AI The List (1)

• Resolution-robust Large Mask Inpainting with Fourier Convolutions [1]
• Stitch it in Time: GAN-Based Facial Editing of Real Videos [2]
• NeROIC: Neural Rendering of Objects from Online Image Collections [3]
• SpeechPainter: Text-conditioned Speech Inpainting [4]
• Towards real-world blind face restoration with generative facial prior [5]
• 4D-Net for Learned Multi-Modal Alignment [6]
• Instant Neural Graphics Primitives with a Multiresolution Hash Encoding [7]
• Hierarchical Text-Conditional Image Generation with CLIP Latents [8]
• MyStyle: A Personalized Generative Prior [9]
• OPT: Open Pre-trained Transformer Language Models [10]

TESOL Arabia Conference

DUBAI, 10th March, 2020 (WAM) — The 27th annual TESOL Arabia exhibition and conference, hosted by Dubai, opened today, and will continue until March 13th, under the patronage and presence of Sheikha Maryam bint Mohammed bin Ahmed bin Juma Al Maktoum.
The conference, which is attended by a specialized group of experts in English language teaching curricula in the world, will be accompanied by a specialized exhibition to introduce the latest curricula and various educational programmes.
During her opening speech, Sheikha Maryam stressed the importance of learning languages ​​to benefit from scientific and technological development, and to establish international relations with the countries of the world and openness to the world. Which reflects positively on the level of learners and the entire educational process.

Today, the TESOL Arabia International Conference was launched, with a distinguished honorary and educational presence from the UAE, represented by Sheikha Aisha bint Rashid Al Mualla, Dr. Maryam Al Hashemi from the Sheikha Salama Bint Hamdan Al Nahyan Foundation, and Dr. Samira Abdullah Al Hosani, Director of Humanities and Languages ​​Curricula from the Ministry of Education in the UAE. United Arab Emirates, and a number of doctors and professors in the educational staff.

In a keynote speech, Dr. Joyce Keeling, President of the Global TESOL Conference in the United States of America, said: “Since the beginning of the century, globalization and the internationalization of education have been on the rise, and this shift in educational goals and policies has led to an increase in the mobility of researchers, teachers, and students with this increase..and the focus on International classrooms, and the use of English as the academic lingua franca of a closely multilingual and multicultural class..However, the challenges of the past few years have led to dramatic changes in student mobility for education..The current situation regarding mobility and internationalization has prompted us to reconsider the classroom. international and English language needs of both students and staff”.
Musabah Mohammed Khalifa Al Kaabi, President of the TESOL Arabia International Conference in the United Arab Emirates, stressed the importance of holding the conference and the accompanying exhibition. To view the latest teaching methods and the latest publications related to learning the English language in light of the technological acceleration in developing pathways for the transfer, dissemination and localization of knowledge around the world.

Ms. Rania Bashar Sabry, Executive Director of the TESOL Global Conference, stated that the success of this conference is reflected in the full support of the wise leadership and encouragement to make the UAE a platform for knowledge towards the world.
She noted that the presence of HE Dr. Joyce Kling, President of the International TESOL Association, represents moral support for holding the conference in a leading Arab country.
She added: “We seek that the success of the twenty-seventh conference, which was launched in Dubai, be a direct reflection in mapping out new educational methodological concepts that contribute to reaching the finest concepts of teaching methods in the world, which has become a small village.” She also noted that it will be held annually and in a sustainable manner. Every year to learn about new innovations and axes of knowledge importance, aiming to invest science in guiding the minds of young people to raise the level of our beloved emirates under the wise leadership represented by His Highness Sheikh Mohammed bin Zayed Al NahyanThe President of the State, “may God protect him,” and His Highness Sheikh Mohammed bin Rashid Al Maktoum, Vice President, Prime Minister and Ruler of Dubai, “may God protect him.”

In its current session, the conference will discuss the most important issues related to teachers and teaching, and the challenges facing the educational process, in the midst of changes and rapid technological development, and how to keep pace with it. Which requires the continuous development of teachers and supervisors in the educational field.

Blueprint for an AI Bill of Rights

Among the great challenges posed to democracy today is the use of technology, data, and automated systems in ways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and prevent our access to critical resources or services. These problems are well documented. In America and around the world, systems supposed to help with patient care have proven unsafe, ineffective, or biased. Algorithms used in hiring and credit decisions have been found to reflect and reproduce existing unwanted inequities or embed new harmful bias and discrimination. Unchecked social media data collection has been used to threaten people’s opportunities, undermine their privacy, or pervasively track their activity—often without their knowledge or consent.

