Mediaboss Marketing

While, in the past, artificial intelligence may have been thought to be a product of science fiction, most professionals today understand that the adoption of smart technology is actively changing workplaces. There are applications of AI throughout nearly every profession and industry, and human resources careers are no exception.

A recent survey conducted by Oracle and Future Workplace found that human resources professionals believe AI can present opportunities for mastering new skills and gaining more free time, allowing HR professionals to expand their current roles in order to be more strategic within their organization.

Among HR leaders who participated in the survey, however, 81 percent said that they find it challenging to keep up with the pace of technological changes at work. As such, it is more important now than ever before for human resources professionals to understand the ways in which AI is reshaping the industry.

Read on to explore what artificial intelligence entails, how it is applied to the world of human resources management, and how HR professionals can prepare for the future of the field today.

At a high level, artificial intelligence (AI) is a technology that allows computers to learn from and make or recommend actions based on previously collected data. In terms of human resources management, artificial intelligence can be applied in many different ways to streamline processes and improve efficiency.

Uwe Hohgrawe, lead faculty for Northeasterns Master of Professional Studies in Analytics program explains that we as humans see the information in front of us and use our intelligence to draw conclusions. Machines are not intelligent, but we can make them appear intelligent by feeding them the right information and technology.

Learn More: AI & Other Trends Defining the HRM Industry

While organizations are adopting AI into their human resources processes at varying rates, it is clear to see that the technology will have a lasting impact on the field as it becomes more widely accepted. For this reason, it is important that HR professionals prepare themselves for these changes by understanding what the technology is and how it is applied across various functions.

Learn more about earning an advanced degree in Human Resources Management

LEARN MORE

Among the numerous applications of AI in the human resources sector, some of the first changes HR professionals should expect to see involve recruitment and onboarding, employee experience, process improvement, and the automation of administrative tasks.

While many organizations are already beginning to integrate AI technology into their recruiting efforts, the vast majority of organizations are not. In fact, Deloittes 2019 Global Human Capital Trends survey found that only 6 percent of respondents believed that they had the best-in-class recruitment processes in technology, while 81 percent believed their organizations processes were standard or below standard. For this reason, there are tremendous opportunities for professionals to adapt their processes and reap the benefits of using this advanced technology.

During the recruitment process, AI can be used to the benefit of not only the hiring organization but its job applicants, as well. For example, AI technology can streamline application processes by designing more user-friendly forms that a job applicant is more likely to complete, effectively reducing the number of abandoned applications.

While this approach has made the role of the human resources department in recruitment much easier, artificial intelligence also allows for simpler and more meaningful applications on the candidates end, which has been shown to improve application completion rates.

Additionally, AI has played an important role in candidate rediscovery. By maintaining a database of past applicants, AI technology can analyze the existing pool of applicants and identify those that would be a good fit for new roles as they open up. Rather than expending time and resources searching for fresh talent, HR professionals can use this technology to identify qualified employees more quickly and easily than ever before.

Once hiring managers have found the best fit for their open positions, the onboarding process begins. With the help of AI, this process doesnt have to be restricted to standard business hoursa huge improvement over onboarding processes of the past.

Instead, AI technology allows new hires to utilize human resources support at any time of day and in any location through the use of chatbots and remote support applications. This change not only provides employees with the ability to go through the onboarding process at their own pace, but also reduces the administrative burden and typically results in faster integration.

In addition to improvements to the recruitment process, HR professionals can also utilize artificial intelligence to boost internal mobility and employee retention.

Through personalized feedback surveys and employee recognition systems, human resources departments can gauge employee engagement and job satisfaction more accurately today than ever before. This is incredibly beneficial considering how important it is to understand the overall needs of employees, however there are several key organizational benefits to having this information, as well.

According to a recent report from the Human Resources Professional Association, some AI software can evaluate key indicators of employee success in order to identify those that should be promoted, thus driving internal mobility. Doing so has the potential to significantly reduce talent acquisition costs and bolster employee retention rates.

This technology is not limited to identifying opportunities to promote from within, however; it can also predict who on a team is most likely to quit. Having this knowledge as soon as possible allows HR professionals to deploy retention efforts before its too late, which can strategically reduce employee attrition.

