Biased Data Isn't the Only Source of AI Bias.

 

In order to eliminate prejudice in artificial intelligence, it will be necessary to address both human and systemic biases. 

Bias in AI systems is often seen as a technological issue, but the NIST study recognizes that human prejudices, as well as systemic, institutional biases, have a role. 

Researchers at the National Institute of Standards and Technology (NIST) recommend broadening the scope of where we look for the source of these biases — beyond the machine learning processes and data used to train AI software to the broader societal factors that influence how technology is developed — as a step toward improving our ability to identify and manage the harmful effects of bias in artificial intelligence (AI) systems. 

The advice is at the heart of a new NIST article, Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (NIST Special Publication 1270), which incorporates feedback from the public on a draft version issued last summer. 

The publication provides guidelines related to the AI Risk Management Framework that NIST is creating as part of a wider effort to facilitate the development of trustworthy and responsible AI. 

The key difference between the draft and final versions of the article, according to NIST's Reva Schwartz, is the increased focus on how bias presents itself not just in AI algorithms and the data used to train them, but also in the sociocultural environment in which AI systems are employed. 

"Context is crucial," said Schwartz, one of the report's authors and the primary investigator for AI bias. 

"AI systems don't work in a vacuum. They assist individuals in making choices that have a direct impact on the lives of others. If we want to design trustworthy AI systems, we must take into account all of the elements that might undermine public confidence in AI. Many of these variables extend beyond the technology itself to its consequences, as shown by the responses we got from a diverse group of individuals and organizations." 

NIST contributes to the research, standards, and data needed to fulfill artificial intelligence's (AI) full potential as a driver of American innovation across industries and sectors. 

NIST is working with the AI community to define the technological prerequisites for cultivating confidence in AI systems that are accurate and dependable, safe and secure, explainable, and bias-free. 

AI bias is harmful to humans. 

AI may make choices on whether or not a student is admitted to a school, approved for a bank loan, or accepted as a rental applicant. 

Machine learning software, for example, might be taught on a dataset that underrepresents a certain gender or ethnic group. 

While these computational and statistical causes of bias remain relevant, the new NIST article emphasizes that they do not capture the whole story. 

Human and structural prejudices, which play a large role in the new edition, must be taken into consideration for a more thorough understanding of bias. 

Institutions that operate in ways that disfavor specific social groups, such as discriminating against persons based on race, are examples of systemic biases. 

Human biases may be related to how individuals utilize data to fill in gaps, such as a person's neighborhood impacting how likely police would consider them to be a criminal suspect. 

When human, institutional, and computational biases come together, they may create a dangerous cocktail – particularly when there is no specific direction for dealing with the hazards of deploying AI systems. 

"If we are to construct trustworthy AI systems, we must take into account all of the elements that might erode public faith in AI." 

Many of these considerations extend beyond the technology itself to the technology's consequences." —Reva Schwartz, AI bias main investigator To address these concerns, the NIST authors propose a "socio-technical" approach to AI bias mitigation. 

This approach recognizes that AI acts in a wider social context — and that attempts to overcome the issue of bias just on a technological level would fall short. 

"When it comes to AI bias concerns, organizations sometimes gravitate to highly technical solutions," Schwartz added. 

"However, these techniques fall short of capturing the social effect of AI systems. The growth of artificial intelligence into many facets of public life necessitates broadening our perspective to include AI as part of the wider social system in which it functions." 

According to Schwartz, socio-technical approaches to AI are a developing field, and creating measuring tools that take these elements into account would need a diverse mix of disciplines and stakeholders. 

"It's critical to bring in specialists from a variety of sectors, not just engineering," she added, "and to listen to other organizations and communities about the implications of AI." 

Over the next several months, NIST will host a series of public workshops aimed at creating a technical study on AI bias and integrating it to the AI Risk Management Framework.

Visit the AI RMF workshop website for further information and to register.

A Method for Reducing Artificial Intelligence Bias Risk. 

The National Institute of Standards and Technology (NIST) is advancing an approach for identifying and managing biases in artificial intelligence (AI) — and is asking for the public's help in improving it — in an effort to combat the often pernicious effect of biases in AI that can harm people's lives and public trust in AI. 

A Proposal for Identifying and Managing Bias in Artificial Intelligence (NIST Special Document 1270), a new publication from NIST, lays out the methodology. 

It's part of the agency's larger effort to encourage the development of trustworthy and responsible AI. 

