Data Science graduates have endless opportunities as long as data abounds in the digital universe. PHOTOS FROM FACEBOOK/MAPUA MATHEMATICS DEPARTMENT

DATA science has been called the "sexiest job of the 21st century," and experts deem it will continue to be sexy well into the future.

Since data is practically used everywhere, it has become one of the most prized commodities today, and data science, one of the fastest-growing fields. Large and well-known companies use data to develop business strategies, create or improve products, understand customers, finalize transactions, and gain profit.

Dr. Mylen Aala-Capuno, chairman of the Mapua University Department of Mathematics, said with data science, organizations gain relevant insights to formulate accurate decisions and corresponding business actions. This wealth of information allows them to optimize their operations and services.

"Data science is an interdisciplinary field for the study of data to extract meaningful insights that will lead to effective decisions. It combines the principles and techniques of mathematics, statistics, and computer science," she said in a press statement.

Many people may not be aware, but data science is not exclusive to tech and finance giants; it is widely used across various industries. In fact, ordinary Filipinos experience its application and conveniences in everyday life.

Whether browsing for content on video or audio streaming sites, searching for new business connections on professional networks, buying items online or looking up lost long friends on social media, the breadth and potential of data science are limitless.

"With data growing at an astronomical rate, the demand for data scientists is also growing, and forecasts show that this demand will grow to 36 percent in the next 10 to 15 years. Being a data scientist is a very lucrative job and is usually in the top 3 highest-paying jobs today," Aala-Capuno added in the statement.

Data science professor Edgar Adina added that the career options of data science graduates are much like the data they explore and analyze boundless.

"Data Science graduates have endless opportunities as long as data abounds in the digital universe. This includes data manager, data architect, data engineer, business analyst, machine learning scientist and engineer, statistician, data modeler, marketing analyst, fraud investigator, business intelligence developer, including a position in the academe," Adina said.

Although the field seems highly technical, Aala-Capuno said anyone passionate about learning could pursue data science since the analytical skills highly valued in the specialization could be learned and developed. Nonetheless, having basic knowledge of mathematics, statistics, and computer programming is also a good foundation for aspiring data science professionals.

While superior mathematical skills could be an advantage, these are not a requirement since the needed competencies will be taught and honed during the program, the university said.

Mapua University said it has been raising Filipino youth to become proficient in the field as its data science program is known for its highly personalized mentorship that empowers students to learn beyond theories.

The three-year program exposes students to numerous training sessions with real-life data using industry-accepted programming languages like Python, R, and software Power BI and MatLab. All courses are technology-integrated so that students can have multiple learning platforms and modalities.

Adina said students will master essential skills like data management, data visualization, data mining and modeling, machine learning, and deep learning, including artificial intelligence and natural language processing. They will also have apprenticeship programs with Mapua's industry partners and job placement opportunities after graduation.

In today's fast-changing world, a career in data science is not only lucrative; it is future-ready. The field's unlimited opportunities and relevance to everyday life assure graduates of professional growth and stability decades after they receive their degrees.

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Right course for 'sexiest job of the 21st century' - The Manila Times

global Agricultural Robots Market size was USD 3.38 billion in 2022, and is expected to reach a value of USD 21.46 billion in 2032, and CAGR of 22.8%

In response to the growing global food demand, farmers are embracing advanced technologies that can enhance productivity and improve the quality of their produce. Agricultural robots play a crucial role in optimizing farming operations, including tasks like planting, harvesting, weeding, and monitoring crop health. By providing precise and timely information on crop health, soil moisture, and environmental conditions, agricultural robots support the need for precision agriculture, thereby driving their adoption.

Addressing the labor shortage in the agriculture sector is another key driver for the widespread use of agricultural robots. As food consumption rises along with the global population, labor scarcity becomes a more pressing issue. By automating tasks such as planting and harvesting, agricultural robots enable farmers to increase their productivity and reduce their reliance on manual labor.

Soil degradation and climate change are significant challenges faced by the agricultural industry. Agricultural robots can assist farmers in better managing their resources and minimizing environmental impact by providing accurate data on soil moisture and nutrient levels. Furthermore, the use of harmful chemicals and pesticides, which can negatively affect soil health and biodiversity, can be reduced through the adoption of agricultural robots.

