As Jadali explains, The Gen Z audience is sensitive, intuitive and versatile, reaching them is not rocket science, it is not science at all, it is an art. It is something that anyone can master, wield and utilize.

Gen Z will help push Machine Learning to become more human, more perfectly imperfect in its responses, and move us closer to contextual AI in marketing and online content.

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Is Machine Learning The Key To Unlocking Gen Z Engagement? A Discussion With Jonathan Jadali Of Ascend - Forbes

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Global Machine Learning & Big Data Analytics Education Market Size will be Expanded and Reach Millions $ by 2024 KSU | The Sentinel Newspaper -...

This article was originally published here

Heliyon. 2021 Jun;7(6):e07371. doi: 10.1016/j.heliyon.2021.e07371. Epub 2021 Jun 23.

ABSTRACT

Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning. The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.

PMID:34179541 | PMC:PMC8219638 | DOI:10.1016/j.heliyon.2021.e07371

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The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review - DocWire News

Crude oil production and natural gas withdrawals in the United States have lessened the country's dependence on foreign oil and provided financial relief to U.S. consumers, but have also raised longstanding concerns about environmental damage, such as groundwater contamination.

A researcher in Syracuse University's College of Arts and Sciences, and a team of scientists from Penn State, have developed a new machine learning technique to holistically assess water quality data in order to detect groundwater samples likely impacted by recent methane leakage during oil and gas production. Using that model, the team concluded that unconventional drilling methods like hydraulic fracturing - or hydrofracking - do not necessarily incur more environmental problems than conventional oil and gas drilling.

The two common ways to extract oil and gas in the U.S. are through conventional and unconventional methods. Conventional oil and gas are pumped from easily accessed sources using natural pressure. Conversely, unconventional oil and gas are acquired from hard-to-reach sources through a combination of horizontal drilling and hydraulic fracturing. Hydrofracking extracts natural gas, petroleum and brine from bedrock formations by injecting a mixture of sand, chemicals and water. By drilling into the earth and directing the high-pressure mixture into rock, the gas inside releases and flows out to the head of a well.

Tao Wen, assistant professor of earth and environmental sciences (EES) at Syracuse, recently led a study comparing data from different states to see which method might result in greater contamination of groundwater. They specifically tested levels of methane, which is the primary component of natural gas.

The team selected four U.S. states located in important shale zones to target for their study: Pennsylvania, Colorado, Texas and New York. One of those states - New York - banned the practice of hydrofracking in 2015 following a review by the NYS Department of Health which found significant uncertainties about health, including increased water and air pollution.

Wen and his colleagues compiled a large groundwater chemistry dataset from multiple sources including federal agency reports, journal articles, and oil and gas companies. The majority of tested water samples in their study were collected from domestic water wells. Although methane itself is not toxic, Wen says that methane contamination detected in shallow groundwater could be a risk to the relevant homeowner as it could be an explosion hazard, could increase the level of other toxic chemical species like manganese and arsenic, and would contribute to global warming as methane is a greenhouse gas.

Their model used sophisticated algorithms to analyze almost all of the retained geochemistry data in order to predict if a given groundwater sample was negatively impacted by recent oil and gas drilling.

The data comparison showed that methane contamination cases in New York - a state without unconventional drilling but with a high volume of conventional drilling - were similar to that of Pennsylvania - a state with a high volume of unconventional drilling. Wen says this suggests that unconventional drilling methods like fracking do not necessarily lead to more environmental problems than conventional drilling, although this result might be alternatively explained by the different sizes of groundwater chemistry datasets compiled for these two states.

The model also detected a higher rate of methane contamination cases in Pennsylvania than in Colorado and Texas. Wen says this difference could be attributed to different practices when drillers build/drill the oil and gas wells in different states. According to previous research, most of the methane released into the environment from gas wells in the U.S. occurs because the cement that seals the well is not completed along the full lengths of the production casing. However, no data exists to conclude if drillers in those three states use different technology. Wen says this requires further study and review of the drilling data if they become available.

According to Wen, their machine learning model proved to be effective in detecting groundwater contamination, and by applying it to other states/counties with ongoing or planned oil and gas production it will be an important resource for determining the safest methods of gas and oil drilling.

Reference:Wen T, Liu M, Woda J, Zheng G, Brantley SL. Detecting anomalous methane in groundwater within hydrocarbon production areas across the United States.Water Res. 2021;200:117236. doi:10.1016/j.watres.2021.117236

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

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Environmental Impact of Oil and Gas Drilling Explored With Novel Machine Learning Method - Technology Networks