These outcomes are deeply harmful—but they are not inevitable. Automated systems have brought about extraordinary benefits, from technology that helps farmers grow food more efficiently and computers that predict storm paths, to algorithms that can identify diseases in patients. These tools now drive important decisions across sectors, while data is helping to revolutionize global industries. Fueled by the power of American innovation, these tools hold the potential to redefine every part of our society and make life better for everyone.

This important progress must not come at the price of civil rights or democratic values, foundational American principles that President Biden has affirmed as a cornerstone of his Administration. On his first day in office, the President ordered the full Federal government to work to root out inequity, embed fairness in decision-making processes, and affirmatively advance civil rights, equal opportunity, and racial justice in America.[i] The President has spoken forcefully about the urgent challenges posed to democracy today and has regularly called on people of conscience to act to preserve civil rights—including the right to privacy, which he has called “the basis for so many more rights that we have come to take for granted that are ingrained in the fabric of this country.”[ii]

To advance President Biden’s vision, the White House Office of Science and Technology Policy has identified five principles that should guide the design, use, and deployment of automated systems to protect the American public in the age of artificial intelligence. The Blueprint for an AI Bill of Rights is a guide for a society that protects all people from these threats—and uses technologies in ways that reinforce our highest values. Responding to the experiences of the American public, and informed by insights from researchers, technologists, advocates, journalists, and policymakers, this framework is accompanied by From Principles to Practice—a handbook for anyone seeking to incorporate these protections into policy and practice, including detailed steps toward actualizing these principles in the technological design process. These principles help provide guidance whenever automated systems can meaningfully impact the public’s rights, opportunities, or access to critical needs.

Safe and Effective Systems

Algorithmic Discrimination Protections

Data Privacy

Notice and Explanation

Human Alternatives, Consideration, and FallbackApplying the Blueprint for an AI Bill of RightsDownload the Blueprint for an AI Bill of Rights

Safe and Effective Systems

You should be protected from unsafe or ineffective systems. Automated systems should be developed with consultation from diverse communities, stakeholders, and domain experts to identify concerns, risks, and potential impacts of the system. Systems should undergo pre-deployment testing, risk identification and mitigation, and ongoing monitoring that demonstrate they are safe and effective based on their intended use, mitigation of unsafe outcomes including those beyond the intended use, and adherence to domain-specific standards. Outcomes of these protective measures should include the possibility of not deploying the system or removing a system from use. Automated systems should not be designed with an intent or reasonably foreseeable possibility of endangering your safety or the safety of your community. They should be designed to proactively protect you from harms stemming from unintended, yet foreseeable, uses or impacts of automated systems. You should be protected from inappropriate or irrelevant data use in the design, development, and deployment of automated systems, and from the compounded harm of its reuse. Independent evaluation and reporting that confirms that the system is safe and effective, including reporting of steps taken to mitigate potential harms, should be performed and the results made public whenever possible.

From Principles to Practice: Safe and Effective Systems

Algorithmic Discrimination Protections

You should not face discrimination by algorithms and systems should be used and designed in an equitable way. Algorithmic discrimination occurs when automated systems contribute to unjustified different treatment or impacts disfavoring people based on their race, color, ethnicity, sex (including pregnancy, childbirth, and related medical conditions, gender identity, intersex status, and sexual orientation), religion, age, national origin, disability, veteran status, genetic information, or any other classification protected by law. Depending on the specific circumstances, such algorithmic discrimination may violate legal protections. Designers, developers, and deployers of automated systems should take proactive and continuous measures to protect individuals and communities from algorithmic discrimination and to use and design systems in an equitable way. This protection should include proactive equity assessments as part of the system design, use of representative data and protection against proxies for demographic features, ensuring accessibility for people with disabilities in design and development, pre-deployment and ongoing disparity testing and mitigation, and clear organizational oversight. Independent evaluation and plain language reporting in the form of an algorithmic impact assessment, including disparity testing results and mitigation information, should be performed and made public whenever possible to confirm these protections.