Get Free Sample PDF (To Understand the Complete Structure of this Report [Summary + TOC]) @ https://www.reportsanddata.com/download-free-sample/2419

Segments Covered in the Report

Unmanned Aerial Vehicles: This segment comprises agricultural robots that are in the form of drones or UAVs. These aerial vehicles are equipped with advanced sensors and imaging technologies to monitor crops, collect data, and assist in crop management.

Milking Robots: Milking robots are designed specifically for dairy management. These robots automate the milking process, ensuring efficient and precise milking of dairy cows while minimizing human labor.

Driverless Tractors: Driverless or autonomous tractors are a key type of agricultural robot used in field farming. These tractors are equipped with navigation systems and advanced technologies to perform tasks such as plowing, seeding, and fertilizing without the need for human operators.

Automated Harvesting Systems: This category includes agricultural robots that are designed for harvesting crops. These robots are capable of identifying ripe crops, picking them, and sorting them based on predetermined criteria, thereby streamlining the harvesting process.

Others: This category encompasses various other types of agricultural robots that are used for specific purposes, such as weed control, pest management, or monitoring crop health.

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Strategic development:

Deere & Company made an announcement in 2021 about their acquisition of Bear Flag Robotics, a startup based in California. Bear Flag Robotics specializes in the development of autonomous driving technology for agricultural tractors. This acquisition will enable Deere & Company to strengthen its autonomous driving capabilities and improve the efficiency and productivity of its tractors.

In 2020, Trimble Inc. completed the acquisition of the assets of Kozalak Technology, a company based in Turkey that focuses on developing precision agriculture technologies. This strategic move by Trimble Inc. aimed to expand their range of precision agriculture solutions and enhance their position in the agricultural robots market.

AGCO Corporation, in 2020, announced a strategic partnership with Robert Bosch GmbH. The collaboration aimed to jointly develop and market smart farming solutions for the agricultural industry. AGCO Corporation's expertise in agricultural machinery, combined with Bosch's proficiency in automation and digitalization, was expected to drive the advancement and adoption of innovative technologies in the field of agriculture.

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Competitive Landscape:

AGCO Corporation Delaval Inc. Deere & Company Lely Holding S.a.r.l. CNH Industrial N.V. Yamaha Motor Co., Ltd. Trimble Inc. Kubota Corporation FANUC Corporation Robert Bosch GmbH

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Agricultural Robots Market to Reach USD 21.46 Billion by 2032, Driven by Population Growth and Agricultural - EIN News

By Todd Feathers, The Markup

This story was copublished with Chalkbeat, a nonprofit news organization covering public education. Sign up for its newsletters here.

Last summer, administrators at Bradford High School in Kenosha, Wis., met as they do every year to plan for the incoming class of ninth graders. From a roster of hundreds of middle schoolers, assistant principal Matt Brown and his staff made a list of 30 to 40 students who they suspected might struggle the most to graduate.

Over the course of the summer break, Brown and his team went down the list and visited each childs home. The staff brought T-shirts for the students, introduced themselves to parents, left behind their contact information and, they hoped, a positive first impression.

Its like, Hey, we want to hook you up with some Bradford gear. Youre gonna be part of a Bradford family now,' Brown said. Its kind of coming out from that standpoint of, Hey, were here to support you, not necessarily, Hey, your kid really messed up last year because we dont want parents to feel like youre already labeling their kid as somebody thats a troublemaker.

But in most cases, the students on Bradfords list for summer visits land there because of a label high risk assigned to them by a racially inequitable algorithm built by the state of Wisconsin, one that frequently raises false alarms.

Since 2012, Wisconsin school administrators like Brown have received their first impression of new students from the Dropout Early Warning System (DEWS), an ensemble of machine learning algorithms that use historical data such as students test scores, disciplinary records, free or reduced lunch-price status, and race to predict how likely each sixth through ninth grader in the state is to graduate from high school on time.

Twice a year, schools receive a list of their enrolled students with DEWS color-coded prediction next to each name: green for low risk, yellow for moderate risk, or red for high risk of dropping out.

Education officials once held up DEWS as a key tool in their fight against the states graduation gap. While 94 percent of White students graduated on time last year, only 82 percent of Hispanic and 71 percent of Black students completed high school in four years. DEWS was intended to put personalized predictions in the hands of educators early enough that they could intervene before a child showed obvious signs of falling off track.

But after a decade of use and millions of predictions, The Markup has found that DEWS may be incorrectly and negatively influencing how educators perceive students, particularly students of color. And a forthcoming academic study from researchers based out of the University of California, Berkeley, who shared data and prepublication findings with The Markup, has concluded that DEWS has failed at its primary goal: improving graduation rates for the students it labels high risk.