One of the key benefits of leveraging artificial intelligence in various human resources processes is actually the same as it is in other disciplines and industries: Automating low value, easily repeatable administrative tasks gives HR professionals more time to contribute to strategic planning at the organizational level. This, in turn, enables the HR department to become a strategic business partner within their organizations.

Smart technologies can automate processes such as the administration of benefits, pre-screening candidates, scheduling interviews, and more. Although each of these functions is important to the overall success of an organization, carrying out the tasks involved in such processes is generally time-consuming, and the burden of these duties often means that HR professionals have less time to contribute to serving their employees in more impactful ways.

Deploying AI software to automate administrative tasks can ease this burden. For instance, a study by Eightfold found that HR personnel who utilized AI software performed administrative tasks 19 percent more effectively than departments that do not use such technology. With the time that is saved, HR professionals can devote more energy to strategic planning at the organizational level.

While it is clear that artificial intelligence will continue to positively shape the field of human resources management in the coming years, HR professionals should also be aware of the challenges that they might face.

The most common concerns that HR leaders have focus primarily on making AI simpler and safer to use. In fact, the most common factor preventing people from using AI at work are security and privacy concerns. Additionally, 31 percent of respondents in Oracles survey expressed that they would rather interact with a human in the workplace than a machine. Moving forward, HR professionals will need to be prepared to address these concerns by staying on top of trends and technology as they evolve and change.

People will need to be aware of ethical and privacy questions when using this technology, Hohgrowe says. In human resources, [AI] can involve using sensitive information to create sensitive insights.

For instance, employees want their organizations to respect their personal data and ask for permission before using such technology to gather information about them. However organizations also want to feel protected from data breaches, and HR professionals must take the appropriate security measures into account.

To prepare for the future of human resources management, professionals should take the necessary steps to learn about current trends in the field, as well as lay a strong foundation of HR knowledge that they can build upon as the profession evolves.

Staying up to date with industry publications and networking with leaders in the field is a great way to stay abreast of current trends like the rapid adoption of artificial intelligence technologies. Building your foundational knowledge of key human resource management theories, strategy, and ethics, on the other hand, is best achieved through higher education.

Although there are many certifications and courses available that focus on specific HR topics, earning an advanced degree like a Master of Science in Human Resources Management provides students with a more holistic approach to understanding the connection between an organization and its people.

At Northeastern, we highlight the importance of three literacies: data literacy, technological literacy, and humanic literacy. That combination is one of the areas where I believe we will pave the way in the future, Hohgrawe says. This also allows us to explore augmented artificial intelligence in a way that appreciates the relationship between human, machine, and data.

Students looking to specialize in AI also have the opportunity to declare a concentration in artificial intelligence within Northeasterns human resource management program. Those who specialize in this specific aspect of the industry will study topics such as human resources information processing, advanced analytical utilization, and AI communication and visualization. Similarly, those who seek a more technical masters degree might consider a Northeasterns Master of Professional Studies in Enterprise Intelligence, which also includes a concentration in AI for human resources.

No matter each students specific path, however, those who choose to study at Northeastern will have the unique chance to learn from practitioners with advanced knowledge and experience in the field. Many of Northeasterns faculty have previously or are currently working in the human resources management field, enabling them to bring a unique perspective to the classroom and educate students on the real-world challenges that HR professionals face today.

Between the world-class faculty members and the multitude of experiential learning opportunities provided during the pursuit of a masters degree, aspiring HR professionals will graduate from Northeasterns program with the unique combination of experience and expertise needed to land a lucrative role in this growing field.

Interested in advancing your career in HR? Explore Northeasterns Master of Science in Human Resources Management program and consider taking the next step toward a career in this in-demand industry.

The rest is here:
Artificial Intelligence in Human Resource Management

What is AI?

A machine with the ability to perform cognitive functions such as perceiving, learning, reasoning and solve problems are deemed to hold an artificial intelligence.

Artificial intelligence exists when a machine has cognitive ability. The benchmark for AI is the human level concerning reasoning, speech, and vision.