NIST will welcome public comments on the paper through September 10, 2021 (an extension of the initial deadline of August 5, 2021), and the writers will utilize the feedback to help define the topic of numerous collaborative virtual events NIST will organize in the following months. 

This series of events aims to engage the stakeholder community and provide them the opportunity to contribute feedback and ideas on how to reduce the danger of bias in AI. 

"Managing the danger of bias in AI is an important aspect of establishing trustworthy AI systems, but the route to accomplishing this remains uncertain," said Reva Schwartz of the National Institute of Standards and Technology, who was one of the report's authors. 

"We intend to include the community in the development of voluntary, consensus-based norms for limiting AI bias and decreasing the likelihood of negative consequences." 

NIST contributes to the research, standards, and data needed to fulfill artificial intelligence's (AI) full potential as a catalyst for American innovation across industries and sectors. 

NIST is working with the AI community to define the technological prerequisites for cultivating confidence in AI systems that are accurate and dependable, safe and secure, explainable, and bias-free. 

Bias in AI-based goods and systems is a critical, but yet poorly defined, component of trustworthiness. 

This prejudice might be intentional or unintentional. 

NIST is working to get us closer to consensus on recognizing and quantifying bias in AI systems by organizing conversations and conducting research. 

Because AI can typically make sense of information faster and more reliably than humans, it has become a transformational technology. 

Everything from medical detection to digital assistants on our cellphones now uses AI. 

However, as AI's uses have developed, we've seen that its conclusions may be skewed by biases in the data it's given - data that either partially or erroneously represents the actual world. 

Furthermore, some AI systems are designed to simulate complicated notions that cannot be readily assessed or recorded by data, such as "criminality" or "employment appropriateness." 

Other criteria, such as where you live or how much education you have, are used as proxies for the notions these systems are attempting to mimic. 

The imperfect association of the proxy data with the original notion might result to undesirable or discriminatory AI outputs, such as wrongful arrests, or eligible candidates being erroneously refused for employment or loans. 

The strategy the authors suggest for controlling bias comprises a conscious effort to detect and manage bias at multiple phases in an AI system’s lifespan, from early idea through design to release. 

The purpose is to bring together stakeholders from a variety of backgrounds, both within and outside the technology industry, in order to hear viewpoints that haven't been heard before. 

“We want to bring together the community of AI developers of course, but we also want to incorporate psychologists, sociologists, legal experts and individuals from disadvantaged communities,” said NIST’s Elham Tabassi, a member of the National AI Research Resource Task Force. 

"We'd want to hear from individuals who are affected by AI, both those who design AI systems and those who aren't." 

Preliminary research for the NIST writers includes a study of peer-reviewed publications, books, and popular news media, as well as industry reports and presentations. 

It was discovered that bias may seep into AI systems at any level of development, frequently in different ways depending on the AI's goal and the social environment in which it is used. 

"An AI tool is often built for one goal, but it is subsequently utilized in a variety of scenarios," Schwartz said. 

"Many AI applications have also been inadequately evaluated, if at all, in the environment for which they were designed. All these elements might cause bias to go undetected.” 

Because the team members acknowledge that they do not have all of the answers, Schwartz believes it is critical to get public comment, particularly from those who are not often involved in technical conversations. 

"We'd want to hear from individuals who are affected by AI, both those who design AI systems and those who aren't." ~ Elham Tabassi.

"We know bias exists throughout the AI lifespan," added Schwartz. 

"It would be risky to not know where your model is biased or to assume that there is none. The next stage is to figure out how to see it and deal with it."

Comments on the proposed method may be provided by downloading and completing the template form (in Excel format) and emailing it to ai-bias@list.nist.gov by Sept. 10, 2021 (extended from the initial deadline of Aug. 5, 2021). 

This website will be updated with further information on the joint event series.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read and learn more Technology and Engineering here.

You may also want to read and learn more Artificial Intelligence here.

Artificial Intelligence - Emotion Recognition And Emotional Intelligence.

A group of academics released a meta-analysis of studies in 2019 indicating that a person's mood may be determined from their facial movements. 

They came to the conclusion that there is no evidence that emotional state can be predicted from expression, regardless of whether the assessment is made by a person or by technology. 

The coauthors noted, "[Facial expressions] in issue are not 'fingerprints' or diagnostic displays that dependably and explicitly convey distinct emotional states independent of context, person, or culture."