An internal Department of Public Instruction (DPI) equity analysis conducted in 2021 found that DEWS generated false alarms about Black and Hispanic students not graduating on time at a significantly greater rate than it did for their White classmates. The algorithms false alarm rate how frequently a student it predicted wouldnt graduate on time actually did graduate on time was 42 percentage points higher for Black students than White students, according to a DPI presentation summarizing the analysis, which we obtained through a public records request. The false alarm rate was 18 percentage points higher for Hispanic students than White students.

DPI has not told school officials who use DEWS about the findings nor does it appear to have altered the algorithms in the nearly two years since it concluded DEWS was unfair.

The DPI presentation summarizing the equity analysis we reviewed did not include the underlying false alarm rates for Black, Hispanic, and White students that DPI used to make its calculations. It also did not include results for students of other races. The department declined to answer questions about the analysis and, in response to a subsequent public records request, DPI said it had no documentation of the equity analysis results beyond the presentation. (A video of the presentation can be seen here.)

A separate DPI validation test of DEWS accuracy in March 2021 shows it was wrong nearly three quarters of the time it predicted a student wouldnt graduate on time.

Students we interviewed were surprised to learn DEWS existed and told The Markup they were concerned that an algorithm was using their race to predict their future and label them high risk. It makes the students of color feel like theyre separated like they automatically have less, said Christopher Lyons, a Black student who graduated from Bradford High School in 2022.

Wisconsin DPI spokesperson Abigail Swetz declined to answer questions about DEWS but provided a brief emailed statement.

Is DEWS racist? Swetz wrote. No, the data analysis isnt racist. Its math that reflects our systems. The reality is that we live in a white supremacist society, and the education system is systemically racist. That is why the DPI needs tools like DEWS and is why we are committed to educational equity.

In response to our findings and further questions, Swetz wrote, You have a fundamental misunderstanding of how this system works. We stand by our previous response. She did not explain what that fundamental misunderstanding was.

To piece together how DEWS has affected the students it has judged, The Markup examined unpublished DPI research, analyzed 10 years of district-level DEWS data, interviewed students and school officials, and collected survey responses from 80 of the states more than 400 districts about their use of the predictions.

Our investigation shows that many Wisconsin districts use DEWS 38 percent of those that responded to our survey and that the algorithms technical failings have been compounded by a lack of training for educators.

DEWS is a voluntary program, and DPI encourages educators to use the predictions in combination with other local data about students to make decisions. The agency does not track whether or how schools use the predictions. Principals, superintendents, and other administrators told The Markup they received little or no explanation of how DEWS calculates its predictions or how to translate a label like high risk into the appropriate intervention.

In districts like Kenosha, students of color dont need data to understand the consequences of being judged by biased systems. In 2020, the city grabbed national headlines following the police shooting of Jacob Blake. And earlier this year, the family of a 12-year-old Black student sued the Kenosha Unified School District after an off-duty police officer working security placed her in a chokehold in the lunchroom of her school.

In 2018, the year Lyons entered Bradford High School, a teacher there was filmed repeatedly using a racial slur in front of students. That year, DEWS labeled 43 percent of Black ninth graders in Kenosha high risk, compared to 11 percent of White ninth graders.

By that point, Lyons said hed already lost motivation academically. It kind of felt like we werent expected to do much, he said. It felt like they knew that we were just destined to fail.

Then something unexpected happened his sophomore year: The COVID-19 pandemic hit, classes went virtual, and, as he put it, his grades skyrocketed from a 2.9 GPA prepandemic to a 3.8 ;GPA after the switch to remote learning. What for many students was a disorienting interruption to their education was for Lyons a reprieve that allowed him to focus. I didnt have that social pressure of, like, the teachers around me or the administration around me, he said. It was just me, the computer, whoever I was talking to.

Last year, Lyons began his freshman year at Carthage College in Kenosha on a full-ride scholarship. His journey illustrates the quirks in personality, learning style, and environment that, some experts say, make it counterproductive to predict an individual students future based on a population-level analysis of statistically similar students.

Nonetheless, early warning systems that use machine learning to predict student outcomes are common in K-12 and higher education. At least eight state public education agencies provide algorithmic early warning systems or are currently building them for future use, according to a Markup survey of all 50 states. Four states did not respond. Montana was the only state besides Wisconsin that said it had examined how its early warning system performed across different racial groups. Montana Office of Public Instruction spokesperson Brian OLeary said that his states equity study was not yet finished.