In this basic tutorial, you will learn-

Nowadays, AI is used in almost all industries, giving a technological edge to all companies integrating AI at scale. According to McKinsey, AI has the potential to create 600 billions of dollars of value in retail, bring 50 percent more incremental value in banking compared with other analytics techniques. In transport and logistic, the potential revenue jump is 89 percent more.

Concretely, if an organization uses AI for its marketing team, it can automate mundane and repetitive tasks, allowing the sales representative to focus on tasks like relationship building, lead nurturing, etc. A company name Gong provides a conversation intelligence service. Each time a Sales Representative make a phone call, the machine records transcribes and analyzes the chat. The VP can use AI analytics and recommendation to formulate a winning strategy.

In a nutshell, AI provides a cutting-edge technology to deal with complex data which is impossible to handle by a human being. AI automates redundant jobs allowing a worker to focus on the high level, value-added tasks. When AI is implemented at scale, it leads to cost reduction and revenue increase.

Artificial intelligence is a buzzword today, although this term is not new. In 1956, a group of avant-garde experts from different backgrounds decided to organize a summer research project on AI. Four bright minds led the project; John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories).

The primary purpose of the research project was to tackle "every aspect of learning or any other feature of intelligence that can in principle be so precisely described, that a machine can be made to simulate it."

The proposal of the summits included

It led to the idea that intelligent computers can be created. A new era began, full of hope - Artificial intelligence.

Artificial intelligence can be divided into three subfields:

Machine learning is the art of study of algorithms that learn from examples and experiences.

Machine learning is based on the idea that there exist some patterns in the data that were identified and used for future predictions.

The difference from hardcoding rules is that the machine learns on its own to find such rules.

Deep learning is a sub-field of machine learning. Deep learning does not mean the machine learns more in-depth knowledge; it means the machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model. For instance, Google LeNet model for image recognition counts 22 layers.

In deep learning, the learning phase is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other.

Most of our smartphone, daily device or even the internet uses Artificial intelligence. Very often, AI and machine learning are used interchangeably by big companies that want to announce their latest innovation. However, Machine learning and AI are different in some ways.

AI- artificial intelligence- is the science of training machines to perform human tasks. The term was invented in the 1950s when scientists began exploring how computers could solve problems on their own.

Artificial Intelligence is a computer that is given human-like properties. Take our brain; it works effortlessly and seamlessly to calculate the world around us. Artificial Intelligence is the concept that a computer can do the same. It can be said that AI is the large science that mimics human aptitudes.

Machine learning is a distinct subset of AI that trains a machine how to learn. Machine learning models look for patterns in data and try to conclude. In a nutshell, the machine does not need to be explicitly programmed by people. The programmers give some examples, and the computer is going to learn what to do from those samples.

AI has broad applications-

AI is used in all the industries, from marketing to supply chain, finance, food-processing sector. According to a McKinsey survey, financial services and high tech communication are leading the AI fields.

A neural network has been out since the nineties with the seminal paper of Yann LeCun. However, it started to become famous around the year 2012. Explained by three critical factors for its popularity are:

Machine learning is an experimental field, meaning it needs to have data to test new ideas or approaches. With the boom of the internet, data became more easily accessible. Besides, giant companies like NVIDIA and AMD have developed high-performance graphics chips for the gaming market.

Hardware

In the last twenty years, the power of the CPU has exploded, allowing the user to train a small deep-learning model on any laptop. However, to process a deep-learning model for computer vision or deep learning, you need a more powerful machine. Thanks to the investment of NVIDIA and AMD, a new generation of GPU (graphical processing unit) are available. These chips allow parallel computations. It means the machine can separate the computations over several GPU to speed up the calculations.

For instance, with an NVIDIA TITAN X, it takes two days to train a model called ImageNet against weeks for a traditional CPU. Besides, big companies use clusters of GPU to train deep learning model with the NVIDIA Tesla K80 because it helps to reduce the data center cost and provide better performances.