From Principles to Practice: Algorithmic Discrimination Protections

Data Privacy

You should be protected from abusive data practices via built-in protections and you should have agency over how data about you is used. You should be protected from violations of privacy through design choices that ensure such protections are included by default, including ensuring that data collection conforms to reasonable expectations and that only data strictly necessary for the specific context is collected. Designers, developers, and deployers of automated systems should seek your permission and respect your decisions regarding collection, use, access, transfer, and deletion of your data in appropriate ways and to the greatest extent possible; where not possible, alternative privacy by design safeguards should be used. Systems should not employ user experience and design decisions that obfuscate user choice or burden users with defaults that are privacy invasive. Consent should only be used to justify collection of data in cases where it can be appropriately and meaningfully given. Any consent requests should be brief, be understandable in plain language, and give you agency over data collection and the specific context of use; current hard-to-understand notice-and-choice practices for broad uses of data should be changed. Enhanced protections and restrictions for data and inferences related to sensitive domains, including health, work, education, criminal justice, and finance, and for data pertaining to youth should put you first. In sensitive domains, your data and related inferences should only be used for necessary functions, and you should be protected by ethical review and use prohibitions. You and your communities should be free from unchecked surveillance; surveillance technologies should be subject to heightened oversight that includes at least pre-deployment assessment of their potential harms and scope limits to protect privacy and civil liberties. Continuous surveillance and monitoring should not be used in education, work, housing, or in other contexts where the use of such surveillance technologies is likely to limit rights, opportunities, or access. Whenever possible, you should have access to reporting that confirms your data decisions have been respected and provides an assessment of the potential impact of surveillance technologies on your rights, opportunities, or access.

From Principles to Practice: Data Privacy

Notice and Explanation

You should know that an automated system is being used and understand how and why it contributes to outcomes that impact you. Designers, developers, and deployers of automated systems should provide generally accessible plain language documentation including clear descriptions of the overall system functioning and the role automation plays, notice that such systems are in use, the individual or organization responsible for the system, and explanations of outcomes that are clear, timely, and accessible. Such notice should be kept up-to-date and people impacted by the system should be notified of significant use case or key functionality changes. You should know how and why an outcome impacting you was determined by an automated system, including when the automated system is not the sole input determining the outcome. Automated systems should provide explanations that are technically valid, meaningful and useful to you and to any operators or others who need to understand the system, and calibrated to the level of risk based on the context. Reporting that includes summary information about these automated systems in plain language and assessments of the clarity and quality of the notice and explanations should be made public whenever possible.

From Principles to Practice: Notice and Explanation

Human Alternatives, Consideration, and Fallback

You should be able to opt out, where appropriate, and have access to a person who can quickly consider and remedy problems you encounter. You should be able to opt out from automated systems in favor of a human alternative, where appropriate. Appropriateness should be determined based on reasonable expectations in a given context and with a focus on ensuring broad accessibility and protecting the public from especially harmful impacts. In some cases, a human or other alternative may be required by law. You should have access to timely human consideration and remedy by a fallback and escalation process if an automated system fails, it produces an error, or you would like to appeal or contest its impacts on you. Human consideration and fallback should be accessible, equitable, effective, maintained, accompanied by appropriate operator training, and should not impose an unreasonable burden on the public. Automated systems with an intended use within sensitive domains, including, but not limited to, criminal justice, employment, education, and health, should additionally be tailored to the purpose, provide meaningful access for oversight, include training for any people interacting with the system, and incorporate human consideration for adverse or high-risk decisions. Reporting that includes a description of these human governance processes and assessment of their timeliness, accessibility, outcomes, and effectiveness should be made public whenever possible.

From Principles to Practice: Human Alternatives, Consideration, and Fallback

Applying the Blueprint for an AI Bill of Rights

While many of the concerns addressed in this framework derive from the use of AI, the technical capabilities and specific definitions of such systems change with the speed of innovation, and the potential harms of their use occur even with less technologically sophisticated tools.

Thus, this framework uses a two-part test to determine what systems are in scope. This framework applies to (1) automated systems that (2) have the potential to meaningfully impact the American public’s rights, opportunities, or access to critical resources or services. These Rights, opportunities, and access to critical resources of services should be enjoyed equally and be fully protected, regardless of the changing role that automated systems may play in our lives.

This framework describes protections that should be applied with respect to all automated systems that have the potential to meaningfully impact individuals’ or communities’ exercise of:

Rights, Opportunities, or Access

Civil rights, civil liberties, and privacy, including freedom of speech, voting, and protections from discrimination, excessive punishment, unlawful surveillance, and violations of privacy and other freedoms in both public and private sector contexts;

Equal opportunities, including equitable access to education, housing, credit, employment, and other programs; or,

Access to critical resources or services, such as healthcare, financial services, safety, social services, non-deceptive information about goods and services, and government benefits.

A list of examples of automated systems for which these principles should be considered is provided in the Appendix. The Technical Companion, which follows, offers supportive guidance for any person or entity that creates, deploys, or oversees automated systems.

Considered together, the five principles and associated practices of the Blueprint for an AI Bill of Rights form an overlapping set of backstops against potential harms. This purposefully overlapping framework, when taken as a whole, forms a blueprint to help protect the public from harm. The measures taken to realize the vision set forward in this framework should be proportionate with the extent and nature of the harm, or risk of harm, to people’s rights, opportunities, and access.