At the beginning of and midway through each year, DEWS calculates how likely each incoming sixth- through ninth-grade student is to graduate from high school on time on a scale of 0 to 100. A score of 90 indicates that students with similar academic, behavioral, and demographic features have graduated on time 90 percent of the time in the past. Any student whose DEWS score (plus margin of error) is below 78.5 is labeled high risk of not graduating on time.

To make it easier for educators to understand the predictions, DPI translates DEWS scores into a simple, color-coded format. Next to every student's name in the DEWS tab of the statewide information system is a label showing their score and a green "low," yellow "moderate," or red "high" risk designation.

During the 202021 academic year, more than 32,000 students 15 percent of the state's sixth through ninth graders were labeled "high risk."

Examples of how students' DEWS predictions are displayed in the statewide information system. (Credit: Wisconsin Department of Public Instruction DEWS Data Brief)

Experts say the system is designed in ways that may inadvertently bias educators' opinions of students and misdirect scarce school resources. Of particular concern is how heavily DEWS draws on factors like race, disability, and family wealth, which are likely to encode systemic discrimination and which neither the school nor student can change. Other data points fed into DEWS, like discipline rates, have clear racial disparities DPI knows this and has written about it on its website.

"I wonder at the ways in which these risk categories push schools and districts to look at individuals instead of structural issues saying this child needs these things, rather than the structural issues being the reason we're seeing these risks," said Tolani Britton, a professor of education at UC Berkeley, who co-wrote the forthcoming study on DEWS. "I don't think it's a bad thing that students receive additional resources, but at the same time, creating algorithms that associate your race or ethnicity with your ability to complete high school seems like a dangerous path to go down."

When DEWS predicts that a student will graduate, it's usually right 97 percent of the time those students graduate in the standard four years, according to the 2021 validation test, which shows how the algorithms performed when tested on historical data. But when DEWS predicted a student wouldn't, it was usually wrong 74 percent of the time those students graduate on time, according to the same test.

This is partially by design. DPI calibrates DEWS to cast a wide net and over-identify students as being at risk of dropping out. In a 2015 paper describing DEWS in the Journal of Educational Data Mining, former DPI research analyst Jared Knowles wrote that DPI was "explicitly stating we are willing to accept" 25 false alarms that students won't graduate if it means correctly identifying one dropout.

But in its equity analysis, DPI found the algorithms don't generate false alarms equally.

A screenshot from a DPI presentation summarizing the results of the department's DEWS equity analysis. (Credit: Wisconsin Department of Public Instruction)

"IN LAYMAN's TERMS: the model over-identifies white students among the on-time graduates while it over-identifies Black, Hispanic and other students of color among the non- on-time graduates," a DPI research analyst wrote in notes for the presentation. The presentation does not specify what DEWS scores qualify as on-time graduation, for the purpose of the equity analysis.

The notes for the slide, titled "Is DEWS Fair?" end with the conclusion "no...."

"They definitely have been using a model that has systematic errors in terms of students' race, and thats really something that's got to get fixed," said Ryan Baker, a University of Pennsylvania education professor who studies early warning systems. "They had demographic factors as predictors and that's going to overemphasize the meaning of those variables and cause this kind of effect."

Recently, a team of researchers working primarily out of UC Berkeley doctoral candidate Juan Perdomo, Britton, and algorithmic fairness experts Moritz Hardt and Rediet Abebe have examined DEWS' efficacy through a different lens.

Their research using nearly 10 years of DEWS data which DPI voluntarily shared is the largest ever analysis of how a predictive early warning system affects student outcomes. While previous studies have asked how accurately early warning systems perform when tested against historical data, the UC Berkeley study examines whether DEWS led to better graduation rates for actual students labeled high risk.

The researchers tested whether graduation rates improved for students whose DEWS scores were just below the 78.5 threshold to put them in the high risk category compared to students whose scores were just above that threshold, placing them in the moderate risk category. If the system worked as intended, students in the high risk category would see improved graduation rates because they received additional resources, but the study found that being placed in the high risk category had no statistically significant effect on whether students graduated on time.

"There is no evidence that DEWS predictions have in any way influenced the likelihood of on-time graduation," the authors wrote.