  "It's impossible to deduce pleasure from a grin, anger from a scowl, or grief from a frown with certainty." 

This statement may be disputed by Alan Cowen. He's the creator of Hume AI, a new research lab and "empathetic AI" firm coming from stealth today. He's an ex-Google scientist. 

Hume claims to have created datasets and models that "react beneficially to [human] emotion signals," allowing clients ranging from huge tech firms to startups to recognize emotions based on a person's visual, vocal, and spoken expressions. 

"When I first entered the area of emotion science, the majority of researchers were focusing on a small number of posed emotional expressions in the lab. 

Cowen told, "I wanted to apply data science to study how individuals genuinely express emotion out in the world, spanning ethnicities and cultures." 

"I uncovered a new universe of nuanced and complicated emotional behaviors that no one had ever recorded before using new computational approaches, and I was quickly publishing in the top journals." That's when businesses started contacting me." 

Hume, which has 10 workers and just secured $5 million in investment, claims to train its emotion-recognizing algorithms using "huge, experimentally-controlled, culturally varied" datasets from individuals throughout North America, Africa, Asia, and South America. 

Regardless of the data's representativeness, some experts doubt the premise that emotion-detecting algorithms have a scientific base. 

"The kindest view I have is that there are some really well-intentioned folks who are naive enough that... the issue they're attempting to cure is caused by technology," 

~ Os Keyes, an AI ethics scientist at the University of Washington. 

"Their first offering raises severe ethical concerns... It's evident that they aren't addressing the topic as a problem to be addressed, interacting deeply with it, and contemplating the potential that they aren't the first to conceive of it." 

HireVue, Entropik Technology, Emteq, Neurodata Labs, Neilson-owned Innerscope, Realeyes, and Eyeris are among the businesses in the developing "emotional AI" sector. 

Entropik says that their technology can interpret emotions "through facial expressions, eye gazing, speech tone, and brainwaves," which it sells to companies wishing to track the effectiveness of their marketing efforts. 

Neurodata created a software that Russian bank Rosbank uses to assess the emotional state of clients phoning customer support centers. 

Emotion AI is being funded by more than just startups. 

Apple bought Emotient, a San Diego company that develops AI systems to assess face emotions, in 2016. 

When Alexa senses irritation in a user's voice, it apologizes and asks for clarification. 

Nuance, a speech recognition firm that Microsoft bought in April 2021, has shown off a device for automobiles that assesses driver emotions based on facial clues. 

In May, Swedish business Smart Eye bought Affectiva, an MIT Media Lab spin-off that claimed it could identify rage or dissatisfaction in speech in 1.2 seconds. 

According to Markets & Markets, the emotion AI market is expected to almost double in size from $19 billion in 2020 to $37.1 billion in 2026. 

Hundreds of millions of dollars have been invested in firms like Affectiva, Realeyes, and Hume by venture investors eager to get in on the first floor. 

According to the Financial Times, it is being used by film companies such as Disney and 20th Century Fox to gauge public response to new series and films. 

Meanwhile, marketing organizations have been putting the technology to the test for customers like Coca-Cola and Intel to examine how audiences react to commercials. 

The difficulty is that there are few – if any – universal indicators of emotion, which calls into doubt the accuracy of emotion AI. 

The bulk of emotion AI businesses are based on psychologist Paul Ekman's seven basic emotions (joy, sorrow, surprise, fear, anger, disgust, and contempt), which he introduced in the early 1970s. 

However, further study has validated the common sense assumption that individuals from diverse backgrounds express their emotions in quite different ways. 

Context, conditioning, relationality, and culture all have an impact on how individuals react to situations. 

For example, scowling, which is commonly linked with anger, has been observed to appear on the faces of furious persons fewer than 30% of the time. 

In Malaysia, the apparently universal expression for fear is the stereotype for a threat or anger. 

• Later, Ekman demonstrated that there are disparities in how American and Japanese pupils respond to violent films, with Japanese students adopting "a whole distinct set of emotions" if another person is around, especially an authority figure. 

• Gender and racial biases in face analysis algorithms have been extensively established, and are caused by imbalances in the datasets used to train the algorithm. 

In general, an AI system that has been trained on photographs of lighter-skinned humans may struggle with skin tones that are unknown to it. 

This isn't the only kind of prejudice that exists. 

Retorio, an AI employment tool, was seen to react differently to the identical applicant wearing glasses versus wearing a headscarf. 