Data

Deep learning is the structure of the model, and the data is the fluid to make it alive. Data powers the artificial intelligence. Without data, nothing can be done. Latest Technologies have pushed the boundaries of data storage. It is easier than ever to store a high amount of data in a data center.

Internet revolution makes data collection and distribution available to feed machine learning algorithm. If you are familiar with Flickr, Instagram or any other app with images, you can guess their AI potential. There are millions of pictures with tags available on these websites. Those pictures can be used to train a neural network model to recognize an object on the picture without the need to manually collect and label the data.

Artificial Intelligence combined with data is the new gold. Data is a unique competitive advantage that no firm should neglect. AI provides the best answers from your data. When all the firms can have the same technologies, the one with data will have a competitive advantage over the other. To give an idea, the world creates about 2.2 exabytes, or 2.2 billion gigabytes, every day.

A company needs exceptionally diverse data sources to be able to find the patterns and learn and in a substantial volume.

Algorithm

Hardware is more powerful than ever, data is easily accessible, but one thing that makes the neural network more reliable is the development of more accurate algorithms. Primary neural networks are a simple multiplication matrix without in-depth statistical properties. Since 2010, remarkable discoveries have been made to improve the neural network

Artificial intelligence uses a progressive learning algorithm to let the data do the programming. It means, the computer can teach itself how to perform different tasks, like finding anomalies, become a chatbot.

Summary

Artificial intelligence and machine learning are two confusing terms. Artificial intelligence is the science of training machine to imitate or reproduce human task. A scientist can use different methods to train a machine. At the beginning of the AI's ages, programmers wrote hard-coded programs, that is, type every logical possibility the machine can face and how to respond. When a system grows complex, it becomes difficult to manage the rules. To overcome this issue, the machine can use data to learn how to take care of all the situations from a given environment.

The most important features to have a powerful AI is to have enough data with considerable heterogeneity. For example, a machine can learn different languages as long as it has enough words to learn from.

AI is the new cutting-edge technology. Ventures capitalist are investing billions of dollars in startups or AI project. McKinsey estimates AI can boost every industry by at least a double-digit growth rate.

See original here:
What is AI? Artificial Intelligence Tutorial for Beginners

Automation, robotics and the use of sophisticated computer software and programs characterize a career in artificial intelligence (AI). Candidates interested in pursuing jobs in this field require specific education based on foundations of math, technology, logic, and engineering perspectives. Written and verbal communication skills are also important to convey how AI tools and services are effectively employed within industry settings. To acquire these skills, those with an interest in an AI career should investigate the various career choices available within the field.

The most successful AI professionals often share common characteristics that enable them to succeed and advance in their careers. Working with artificial intelligence requires an analytical thought process and the ability to solve problems with cost-effective, efficient solutions. It also requires foresight about technological innovations that translate to state-of-the-art programs that allow businesses to remain competitive. Additionally, AI specialists need technical skills to design, maintain and repair technology and software programs. Finally, AI professionals must learn how to translate highly technical information in ways that others can understand in order to carry out their jobs. This requires good communication and the ability to work with colleagues on a team.

Basic computer technology and math backgrounds form the backbone of most artificial intelligence programs. Entry level positions require at least a bachelors degree while positions entailing supervision, leadership or administrative roles frequently require masters or doctoral degrees. Typical coursework involves study of:

Candidates can find degree programs that offer specific majors in AI or pursue an AI specialization from within majors such as computer science, health informatics, graphic design, information technology or engineering.

A career in artificial intelligence can be realized within a variety of settings including private companies, public organizations, education, the arts, healthcare facilities, government agencies and the military. Some positions may require security clearance prior to hiring depending on the sensitivity of information employees may be expected to handle. Examples of specific jobs held by AI professionals include:

From its inception in the 1950s through the present day, artificial intelligence continues to advance and improve the quality of life across multiple industry settings. As a result, those with the skills to translate digital bits of information into meaningful human experiences will find a career in artificial intelligence to be sustaining and rewarding.

Read more here:
What Skills Do I Need to Get a Job in Artificial Intelligence?

Overview

"Artificial intelligence is the new electricity."