If the system was working as intended and schools were directing more resources to students labeled high risk, the UC Berkeley study suggests, it would have a different but also inequitable impact. "If schools select students for intervention by ranking their [DEWS] scores and selecting those with the lowest predicted probability of graduation, underserved students would be systematically overlooked and de-prioritized," the authors wrote.

That's because DEWS' predicted graduation rates don't accurately reflect students' true graduation rates. White students, in particular, graduate at much higher rates than their DEWS scores would suggest, according to data shared with The Markup by the UC Berkeley researchers.

For example, students of color who received DEWS scores of 83 went on to graduate on time 90 percent of the time. That's the same as Wisconsin's statewide average graduation rate last year. White students who received the same DEWS score of 83 went on to graduate on time 93 percent of the time, above the state average.

But crucially, White students who received significantly lower DEWS scores of 63 graduated on time at essentially the same rate as the higher-scoring White students: 92 percent of the time. But students of color who received DEWS scores of 68 graduated on time only 81 percent of the time, below the state average.

In other words, if educators followed DEWS' advice and prioritized White students with scores of 63 for help over students of color with scores of 68, they would have prioritized students who ultimately graduate at above-average rates over students who ultimately graduate at below-average rates.

That particular quirk of the algorithm likely hasn't exacerbated inequality in Wisconsin, the study concluded, because DEWS isn't improving outcomes for anybody labeled high risk, regardless of race.

From its earliest days, DPI promoted DEWS as a cost-effective tool to combat the state's "unacceptable" graduation gap. But the early warning system wasn't the agency's first-choice solution.

As part of its biannual budget proposal in 2011, Wisconsin DPI, which was under the leadership of Tony Evers, who is now the state's governor, requested $20 million for an "Every Child a Graduate" grant program that would send resources directly to struggling districts. That year, 91 percent of White students in the state graduated from high school on time compared to 64 percent of Black students.

But then-governor Scott Walker had a different plan for public education. He cut nearly $800 million, about 7 percent, in state funding for public schools from the two-year budget. That included the $20 million for "Every Child a Graduate," of which Walker's administration redirected $15 million to build a statewide student information system to house all pupil data in one place.

Denied its grant program but in possession of a wealth of new data, DPI looked for a high-tech solution to its graduation gap. In 2012, it began piloting DEWS.

At the time of its creation, DEWS was one of the most advanced predictive early warning systems in the country. Its accuracy was "on par with some of the most well regarded systems currently in use, but is done at a larger scale, across a more diverse set of school environments, [and] in earlier grades," Knowles, the former DPI research analyst who built the system, wrote in the 2015 Journal of Educational Data Mining paper.

DPI quickly decided to expand its use of predictive analytics and in 2016 launched a sister algorithm, called the College and Career Readiness Early Warning System (CCREWS), which predicts whether students are "ready" or "not ready" for the ACT and college. In The Markup's survey of Wisconsin school districts, seven out of 80 respondents said they use CCREWS in some capacity, compared with 30 districts that reported using DEWS.

In 2019, DPI piloted yet another algorithmic model based on DEWS that purported to predict which students would succeed in AP courses. Schools in 11 districts signed up for the pilot, but the project was abandoned after the onset of the COVID-19 pandemic, according to documents obtained through a public records request.

Over the past decade of the states experimentation with predictive algorithms, Wisconsin's educational inequality has hardly improved.

The graduation gap between Black and White students has shrunk by only four points since 2011, from 27 to 23 percent. Meanwhile, the gulf between Black and White eighth graders' reading scores in Wisconsin has been the worst of any state's in the nation on every National Assessment of Educational Progress (NAEP) going back to 2011. It has also had the widest gap of any state between Black and White eighth graders' math scores on every NAEP since 2009.

"The question I always ask when that data comes out is not just how bad are Black kids doing, [but] how is it that White kids are doing so well?" said Gloria Ladson-Billings, a national expert on education inequality and a retired University of WisconsinMadison professor. "It's not like we don't know how to get these kids through. The problem is they have to look like Division I athletes for us to care enough."

Black and Hispanic students in Wisconsin told The Markup that they often feel part of a second-class school system.

Kennise Perry, a 21-year-old student at UW-Parkside, attended Milwaukee Public Schools, which are 49 percent Black before moving to the suburb of Waukesha, where the schools are only 6 percent Black. She said her childhood was difficult, her home life sometimes unstable, and her schools likely considered her a "high risk" student.