The Full List of AI Tools in Education

• Resolution-robust Large Mask Inpainting with Fourier Convolutions [1]
• Stitch it in Time: GAN-Based Facial Editing of Real Videos [2]
• NeROIC: Neural Rendering of Objects from Online Image Collections [3]
• SpeechPainter: Text-conditioned Speech Inpainting [4]
• Towards real-world blind face restoration with generative facial prior [5]
• 4D-Net for Learned Multi-Modal Alignment [6]
• Instant Neural Graphics Primitives with a Multiresolution Hash Encoding [7]
• Hierarchical Text-Conditional Image Generation with CLIP Latents [8]
• MyStyle: A Personalized Generative Prior [9]
• OPT: Open Pre-trained Transformer Language Models [10]
• BlobGAN: Spatially Disentangled Scene Representations [11]
• A Generalist Agent [12]
• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding [13]
• Dalle mini [14]
• No Language Left Behind: Scaling Human-Centered Machine Translation [15]
• Dual-Shutter Optical Vibration Sensing [16]
• Make-a-scene: Scene-based text-to-image generation with human priors [17]
• BANMo: Building Animatable 3D Neural Models from Many Casual Videos [18]
• High-resolution image synthesis with latent diffusion models [19]
• Panoptic Scene Graph Generation [20]
• An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion [21]
• Expanding Language-Image Pretrained Models for General Video Recognition [22]
• MAKE-A-VIDEO: TEXT-TO-VIDEO GENERATION WITHOUT TEXT-VIDEO DATA [23]
• Robust Speech Recognition via Large-Scale Weak Supervision [24]
• DreamFusion: Text-to-3D using 2D Diffusion [25]
• Imagic: Text-Based Real Image Editing with Diffusion Models [26]
• eDiffi: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers [27]
• InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images [28]
• Galactica: A Large Language Model for Science [29]
• Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition [30]
• ChatGPT: Optimizing Language Models for Dialogue [31]
• Production-Ready Face Re-Aging for Visual Effects [32]
• Paper references

The end of the high school essay

Good riddance.

There’s not a lot of evidence that getting good at writing book reports or regurgitated essays under typical high school conditions leads people to success or happiness later in life.

When typing became commonplace, handwriting was suddenly no longer a useful clue about the background or sophistication of the writer. Some lamented this, others decided it opened the door for a whole new opportunity for humans to make an impact, regardless of whether they went to a prep school or not.

New York City schools are trying to ban GPT3 because it’s so good at writing superficial essays that it undermines the command structure of the essay as a sorting tool. An easy thing to assign (and a hard thing to grade) just became an easy task to hack.

High school essays had a huge range of problems, and banning the greatest essay device since Danny Dunn and his Homework Machine is not the answer. In fact, it’s a great opportunity to find a better way forward.

The first challenge of the essay was the asymmetrical difficulty in giving useful feedback. 30 essays, 5 minutes each, do the math. It doesn’t scale, and five minutes isn’t even close to enough time to honor the two hours you asked a student to put into the work.

As a result, the superficial inspection system led to the second challenge: Students get more points for good typing and clear sentence structure than they did for actually thinking deeply, questioning the status quo or changing their minds. If you grew up in a household with verbally agile family members, you probably did way better on essays than your peers, but not due to much effort on your own.

The third challenge was the lack of clarity about why we were even bothering to have kids write essays. Clearly there wasn’t an essay shortage. Ostensibly, it was either to prove that they read what they were supposed to read, or that they were able to create cogent and persuasive arguments and analysis. Essays were a signal that you could read and you could think.

Except…

They were actually a signal that you could do just enough work to persuade an overwhelmed teacher that you were compliant.

So, now that a simple chat interface can write a better-than-mediocre essay on just about any topic for just about any high school student, what should be done?

The answer is simple but difficult: Switch to the Sal Khan model. Lectures at home, classes are for homework.

When we’re on our own, our job is to watch the best lecture on the topic, on YouTube or at Khan Academy. And in the magic of the live classroom, we do our homework together.

In a school that’s privileged enough to have decent class sizes and devices in the classroom, challenge the students to actually discuss what they’ve read or learned. In real-time, teach them to not only create arguments but to get confident enough to refute them. Not only can the teacher ask a student questions, but groups of students can ask each other questions. Sure, they can use GPT or other tools to formulate where they begin, but the actual work is in figuring out something better than that.

At first, this is harder work for the teacher, but in fact, it’s what teachers actually signed up to do when they become teachers.