• Researchers from MIT, the Universitat Oberta de Catalunya in Barcelona, and the Universidad Autonoma de Madrid revealed in a 2020 study that algorithms may become biased toward specific facial expressions, such as smiling, lowering identification accuracy. 

• Researchers from the University of Cambridge and the Middle East Technical University discovered that at least one of the public datasets often used to train emotion recognition systems was contaminated. 

There are substantially more Caucasian faces in AI systems than Asian or Black ones. 

• Recent study has shown that major vendors' emotional analysis programs assign more negative feelings to Black men's faces than white men's looks, highlighting the repercussions. 

• Persons with impairments, disorders like autism, and people who communicate in various languages and dialects, such as African-American Vernacular English, all have different voices (AAVE). 

• A native French speaker doing an English survey could hesitate or enunciate a word with considerable trepidation, which an AI system might misinterpret as an emotion signal. 

Despite the faults in the technology, some businesses and governments are eager to use emotion AI to make high-stakes judgments. 

Employers use it to assess prospective workers by giving them a score based on their empathy or emotional intelligence. 

It's being used in schools to track pupils' participation in class — and even when they're doing homework at home. 

Emotion AI has also been tried at border checkpoints in the United States, Hungary, Latvia, and Greece to detect "risk persons." 

To reduce prejudice, Hume claims that "randomized studies" are used to collect "a vast variety" of facial and voice expressions from "people from a wide range of backgrounds." 

According to Cowen, the company has gathered over 1.1 million images and videos of facial expressions from over 30,000 people in the United States, China, Venezuela, India, South Africa, and Ethiopia, as well as over 900,000 audio recordings of people voicing their emotions labeled with people's self-reported emotional experiences. 

Hume's dataset is less than Affectiva's, which claimed to be the biggest of its sort at the time, with over 10 million people's expressions from 87 countries. 

Cowen, on the other hand, says that Hume's data can be used to train models to assess "an exceptionally broad spectrum of emotions," including over 28 facial expressions and 25 verbal expressions. 

"As demand for our empathetic AI models has grown, we've been prepared to provide access to them at a large scale." 

As a result, we'll be establishing a developer platform that will provide developers and researchers API documentation and a playground," Hume added. 

"We're also gathering data and developing training models for social interaction and conversational data, body language, and multi-modal expressions, which we expect will broaden our use cases and client base." 

Beyond Mursion, Hume claims it's collaborating with Hoomano, a firm that develops software for "social robots" like Softbank Robotics' Pepper, to build digital assistants that make better suggestions by taking into consideration the emotions of users. 

Hume also claims to have collaborated with Mount Sinai and University of California, San Francisco experts to investigate whether its models can detect depression and schizophrenia symptoms "that no prior methodologies have been able to capture." 

"A person's emotions have a big impact on their conduct, including what they pay attention to and click on." 

As a result, 'emotion AI' is already present in AI technologies such as search engines, social media algorithms, and recommendation systems. It's impossible to avoid. 

As a result, decision-makers must be concerned about how these technologies interpret and react to emotional signals, influencing their users' well-being in ways that their inventors are unaware of." Cowen remarked. 

"Hume AI provides the tools required to guarantee that technologies are built to increase the well-being of their users. There's no way of understanding how an AI system is interpreting these signals and altering people's emotions without means to assess them, and there's no way of designing the system to do so in a way that is compatible with people's well-being." 

Leaving aside the thorny issue of using artificial intelligence to diagnose mental disorder, Mike Cook, a Queen Mary University of London AI researcher, believes the company's message is "performative" and the language is questionable. 

"[T]hey've obviously gone to tremendous lengths to speak about diversity and inclusion and other such things, and I'm not going to whine about people creating datasets with greater geographic variety." "However, it seems a little like it was massaged by a PR person who knows how to make your organization appear to care," he remarked. 

Cowen claims that by forming The Hume Initiative, a nonprofit "committed to governing empathetic AI," Hume is taking a more rigorous look at the uses of emotion AI than rivals. 

The Hume Initiative, whose ethical committee includes Taniya Mishra, former director of AI at Affectiva, has established regulatory standards that the company claims it would follow when commercializing its innovations. 

The Hume Initiative's principles forbid uses like manipulation, fraud, "optimizing for diminished well-being," and "unbounded" emotion AI. 