"I was the only Black kid in all of my classes. No other representation of anyone who looks like me, and my peers were extremely racist," she said. "It was really traumatic. ... I was just so angry and I didn't know how to place my anger. I was miserable. So then, of course, the labels and stuff started. But I feel that the difference between people who make it and people who don't are the people you have around you, like I had people who cared about me and gave me a second chance and stuff. [DEWS] listing these kids high risk and their statistics, youre not even giving them a chance, you're already labeling them.

Waukesha's school district did not respond to The Markup's survey or request for comment. However, documents obtained through public records requests show that Waukesha North High School, which Perry attended, signed up to participate in the pilot for DPI's algorithm designed to predict which students would succeed in AP classes.

Milwaukee Public Schools, the state's largest district, does not use DEWS or any kind of machine learning for its early warning system, spokesperson Stephen Davis wrote in an email to The Markup. Like many districts and states, it instead uses a low-tech approach that identifies students as on or off track based on whether they've hit certain benchmarks, such as being absent for a predefined number of days.

Last year, students at Cudahy High School created its first Black Student Union in response to racist incidents they felt the school's administration wasnt properly addressing.

"You know that [White students] already have a leg up," said Mia Townsend, a junior and vice president of Cudahy's Black Student Union. "You already feel that separation. ... They have more opportunities and they have more leeway when it comes to certain things."

Students in the BSU have organically provided the same kind of supportive interventions for each other that the state hoped to achieve through its predictive algorithms.

During the 202021 school year, 18 percent of White students in Wisconsin took AP exams compared to 5 percent of Black students. Townsend, an honor roll student, said she was on path to avoid AP courses until fellow junior Maurice Newton, the BSU's president, urged her to accept the challenge. She asked to join an AP English class next year.

"They make it seem like it's more challenging and it's honestly the same," Newton said. "You can pass the class with a good grade."

Mia Townsend, left, and Maurice Newton, right, started Cudahy High Schools first Black Student Union. (Credit: Rodney Johnson for The Markup)

In response to The Markup's questions about DEWS, Cudahy district superintendent Tina Owen-Moore shared an email thread in which staff members expressed that they hadn't known about and didn't currently use the predictions but that counselors were "excited about this resource." After reviewing our findings, however, Owen-Moore wrote, "That certainly changes my perspective!!"

Many districts who responded to The Markup's survey said they use DEWS predictions similarly to the way Brown and the staff at Bradford High School in Kenosha do to identify which new students in their buildings may require additional attention.

In the city of Appleton's school district, high school case managers use DEWS and other data to identify incoming first-year students in need of support and to determine special education caseloads, for example. Relying "heavily" on DEWS data, Winneconne School District sends letters to parents informing them their child may be at risk, although those letters don't reference the algorithm.

But some schools have found other, off-label uses for the data. For example, Sara Croney, the superintendent of Maple School District, told The Markup that her staff has used DEWS' "perceived unbiased data" to successfully apply for a staff development grant focused on reaching unengaged students. In the city of Racine, middle schools once used DEWS to select which students would be placed in a special "Violence Free Zone" program, which included sending disruptive students to a separate classroom.

The Racine School District is "not currently utilizing DEWS or CCREWS," spokesperson Stacy Tapp wrote in an email.

Many administrators The Markup interviewed said they had received little or no training on how DEWS calculates its predictions or how to interpret them.

"They just handed us the data and said, 'Figure it out,'" said Croney. "So our principals will analyze it and decide who are the kids in the at-risk area."

DPI provides documentation about how DEWS works and its intended uses on its website, but much of the public-facing material leaves out a key fact about the system: that its predictions are based in part on students' race, gender, family wealth, and other factors that schools have no control over.

For example, the department's DEWS Action Guide makes no mention that student race, gender, or free or reduced-price lunch status are key input variables for the algorithms.

DPI's webpage describing the data used to generate DEWS predictions lists four distinct categories of information: attendance, disciplinary record, number of districts attended in the prior year (mobility), and state test scores. It states that "demographic attributes are used," but not which ones or how they influence the predictions.

Similarly, when educators view students' DEWS predictions in the statewide information system, they can examine how students' attendance, disciplinary record, mobility, and test scores affect the overall risk label, but they are not shown how students' demographic features affect the prediction.

Shari Johnson, director of curriculum and instruction for the Richland School District, said her schools were starting to create action plans and assign staff mentors to "high risk" students with the goal of getting them out of that category, especially those at "most risk" because she said it wouldn't be possible to mentor everyone.