This is far less cohesive and controllable than the industrial model of straight rows and boring lectures. It will be a difficult transition indeed. But it’s simple to think about: If we want to train people to take initiative, to question the arguments of others, to do the reading and to create, perhaps the best way to do that is to have them do that.

We’ll never again need to hire someone to write a pretty good press release, a pretty good medical report or a pretty good investor deck. Those are instant, free and the base level of mediocre. The opportunity going forward remains the same: Bringing insight and guts to interesting problems.

8 Ways AI is Used in Education

While AI has been in the education technology space for a while, adoption has been slow. However, during the COVID-19 pandemic, virtual learning forced the industry to shift. AI helps streamline the student education process by offering access to suitable courses, bettering communication with tutors, and giving them more time to focus on other life aspects.

AI enhances the personalization of student learning programs and courses, promotes tutoring by helping students improve their weak spots and sharpen their skills, ensures quick responses between teachers and students, and enhances universal 24/7 learning access. Educators can use AI for task automation, including administrative work, evaluating learning patterns, grading papers, responding to general queries, and more. Here are eight ways AI is used in education.

1. Creating courses

A lot of time and money goes into creating learning courses via a central department. The use of AI streamlines the course creation process, speeding up the process and reducing costs. Whether you’re using premade templates or starting from scratch,AI software for creating courses can help create interactive content seamlessly. You can efficiently work with your entire team via in-app comments from reviewers and co-authors to create perfect training material.

AI simplifies and accelerates course development. By assessing student learning history and abilities, AI gives teachers a clear picture of the lessons and subjects requiring reevaluation. Teachers alter their courses by evaluating every student’s specific needs to address common knowledge gaps. This enables teachers to develop the best learning programs for all students.

2. Offering personalized learning

Personalization is a significant trend in education. AI gives students a customized learning approach depending on their unique preferences and experiences. AI can adjust to every student’s knowledge level, desired goals, and learning speed to help get the most out of their learning. Additionally, AI-powered solutions can assess a student’s learning history, pinpoint weaknesses, and provide courses suitable for improvement, offering many opportunities for personalized learning experiences.

3. Enabling universal access

AI breaks down the silos between schools and traditional grade levels. Through AI tools, classrooms are now globally available to students, including those with visual or hearing impairment or who use different languages. Using a PowerPoint plugin like Presentation Translator, learners get real-time subtitles for all the teacher says, opening up new possibilities for the learners who have to learn at varying levels, want to learn subjects that aren’t in their school, or are absent from school.

4.  Pinpointing where courses should be improved

Teachers may not always know the gaps in their educational materials and lectures, which can confuse learners regarding particular concepts. AI provides a way to solve this issue. For instance, Coursera is already applying this. When many students give the wrong answers to their homework assignments, the system alerts the professor and offers future students customized messages that provide hints to the correct answer.

This kind of system fills the gaps in explanation in courses and ensures every student is building a similar conceptual foundation. Instead of waiting to hear from the teacher, students receive immediate feedback to help them understand concepts better.

5. Automation tasks

Teachers usually have a lot to manage, including classes and other administrative and organizational tasks. They grade tests, evaluate homework, fill the needed paperwork, make progress reports, organize lecture resources and materials, manage teaching materials, and more. This means they might spend too much time on non-teaching activities, leaving them overwhelmed. With the help of automation tools and solutions, educators can automate manual processes giving them more time to concentrate on teaching key competencies.

6. Providing tutoring support

Intelligent tutoring systems, including AI chatbots and tutors, and tutoring programs are designed to handle customized feedback and guidelines for one-on-one teaching. Nonetheless, they can’t replace teachers because they aren’t advanced enough to teach the way humans can. They help in cases where teachers aren’t available for subjects that can be taught and assessed online.

AI is an effective tool that e-learning platforms can use to teach geography, languages, circuits, computer programming, medical diagnosis, physics, mathematics, chemistry, genetics, and more. They’re equipped to consider engagement, grading metrics, and comprehension. AI tools help students sharpen their skills while improving weak areas outside the classroom.

7. Promoting virtual learning

A virtual learning environment can provide group educational experiences, offer counseling services to students, and facilitate immersive learning experiences. With VR technologies, learners can directly connect their laptops or mobile devices to access the content. Using VR headsets, students with ADHD/ ADD can block distractions and increase concentration spans. In addition, students can help others in soft skill coaching, self-development, and life skills with interactive simulations.

8. Creating smart content

Smart content may include digital guides, textbooks, videos, instructional snippets, and AI, which develop customized environments for learning organizations depending on goals and strategies. Personalization in the education sector is a future world trend that can be achieved by pinpointing the areas where AI solutions can play a role. For instance, an educational institution can establish an AR/VR-based learning environment and web-based lessons to go with it.