- Andrew Ng, Stanford Adjunct Professor

Take advantage of the opportunity to virtually step into the classrooms of Stanford professors like Andrew Ng who are leading the Artificial Intelligence revolution.

Classes in the Artificial Intelligence Graduate Certificate provide the foundation and advanced skills in the principles and technologies that underlie AI including logic, knowledge representation, probabilistic models, and machine learning. Students can pursue topics in depth, with courses available in areas such as robotics, vision, and natural language processing.

Prepare for advanced Artificial Intelligence curriculum and earn graduate credit by taking these recommended courses; these courses willnot count towards the Artificial Intelligence graduate certificate. We highly recommend taking CS109 Introduction to Probability for Computer Scientists, or STATS116 Theory of Probability.

The certificate is designed to be completed in nine months, but you may take up to three years to complete it. Courses are available during Autumn, Winter, and Spring quarters:

Note: Course offerings may be subject to change. You do not need to enroll in the certificate to take the courses. You may enroll in any courses if you meet its prerequisites.

Software engineers interested in artificial intelligence. The fast-paced, academically rigorous classes that are part of this certificate are appropriate for applicants who can demonstrate mastery of the prerequisite subject matter including statistics and probability, linear algebra and calculus. Students should also have significant programming experience in Java, C++, Python or similar languages.

As demand for AI courses is high and seats are limited, applications are subject to additional review. Applicants will be notified once the application review process is complete and a decision has been made.

To pursue a graduate certificate you need to apply.

Tuition is based on the number of units you take. See Graduate Course Tuition on our Tuition & Fees page for more information.

1-2 years average3 years maximum to complete

Submit an inquiry to receive more information.

Read more:
Artificial Intelligence Graduate Certificate | Stanford Online

Intelligent machines have long been the subject of science fiction. However, we now live in an era in which artificial intelligence (Al) is a reality, and it is having very real and deep impacts on our daily lives. From phones to cars to finances and medical care, AI is shifting the way we live.

AI applications can be found in many aspects of our lives, from agriculture to industry, communications, education, finance, government, service, manufacturing, medicine, and transportation. Even public safety and criminal justice are benefiting from AI. For example, traffic safety systems identify violations and enforce the rules of the road, and crime forecasts allow for more efficient allocation of policing resources. AI is also helping to identify the potential for an individual under criminal justice supervision to reoffend.[1]

Research supported by NIJ is helping to lead the way in applying AI to address criminal justice needs, such as identifying individuals and their actions in videos relating to criminal activity or public safety, DNA analysis, gunshot detection, and crime forecasting.

AI is a rapidly advancing field of computer science. In the mid-1950s, John McCarthy, who has been credited as the father of AI, defined it as the science and engineering of making intelligent machines.[2] Conceptually, AI is the ability of a machine to perceive and respond to its environment independently and perform tasks that would typically require human intelligence and decision-making processes, but without direct human intervention.

See A Brief History of Artificial Intelligence

One facet of human intelligence is the ability to learn from experience. Machine learning is an application of AI that mimics this ability and enables machines and their software to learn from experience.[3] Particularly important from the criminal justice perspective is pattern recognition. Humans are efficient at recognizing patterns and, through experience, we learn to differentiate objects, people, complex human emotions, information, and conditions on a daily basis. AI seeks to replicate this human capability in software algorithms and computer hardware. For example, self-learning algorithms use data sets to understand how to identify people based on their images, complete intricate computational and robotics tasks, understand purchasing habits and patterns online, detect medical conditions from complex radiological scans, and make stock market predictions.

AI is being researched as a public safety resource in numerous ways. One particular AI application facial recognition can be found everywhere in both the public and the private sectors.[4] Intelligence analysts, for example, often rely on facial images to help establish an individuals identity and whereabouts. Examining the huge volume of possibly relevant images and videos in an accurate and timely manner is a time-consuming, painstaking task, with the potential for human error due to fatigue and other factors. Unlike humans, machines do not tire. Through initiatives such as the Intelligence Advanced Research Projects Activitys Janus computer-vision project, analysts are performing trials on the use of algorithms that can learn how to distinguish one person from another using facial features in the same manner as a human analyst.[5]