However, when she spoke to The Markup, she didn't know that characteristics such as a disability or being economically disadvantaged affected a student's score.

"Whose responsibility is it that we know about these things? That's my concern in this position, for me to only have found out by chance," Johnson said. "What I do is directly correlated to DEWS and the information that's there, and that's scary to me."

The disconnect between how DEWS works and how educators understand it to work isn't news to DPI.

In 2016, researchers with the Midwest Regional Education Laboratory wrote a report for DPI that was never published, based on a survey of middle school principals' experiences with DEWS. The report, which we obtained through public records requests, concluded that respondents "desired more training and support on how to identify and monitor interventions" and that "time, money, and training on DEWS" were the top impediments to using the system.

Bradford High School principal Brian Geiger said he remembers hearing about DEWS around the time of its launch, back when he was an assistant principal at another Kenosha school, and has used it for various purposes, including summer home visits, ever since. Now Brown, his assistant principal at Bradford, has picked up the practice. Even knowing there are flaws with DEWS, Brown said the predictions are the best data he has for incoming students.

"It's not a 100 percent predictor. My perception on this is that we kind of use it as a guide," he said, adding, "I wish we could go visit every single house of all 1,400 kids [enrolled at Bradford High School]. We don't have a summer school budget to do that."

Credits: By Todd Feathers, enterprise reporter; Ko Bragg, editor; Joel Eastwood and Gabriel Hongsdusit, design and graphics; Rodney Johnson, photography; Gabriel Hongsdusit, illustration; Jeremy Singer-Vine, data coach; Maria Puertas, engagement; and, Jill Jaroff, copy editing/production for The Markup

This article was originally published on The Markup and was republished under the Creative Commons Attribution-NonCommercial-NoDerivatives license.

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False alarm: How Wisconsin uses race and income to label students ... - PBS Wisconsin

As technology advances and becomes a more integral part of the modern world, cybercriminals will learn new ways to exploit it. The cybersecurity sector must evolve faster. Could artificial intelligence (AI) be a solution for future security threats?

AI programs can make autonomous decisions and implement security efforts around the clock. The programs analyze much more risk data at any given time than a human mind. The networks or data storage systems under an AI programs protection gain continually updated protection thats always studying responses to ongoing cyber-attacks.

People need cybersecurity experts to implement measures that protect their data or hardware against cyber criminals. Crimes like phishing and denial-of-service attacks happen all the time. While cybersecurity experts need to do things like sleep or study new cybercrime strategies to fight suspicious activity effectively, AI programs dont have to do either.

Advancements in any field have pros and cons. AI protects user information day and night while automatically learning from cyber attacks happening elsewhere. Theres no room for human error that could cause someone to overlook an exposed network or compromised data.

However, AI software could be a risk in itself. Attacking the software is possible because its another part of a computer or networks system. Human brains arent susceptible to malware in the same way.

Deciding if AI should become the leading cybersecurity effort of a network is a complicated decision. Evaluating the benefits and potential risks before choosing is the smartest way to handle a possible cybersecurity transition.

When people picture an AI program, they likely think of it positively. Its already active in the everyday lives of global communities. AI programs are reducing safety risks in potentially dangerous workplaces so employees are safer while theyre on the clock. It also has machine learning (ML) capabilities that collect instant data to recognize fraud before people can potentially click links or open documents sent by cybercriminals.

AI decision-making in cybersecurity could be the way of the future. In addition to helping people in numerous industries, it can improve digital security in these significant ways.

Even the most skilled cybersecurity teams have to sleep occasionally. When they arent monitoring their networks, intrusions, and vulnerabilities remain a threat. AI can analyze data continuously to recognize potential patterns that indicate an incoming cyber threat. Since global cyber attacks occur every 39 seconds, staying vigilant is crucial to securing data.

An AI program that monitors network, cloud, and application vulnerabilities would also prevent financial loss after a cyber attack. The latest data shows companies lose over $1 million per breach, given the rise of remote employment. Home networks stop internal IT teams from completely controlling a businesss cybersecurity. AI would reach those remote workers and provide an additional layer of security outside professional offices.

People accessing systems with AI capabilities can also opt to log into their accounts using biometric validation. Scanning someones face or fingerprint creates biometric login credentials instead of or in addition to traditional passwords and two-factor authentication.

Biometric data also save as encrypted numerical values instead of raw data. If cybercriminals hacked into those values, theyd be nearly impossible to reverse engineer and use to access confidential information.