Artificial Intelligence: Underlining The 7 Most Common Ethical Issues

Ever since the world has stepped forward towards the age of digitalization, things have never been the same. From the introduction of the internet to the expansion of the mobile-first concept and innovations like artificial intelligence and machine learning, people have experienced the highest exposure to technology ever.

Amidst all this development and expansion, one thing that has scaled dynamically is Artificial Intelligence. From the expansion of neural networks to energy use, data sets, and the prevalence of society, the growth of AI has made way for significant ethical concerns.

Before we jump on unraveling the most common ethical issues surrounding artificial intelligence, let us begin with developing an understanding of what ethical AI is.

What is ethical AI?

When it comes to “Ethics in AI”, the term means to investigate and constantly question the technologies that can hamper human life. Be it replacing humans with smart machines or concerns related to sharing personal information on AI-powered systems, the concept of ethical AI has gained all the pace due to the rapid scaling of AI technologies.

From computing power to data fed, AI systems have grown tremendously big in the past few years. Moreover, the rapid growth of AI has even dwarfed the potential of computing that was carried back from the era of the internet and PCs.

The extensive scale of deployment and responsibilities given to AI has now even involved other aspects of technology in the picture. Be it deep learning or scaling of any other advanced technologies that involve the use of AI, the situation has escaped the comprehension capabilities of even the most proficient practitioners.

And therefore, ethical AI brings some really interesting and important factors to the light that need immediate consideration in order to overcome ethical concerns surrounding AI technology:

1. Biases

From the training artificial intelligence algorithms to removing the bias involved, a huge amount of data is needed. Consider an example of any application made to allow editing of pictures. These applications are made to use AI to beautify the pictures and therefore contain a vast amount of data that has more white faces over non-white faces.

Therefore, it is necessary that AI algorithms must be trained to recognize and process non-white faces as efficiently as it does for white faces. The process requires feeding the right balance of faces to the database in order to ensure the algorithm works well to cut the built-in bias for beauty apps.

In other words, eliminating bias is extremely necessary when we need to create technology that can reflect our society with greater precision. Such actions thus require identifying all the potential areas of bias and fixing the AI solutions with the right approach.

2. Infusing Morality, Loss of Control

With more and more use of artificial intelligence, machines are capable of making important decisions. Be it the use of drones for delivery by carrier services or building autonomous rockets/missiles that can potentially kill a banned object. However, there is still a need for human involvement in such decision-making that can work on any rules and regulations that can impact humanity in any form.

The concern here is actually allowing AI to work on quick decisions. However, in operations like financial trading where it is essential to make split-second decisions, giving control to humans leaves no chance to make the right move at the right time.

Another example of the same is autonomous cars as they are made to make immediate reactions to take control of situations. The problem with all these scenarios is the ethical challenge of establishing a balance between control and AI.

3. Privacy

One of the most significant ethical concerns that have been long associated with AI is Privacy. There are many ethical concerns, from training AIs to the use of data and its source. Oftentimes, it is assumed that the data is coming from adults with high mental capabilities making the data used for creating AI that can work on choices. However, the situation is not always the same.

A quick example of the same can be the use of AI-powered toys that are designed to converse with children. Here the ethical concerns are about the algorithms collecting data from those conversations and making way for queries like where and how this data is being used.

The ethical concerns with such conversations grow even bigger when it comes to companies collecting that data and selling it to other companies. There is a need for rules that can justify data collection.

Moreover, there must be strict legislation made to protect the user’s privacy as an object that can collect data from conversations with children could potentially be used for taping the conversations of adults within the same environment.

4. Power Balance

The next significant ethical issue that comes with AI is giants like Amazon and Google using technology to dominate their competitors. More importantly, there are countries like China and Russia competing in the AI landscape, and here the question arises of the power balance.

From equal wealth generation to balancing monopolies, it is very likely that countries that are ahead of AI development and implementation are likely to race ahead of others.

For instance, countries with better access to resources that can develop and implement AI could utilize its power to innovate their war strategies, finance building, and more. Thus, AI creates some serious gaps around the subject of power balance.

5. Ownership

At number five, we have another big ethical challenge that needs to identify people or organizations that can be held accountable for things that AI creates. As Artificial Intelligence has all the potential to develop texts, bots, and video content, it is likely to create things that are misleading. Such material could trigger any violent circumstances for a particular community, ethnicity, or belief and therefore it becomes necessary to understand who could take ownership of the content.