See

The U.S. Department of Transportation is also looking to increase public safety through researching, developing, and testing automatic traffic accident detection based on video to help maintain safe and efficient commuter traffic over various locations and weather, lighting, and traffic conditions.[6] AI algorithms are being used in medicine to interpret radiological images, which could have important implications for the criminal justice and medical examiner communities when establishing cause and manner of death.[7] AI algorithms have also been explored in various disciplines in forensic science, including DNA analysis.[8]

AI is also quickly becoming an important technology in fraud detection.[9] Internet companies like PayPal stay ahead of fraud attempts by using volumes of data to continuously train their fraud detection algorithms to predict and recognize anomalous patterns and to learn to recognize new patterns.[10]

The AI research that NIJ supports falls primarily into four areas: public safety video and image analysis, DNA analysis, gunshot detection, and crime forecasting.

Video and image analysis is used in the criminal justice and law enforcement communities to obtain information regarding people, objects, and actions to support criminal investigations. However, the analysis of video and image information is very labor-intensive, requiring a significant investment in personnel with subject matter expertise. Video and image analysis is also prone to human error due to the sheer volume of information, the fast pace of changing technologies such as smartphones and operating systems, and a limited number of specialized personnel with the knowledge to process such information.

AI technologies provide the capacity to overcome such human errors and to function as experts. Traditional software algorithms that assist humans are limited to predetermined features such as eye shape, eye color, and distance between eyes for facial recognition or demographics information for pattern analysis. AI video and image algorithms not only learn complex tasks but also develop and determine their own independent complex facial recognition features/parameters to accomplish these tasks, beyond what humans may consider. These algorithms have the potential to match faces, identify weapons and other objects, and detect complex events such as accidents and crimes in progress or after the fact.

In response to the needs of the criminal justice and law enforcement communities, NIJ has invested in several areas to improve the speed, quality, and specificity of data collection, imaging, and analysis and to improve contextual information.

For instance, to understand the potential benefits of AI in terms of speed, researchers at the University of Texas at Dallas, with funding from NIJ and in partnership with the FBI and the National Institute of Standards and Technology, are assessing facial identification by humans and examining methods for effectively comparing AI algorithms and expert facial examiners. Preliminary results show that when the researchers limit the recognition time to 30 seconds, AI-based facial-recognition algorithms developed in 2017 perform comparably to human facial examiners.[11] The implications of these findings are that AI-based algorithms can potentially be used as a second pair of eyes to increase the accuracy of expert human facial examiners and to triage data to increase productivity.

In addition, in response to the need for higher quality information and the ability to use lower quality images more effectively, Carnegie Mellon University is using NIJ funding to develop AI algorithms to improve detection, recognition, and identification. One particularly important aspect is the universitys work on images in which an individuals face is captured at different angles or is partially to the side, and when the individual is looking away from the camera, obscured by masks or helmets, or blocked by lamp posts or lighting. The researchers are also working with low-quality facial image construction, including images with poor resolution and low ambient light levels, where the image quality makes facial matching difficult. NIJs test and evaluation center is currently testing and evaluating these algorithms.[12]

Finally, to decipher a license plate (which could help identify a suspect or aid in an investigation) or identify a person in extremely low-quality images or video, researchers at Dartmouth College are using AI algorithms that systematically degrade high-quality images and compare them with low-quality ones to better recognize lower quality images and video. For example, clear images of numbers and letters are slowly degraded to emulate low-quality images. The degraded images are then expressed and catalogued as mathematical representations. These degraded mathematical representations can then be compared with low-quality license plate images to help identify the license plate.[13]

Also being explored is the notion of scene understanding, or the ability to develop text that describes the relationship between objects (people, places, and things) in a series of images to provide context. For example, the text may be Pistol being drawn by a person and discharging into a store window. The goal is to detect objects and activities that will help identify crimes in progress for live observation and intervention as well as to support investigations after the fact.[14] Scene understanding over multiple scenes can indicate potentially important events that law enforcement should view to confirm and follow. One group of researchers at the University of Central Florida, in partnership with the Orlando Police Department, is using NIJ funding to develop algorithms to identify objects in videos, such as people, cars, weapons, and buildings, without human intervention. They are also developing algorithms to identify actions such as traffic accidents and violent crimes.