When human-powered IT security teams want to identify new cybersecurity threats, they must undergo training that could take days or weeks. AI programs learn about new dangers automatically. Theyre always ready for system updates that inform them about the latest ways cybercriminals are trying to hack their technology.

Continually updating threat identification methods mean network infrastructure and confidential data are safer than ever. Theres no room for human error due to knowledge gaps between training sessions.

Someone can become the leading expert in their field but still be subject to human error. People get tired, procrastinate, and forget to take essential steps within their roles. When that happens with someone on an IT security team, it could result in an overlooked security task that leaves the network open to vulnerabilities.

AI doesnt get tired or forget what it needs to do. It removes potential shortcomings due to human error, making cybersecurity processes more efficient. Lapses in security and network holes wont remain a risk for long, if they happen at all.

As with any new technological development, AI still poses a few risks. Its relatively new, so cybersecurity experts should remember these potential concerns when picturing a future of AI decision-making.

AI also requires an updated data set to remain at peak performance. Without input from computers across a companys entire network, it wouldnt provide the security expected from the client. Sensitive information could remain more at risk of intrusions because the AI system doesnt know its there.

Data sets also include the latest upgrades in cybersecurity resources. The AI system would need the newest malware profiles and anomaly detection capabilities to provide adequate protection consistently. Providing that information can be more work than an IT team can handle at one time.

IT team members would need the training to gather and provide updated data sets to their newly installed AI security programs. Every step of upgrading to AI decision-making takes time and financial resources. Organizations lacking the ability to do both swiftly could become more vulnerable to attacks than before.

Some older methods of cybersecurity protection are easier for IT professionals to take apart. They could easily access every layer of security measures for traditional systems, whereas AI programs are much more complex.

AI isnt easy for people to take apart for minor data mining because its supposed to function independently. IT and cybersecurity professionals may see it as less transparent and more challenging to manipulate to a businesss advantage. It requires more trust in the automatic nature of the system, which can make people wary of using them for their most sensitive security needs.

ML algorithms are part of AI decision-making. People rely on that vital component of AI programs to identify security risks, but even computers arent perfect. Due to data reliance and the newness of technology, all machine learning algorithms can make anomaly detection mistakes.

When an AI security program detects an anomaly, it may alert security operations center experts so they can manually review and remove the issue. However, the program can also remove it automatically. Although thats a benefit for real threats, its dangerous when the detection is a false positive.

The AI algorithm could remove data or network patches that arent a threat. That makes the system more at risk for real security issues, especially if there isnt a watchful IT team monitoring what the algorithm is doing.

If events like that happen regularly, the team could also become distracted. Theyd have to devote attention to sorting through false positives and fixing what the algorithm accidentally disrupted. Cybercriminals would have an easier time bypassing both the team and the algorithm if this complication lasted long-term. In this scenario, updating the AI software or waiting for more advanced programming could be the best way to avoid false positives.

Artificial intelligence is already helping people secure sensitive information. If more people begin to trust AI decision-making in cybersecurity for broader uses, there could be potential benefits against future attacks.

Understanding the risks and rewards of implementing technology in new ways is always essential.

Cybersecurity teams will understand how best to implement technology in new ways without opening their systems to potential weaknesses.

Featured Image Credit: Photo by cottonbro studio; Pexels; Thank you!

Zac is the Features Editor at ReHack, where he covers tech trends ranging from cybersecurity to IoT and anything in between.

Originally posted here:

Can We Trust AI Decision-Making in Cybersecurity? - ReadWrite

Key Financial Results

All figures shown in the chart above are for the trailing 12 month (TTM) period

Revenue missed analyst estimates by 4.8%. Earnings per share (EPS) also missed analyst estimates significantly.

Looking ahead, revenue is forecast to grow 21% p.a. on average during the next 2 years, compared to a 12% growth forecast for the Software industry in the US.

Performance of the American Software industry.

The company's shares are down 18% from a week ago.

Before we wrap up, we've discovered 4 warning signs for Stronghold Digital Mining (2 are a bit concerning!) that you should be aware of.

Find out whether Stronghold Digital Mining is potentially over or undervalued by checking out our comprehensive analysis, which includes fair value estimates, risks and warnings, dividends, insider transactions and financial health.

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Stronghold Digital Mining First Quarter 2023 Earnings: Misses Expectations - Simply Wall St