Another example of the same could be AIs that are used to create music pieces or art. Thus, it is necessary that any new piece of content developed with AI that reaches some audience must have some ownership or could have intellectual property rights.

6. Environmental Concerns

Most of the time, the companies working on AI are not so concerned about how AI could impact the environment. Developers working on AI assume that they are using data on the cloud to work on their algorithm and then the data works on, say creating suggestions, recommendations, or making automated decisions. Though the systems are running efficiently and effectively, the computers that are keeping up the AI and cloud infrastructure require immense power.

A quick example of the impact that AI could create on the environment is the fact that training in AI could create 17 times more carbon emissions than an average American does in a year. Therefore, it is important that developers find ways to use this energy for other productive purposes and get over one of the most pressing problems of declining energy resources.

7. Humanity

Last but not least, it is the challenge of how humans feel in the presence of AI. Especially, when AI has been developed to be so powerful and efficient, it triggers the challenge for humans missing the feeling of how it actually feels to be human. As AI is designed and created to work on precision, it diminishes the human morale built through making errors and learning from it.

Especially, when AI has automated jobs for so long, it often leads to the question that what contributions human beings could make to the technology landscape. Though it is not possible for AI to replace humans for all jobs, only the idea of augmenting AI possesses some serious challenges.

To conclude

Humans need to get better when working along with smart machines in order to align with the tech transition. Besides, it is extremely necessary for the people to sustain their dignity and have respect for technology. Therefore, it is necessary that all the ethical challenges surrounding AI must be understood.

Especially, when AI is seen as a technology that has all the capability to create user-oriented and sustainable IT solutions, creating ethical AI could help empower digitalization. Be it advancing the process through AI improved Quality Assurance and software testing or using AI itself to create unbiased technology for users across the world.

More importantly, it is crucial that engineers working on AI technology should always have consideration for the human aspect of using AI. Be it the use of AI machines or software, it is vital that transparency should be maintained with respect to user data consumption and human involvement in decision making, the privacy of data, no biases, and the power balance.

Even if the thought of AI systems surpassing human intelligence may appear scary, the key is to have an early vision of all the ethical issues surrounding AI adoption. It not only needs humans to keep on learning but stay informed of the impact that any potential implementations related to AI could have on society.

The future of AI in Test Prep.

Since Socrates taught Plato and Plato taught Aristotle, humanity has known that the best education is delivered one-to-one by an experienced educator. But that is expensive, labor-intensive and difficult to scale. The result is the imperfect classroom-based instruction that we live with today: large class sizes, overworked and overloaded teachers, a deficiency of resources. Educators focus what little time they have for personal attention either on the best and the brightest or on the bottom of the class. The broad middle is often left to fend for itself.

Educators may have a new tool, A.I., to address those issues. Innovative forms of the technology, based on computer code that mimics the networks of neurons in the human brain, can uncover patterns in how students perform and can help teachers adjust their strategies accordingly. “A.I. tutors” — software systems that students interact with online — promise to give every student individualized attention, potentially remaking education as we know it.

Among the handful of companies leading that transformation is Riiid (pronounced “rid”), a start-up founded in Korea by YJ Jang, a graduate of Haas School of Business at the University of California, Berkeley. Riiid already has a strong presence in the Asian test-prep app market for the Test of English for International Communication, or TOEIC, which measures English-language proficiency for business. Now, Riiid is about to enter the SAT and ACT prep market in the United States.

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Program to Relieve Student Debt Proves Unforgiving

When Bonnie Svitavsky first heard about the Public Service Loan Forgiveness program, she thought she had finally found a way out of the large debt she had taken on to become a public librarian.

Congress created the program in 2007 with people like her in mind. The goal was to lure people into professions like teaching, nursing or public-interest law, where pricey degrees are the ticket of entry but wages typically aren’t high enough to pay them off.

More than a decade later, now that the first borrowers are eligible, the program is in disarray. More than 73,000 people have applied for debt forgiveness as of March 31 of this year, according to Education Department data, but just 864 have had their loans erased.

Ms. Svitavsky completed two master’s programs, creative writing and library sciences, which along with her bachelor’s degree left her with more than $97,000 in loans by the time she began her job at the Puyallup Public Library just outside Tacoma, Wash., in 2008. She consolidated those loans, per the program’s requirements, and began following the steps toward debt forgiveness.

Or she thought she had.

“It’s deeply frustrating, because you know you’re done, and you’ve jumped through all these hoops,” Ms. Svitavsky said. “It feels like a broken promise.”