Another important aspect of AI is the ability to predict behavior. In contrast to the imaging and identification of criminal activity in progress, the University of Houston has used NIJ funding to develop algorithms that provide continuous monitoring to assess activity and predict emergent suspicious and criminal behavior across a network of cameras. This work also concentrates on using clothing, skeletal structure, movement, and direction prediction to identify and re-acquire people of interest across multiple cameras and images.[15]

AI can also benefit the law enforcement community from a scientific and evidence processing standpoint. This is particularly true in forensic DNA testing, which has had an unprecedented impact on the criminal justice system over the past several decades.

Biological material, such as blood, saliva, semen, and skin cells, can be transferred through contact with people and objects during the commission of a crime. As DNA technology has advanced, so has the sensitivity of DNA analysis, allowing forensic scientists to detect and process low-level, degraded, or otherwise unviable DNA evidence that could not have been used previously. For example, decades-old DNA evidence from violent crimes such as sexual assaults and homicide cold cases is now being submitted to laboratories for analysis. As a result of increased sensitivity, smaller amounts of DNA can be detected, which leads to the possibility of detecting DNA from multiple contributors, even at very low levels. These and other developments are presenting new challenges for crime laboratories. For instance, when using highly sensitive methods on items of evidence, it may be possible to detect DNA from multiple perpetrators or from someone not associated with the crime at all thus creating the issue of DNA mixture interpretation and the need to separate and identify (or deconvolute) individual profiles to generate critical investigative leads for law enforcement.

AI may have the potential to address this challenge. DNA analysis produces large amounts of complex data in electronic format; these data contain patterns, some of which may be beyond the range of human analysis but may prove useful as systems increase in sensitivity. To explore this area, researchers at Syracuse University partnered with the Onondaga County Center for Forensic Sciences and the New York City Office of Chief Medical Examiners Department of Forensic Biology to investigate a novel machine learning-based method of mixture deconvolution. With an NIJ research award, the Syracuse University team worked to combine the strengths of approaches involving human analysts with data mining and AI algorithms. The team used this hybrid approach to separate and identify individual DNA profiles to minimize the potential weaknesses inherent in using one approach in isolation. Although ongoing evaluation of the use of AI techniques is needed and there are many factors that can influence the ability to parse out individual DNA donors, research shows that AI technology has the potential to assist in these complicated analyses.[16]

The discovery of pattern signatures in gunshot analysis offers another area in which to use AI algorithms. In one project, NIJ funded Cadre Research Labs, LLC, to analyze gunshot audio files from smartphones and smart devices based on the observation that the content and quality of gunshot recordings are influenced by firearm and ammunition type, the scene geometry, and the recording device used.[17] Using a well-defined mathematical model, the Cadre scientists are working to develop algorithms to detect gunshots, differentiate muzzle blasts from shock waves, determine shot-to-shot timings, determine the number of firearms present, assign specific shots to firearms, and estimate probabilities of class and caliber all of which could help law enforcement in investigations.[18]

Predictive analysis is a complex process that uses large volumes of data to forecast and formulate potential outcomes. In criminal justice, this job rests mainly with police, probation practitioners, and other professionals, who must gain expertise over many years. The work is time-consuming and subject to bias and error.[19]

With AI, volumes of information on law and legal precedence, social information, and media can be used to suggest rulings, identify criminal enterprises, and predict and reveal people at risk from criminal enterprises. NIJ-supported researchers at the University of Pittsburgh are investigating and designing computational approaches to statutory interpretation that could potentially increase the speed and quality of statutory interpretation performed by judges, attorneys, prosecutors, administrative staff, and other professionals. The researchers hypothesize that a computer program can automatically recognize specific types of statements that play the most important roles in statutory interpretation. The goal is to develop a proof-of-concept expert system to support interpretation and perform it automatically for cybercrime.[20]