It also establishes limitations for use cases such as platforms and interfaces, health and development, and education, such as mandating educators to utilize the output of an emotion AI model to provide constructive — but non-evaluative — input. 

Danielle Krettek Cobb, the creator of the Google Empathy Lab, Dacher Keltner, a professor of psychology at UC Berkeley, and Ben Bland, the head of the IEEE group establishing standards for emotion AI, are coauthors of the recommendations. 

"The Hume Initiative started by compiling a list of all known applications for empathetic AI. 

After that, they voted on the first set of specific ethical principles. 

The resultant principles are tangible and enforceable, unlike any prior attempt to AI ethics. 

They describe how empathetic AI may be used to increase mankind's finest traits of belonging, compassion, and well-being, as well as how it might be used to expose humanity to intolerable dangers," Cowen remarked. 

"Those who use Hume AI's data or AI models must agree to use them solely in accordance with The Hume Initiative's ethical rules, guaranteeing that any applications using our technology are intended to promote people's well-being." Companies have boasted about their internal AI ethical initiatives in the past, only to have such efforts fall by the wayside – or prove to be performative and ineffective. 

Google's AI ethics board was notoriously disbanded barely one week after it was established. 

Meta's (previously Facebook's) AI ethics unit has also been labeled as essentially useless in reports. 

It's referred to as "ethical washing" by some. 

Simply said, ethical washing is the practice of a firm inventing or inflating its interest in fair AI systems that benefit everyone. 

When a firm touts "AI for good" activities on the one hand while selling surveillance technology to governments and companies on the other, this is a classic example for tech titans. 

The coauthors of a report published by Trilateral Research, a London-based technology consultancy, claim that ethical principles and norms do not, by themselves, assist practitioners grapple with difficult concerns like fairness in emotion AI. 

They argue that these should be thoroughly explored to ensure that businesses do not deploy systems that are incompatible with societal norms and values. 

"Ethics is made ineffectual without a continual process of challenging what is or may be clear, of probing behind what seems to be resolved, of keeping this interrogation alive," they said. 

"As a result, the establishment of ethics into established norms and principles comes to an end." Cook identifies problems in The Hume Initiative's stated rules, especially in its use of ambiguous terminology. 

"A lot of the standards seem performatively written — if you believe manipulating the user is wrong, you'll read the guidelines and think to yourself, 'Yes, I won't do that.' And if you don't care, you'll read the rules and say, 'Yes, I can justify this,'" he explained. 

Cowen believes Hume is "open[ing] the door to optimize AI for human and societal well-being" rather than short-term corporate objectives like user engagement. 

"We don't have any actual competition since the other AI models for measuring emotional signals are so restricted." They concentrate on a small number of facial expressions, neglect the voice entirely, and have major demographic biases. 

These biases are often weaved into the data used to train AI systems. 

Furthermore, no other business has established explicit ethical criteria for the usage of empathetic AI," he said. 

"We're building a platform that will consolidate our model deployment and provide customers greater choice over how their data is utilized." 

Regardless of whether or not rules exist, politicians have already started to limit the use of emotion AI systems. 

The New York City Council recently established a regulation mandating companies to notify applicants when they are being evaluated by AI, as well as to audit the algorithms once a year. 

Candidates in Illinois must provide their agreement for video footage analysis, while Maryland has outlawed the use of face analysis entirely. 

Some firms have voluntarily ceased supplying emotion AI services or erected barriers around them. 

HireVue said that its algorithms will no longer use visual analysis. 

Microsoft's sentiment-detecting Face API, which once claimed it could detect emotions across cultures, now says in a caveat that "facial expressions alone do not reflect people's interior moods."

The Hume Initiative, according to Cook, "developed some ethical papers so people don't worry about what [Hume] is doing." 

"Perhaps the most serious problem I have is that I have no idea what they're doing." "Apart from whatever datasets they created, the part that's public doesn't appear to have anything on it," Cook added. 

Emotion recognition using AI. 

Emotion detection is a hot new field, with a slew of entrepreneurs marketing devices that promise to be able to read people's interior emotional states and AI academics attempting to increase computers' capacity to do so. 

Voice analysis, body language analysis, gait analysis, eye tracking, and remote assessment of physiological indications such as pulse and respiration rates are used to do this. 

The majority of the time, though, it's done by analyzing facial expressions. 

However, a recent research reveals that these items are constructed on a foundation of intellectual sand. 

The main issue is whether human emotions can be successfully predicted by looking at their faces. 