A mix of factors combined to derail the program, including poorly written legislation, neglect by multiple administrations, mismanagement by servicer’s contracted to carry it out and antipathy from conservatives—particularly in the Trump administration—who would prefer the program had never been created.G

“The department’s goal of pursuing its conservative agenda is made easier by the fact that the law itself is a mess,” said Terry Hartle, senior vice president at the American Council on Education, a Washington trade group representing colleges and universities.

To qualify for forgiveness, borrowers must work for a government entity or nonprofit, hold a certain type of loan, enroll in one of several specific repayment plans and make 120 full and on-time monthly payments, or 10 years’ worth. Falling short on almost any of these requirements can mean disqualification.

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Education Department data show the rejections are prompted by a wide range of issues: 16% of denials were because borrowers had the wrong type of loan, making them permanently ineligible. Twenty-five percent were turned away due to missing information in applications, a potentially fixable error. And 53% were denied for not making enough payments, which could have been due to a simple counting error or being enrolled in an ineligible repayment plan for years.

The program’s congressional backers created one obstacle from the get-go. They required that applicants hold loans made directly from the federal government. Most student loans offered at the time were federally guaranteed but privately owned, making them much more expensive to manage.

That requirement predictably tripped up initial borrowers. Law schools around the country advertised the program to their students, but many offered only private loans in their financial-aid packages.

Congress went on to eliminate federally guaranteed private loans entirely in 2010, making all future loans eligible for public-service discharge, but private loans taken out before that time remained ineligible.

At that point, with the first borrowers not eligible for forgiveness for seven years, the Obama administration put off specific steps that would have helped the program run smoothly. Officials didn’t advertise the program or establish a platform to guide borrowers through its requirements. They didn’t draw up clear guidance on which employers should qualify as public-service organizations—now a subject of litigation. A government investigation last year found that officials didn’t even produce a guidebook for the servicing company they hired, Fed Loan, to implement the program.

“We had a lot else going on at the time, and this had not really appeared on our radar as a major problem that needed attention,” said Spiros Protopsaltis, a senior education policy adviser in the Obama administration. “We did some things, but clearly it wasn’t enough.”

Borrowers, meanwhile, also accused several of the companies charged with handling their loans with allowing them to enroll in ineligible repayment plans, though they had mentioned an interest in public-service loan forgiveness. Others made payments that were slightly too small—sometimes off by cents—that disqualified them from the minimum payment count. Still others were initially told their employers qualified, only to be told by the Education Department later that the servicing company had made an error.

Ms. Svitavsky hit her first snag in 2013, when she submitted a form to ensure her employer qualified her for loan forgiveness. It did, but that step revealed another problem: For the prior 23 months, her servicer, like with so many other borrowers, had her on a plan known as extended repayment, which charges standard monthly payments over 25 years. Those payments were now all ineligible toward her payment count.

The improper payment plan issue raised particular concern in Washington, where members of Congress, led by Sen. Elizabeth Warren (D., Mass.), in 2018 created a temporary fund of $700 million to reimburse borrowers who had mistakenly enrolled in ineligible repayment plans but otherwise qualified. The program has so far granted loan relief to 442 additional people.

The Trump administration, for its part, has done little more than the law requires to set up clearer processes for borrowers or the companies serving them. In its last three budget recommendations, the administration has proposed ending the program entirely.

In a hearing last month, Education Secretary Betsy DeVos criticized the program for, as she put it, elevating certain professions over others. “We don’t think that one type of a job, one type of role should be incentivized over another,” she said.

Critics on the right and the left bring up a broader contention: As more people work their way through the program’s labyrinthine requirements, some wealthier borrowers could stand to save big sums. That is because the government’s definition of public service extends to higher-paid professors at nonprofit universities and doctors at nonprofit hospitals.

“If Congress was totally honest with taxpayers about what they’re doing, which is creating a loan forgiveness program for people with master’s degrees who are fully employed.…I don’t think Congress could get away with doing that,” said Jason Delisle, a resident fellow at the American Enterprise Institute.

As for Ms. Svitavsky, who now earns $72,000 a year, the establishment of the congressional reimbursement fund last year means her 23 ineligible payments now count. In August 2018, she filed for forgiveness of her remaining roughly $80,000 in loans.

She was rejected.

The problem: Three different servicing companies had handled Ms. Svitavsky’s loan, and each time her account was transferred, her automatic payments hadn’t been made, which she didn’t notice.

She made three additional payments and tried again in November.

Again, her application was rejected.

This time, Ms. Svitavsky couldn’t find the flaw. She counted 121 qualifying payments. A review of her documents by The Wall Street Journal and two independent higher-education analysts confirmed her count.

A customer-service official with Fed Loan agreed with Ms. Svitavsky’s contention and appealed her case.

She is still waiting.