AI is also capable of analyzing large volumes of criminal justice-related records to predict potential criminal recidivism. Researchers at the Research Triangle Institute, in partnership with the Durham Police Department and the Anne Arundel Sheriffs Department, are working to create an automated warrant service triage tool for the North Carolina Statewide Warrant Repository. The NIJ-supported team is using algorithms to analyze data sets with more than 340,000 warrant records. The algorithms form decision trees and perform survival analysis to determine the time span until the next occurrence of an event of interest and predict the risk of re-offending for absconding offenders (if a warrant goes unserved). This model will help practitioners triage warrant service when backlogs exist. The resulting tool will also be geographically referenced so that practitioners can pursue concentrations of high-risk absconders along with others who have active warrants to optimize resources.[21]

AI can also help determine potential elder victims of physical and financial abuse. NIJ-funded researchers at the University of Texas Health Science Center at Houston used AI algorithms to analyze elder victimization. The algorithms can determine the victim, perpetrator, and environmental factors that distinguish between financial exploitation and other forms of elder abuse. They can also differentiate pure financial exploitation (when the victim of financial exploitation experiences no other abuse) from hybrid financial exploitation (when physical abuse or neglect accompanies financial exploitation). The researchers hope that these data algorithms can be transformed into web-based applications so that practitioners can reliably determine the likelihood that financial exploitation is occurring and quickly intervene.[22]

Finally, AI is being used to predict potential victims of violent crime based on associations and behavior. The Chicago Police Department and the Illinois Institute of Technology used algorithms to collect information and form initial groupings that focus on constructing social networks and performing analysis to determine potential high-risk individuals. This NIJ-supported research has since become a part of the Chicago Police Departments Violence Reduction Strategy.[23]

Every day holds the potential for new AI applications in criminal justice, paving the way for future possibilities to assist in the criminal justice system and ultimately improve public safety.

Video analytics for integrated facial recognition, the detection of individuals in multiple locations via closed-circuit television or across multiple cameras, and object and activity detection could prevent crimes through movement and pattern analysis, recognize crimes in progress, and help investigators identify suspects. With technology such as cameras, video, and social media generating massive volumes of data, AI could detect crimes that would otherwise go undetected and help ensure greater public safety by investigating potential criminal activity, thus increasing community confidence in law enforcement and the criminal justice system. AI also has the potential to assist the nations crime laboratories in areas such as complex DNA mixture analysis.

Pattern analysis of data could be used to disrupt, degrade, and prosecute crimes and criminal enterprises. Algorithms could also help prevent victims and potential offenders from falling into criminal pursuits and assist criminal justice professionals in safeguarding the public in ways never before imagined.

AI technology also has the potential to provide law enforcement with situational awareness and context, thus aiding in police well-being due to better informed responses to possibly dangerous situations. Technology that includes robotics and drones could also perform public safety surveillance, be integrated into overall public safety systems, and provide a safe alternative to putting police and the public in harms way. Robotics and drones could also perform recovery, provide valuable intelligence, and augment criminal justice professionals in ways not yet contrived.

By using AI and predictive policing analytics integrated with computer-aided response and live public safety video enterprises, law enforcement will be better able to respond to incidents, prevent threats, stage interventions, divert resources, and investigate and analyze criminal activity. AI has the potential to be a permanent part of our criminal justice ecosystem, providing investigative assistance and allowing criminal justice professionals to better maintain public safety.

On May 3, 2016, the White House announced a series of actions to spur public dialogue on artificial intelligence (AI), identify challenges and opportunities related to this technology, aid in the use of Al for more effective government, and prepare for the potential benefits and risks of Al. As part of these actions, the White House directed the creation of a national strategy for AI research and development. Following is a summary of the plans areas and intent.[24]

Manufacturing

Logistics

Finance

Transportation

Agriculture

Marketing

Communications

Science and Technology

Education

Medicine

Law

Personal Services

Security and Law Enforcement

Safety and Prediction

Return to text.

Christopher Rigano is a senior computer scientist in NIJs Office of Science and Technology.

This article was published as part of NIJ Journal issue number 280, December 2018.

This article discusses the following grants:

See the original post here:
Using Artificial Intelligence to Address Criminal Justice ...