"Whether facial expressions of emotion are universal, whether you can look at someone's face and read emotion in their face," Lisa Feldman Barrett, a professor of psychology at Northeastern University and an expert on emotion, told me, "is a topic of great contention that scientists have been debating for at least 100 years." 

Despite this extensive history, she said that no full review of all emotion research conducted over the previous century had ever been completed. 

So, a few years ago, the Association for Psychological Science gathered five eminent scientists from opposing viewpoints to undertake a "systematic evaluation of the data challenging the popular opinion" that emotion can be consistently predicted by outward facial movements. 

According to Barrett, who was one of the five scientists, they "had extremely divergent theoretical ideas." "We arrived to the project with very different assumptions of what the data would reveal, and it was our responsibility to see if we could come to an agreement on what the data revealed and how to best interpret it." We weren't sure we could do it since it's such a divisive issue." The procedure, which was supposed to take a few months, took two years. 

Nonetheless, after evaluating over 1,000 scientific studies in the psychology literature, these experts arrived to an united conclusion: "a person's emotional state may be simply determined from his or her facial expressions" has no scientific basis. 

According to the researchers, there are three common misconceptions "about how emotions are communicated and interpreted in facial movements." 

The relationship between facial expressions and emotions is neither dependable, particular, or generalizable (i.e., the same emotions are not always exhibited in the same manner) (the effects of different cultures and contexts has not been sufficiently documented). 

"A scowling face may or may not be an indication of rage," Barrett said to me. 

People frown in rage at times, and you could grin, weep, or simply seethe with a neutral look at other moments. 

People grimace at other times as well, such as when they're perplexed, concentrating, or having gas." These results do not suggest that individuals move their faces at random or that [facial expressions] have no psychological significance, according to the researchers. 

Instead, they show that the facial configurations in issue aren't "fingerprints" or diagnostic displays that consistently and explicitly convey various emotional states independent of context, person, or culture. 

It's impossible to deduce pleasure from a grin, anger from a scowl, or sorrow from a frown, as most of today's technology seeks to accomplish when applying what are incorrectly considered to be scientific principles. 

Because an entire industry of automated putative emotion-reading devices is rapidly growing, this work is relevant. 

The market for emotion detection software is expected to reach at least $3.8 billion by 2025, according to our recent research on "Robot Surveillance." 

Emotion detection (also known as "affect recognition" or "affective computing") is already being used in devices for marketing, robotics, driving safety, and audio "aggression detectors," as we recently reported. 

Emotion identification is built on the same fundamental concept as polygraphs, or "lie detectors": that a person's internal mental state can be accurately associated with physical bodily motions and situations. 

They can't — and this includes face muscles in particular. 

It seems to reason that what is true of facial muscles would also be true of all other techniques of detecting emotion, such as body language and gait. 

However, the assumption that such mind reading is conceivable might cause serious damage. 

A jury's cultural misunderstanding of what a foreign defendant's facial expressions mean, for example, can lead to a death sentence rather than a prison sentence. 

When such mindset is translated into automated systems, it may lead to further problems. 

For example, a "smart" body camera that incorrectly informs a police officer that someone is hostile and angry might lead to an unnecessary shooting. 

"There is no automatic emotion identification. 

The top algorithms can confront a face — full frontal, no occlusions, optimal illumination — and are excellent at recognizing facial movements. 

They aren't able, however, to deduce what those facial gestures signify."

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See Also: 

AI Emotions, AI Emotion Recognition, AI Emotional Intelligence, Surveillance Technologies, Privacy and Technology, AI Bias, Human Rights.

Download PDF: 

• Feasibility of AI Emotion Recognition and Emotional Intelligence.

Artificial Intelligence - What Is Automatic Film Editing?

  

Automatic film editing is a method of assembling full motion movies in which an algorithm, taught to obey fundamental cinematography standards, cuts and sequences footage.

Automated editing is part of a larger endeavor, known as intelligent cinematography, to include artificial intelligence into filmmaking.

Alfred Hitchcock, the legendary director, predicted that an IBM computer will one day be capable of converting a written script into a polished picture in the mid-1960s.

Many of the concepts of modern filmmaking were created by Alfred Hitchcock.

His argument that, if feasible, the size of a person or item in frame should be proportionate to their importance in the plot at that precise moment in time is one well-known rule of thumb.

"Exit left, enter right," which helps the audience follow lateral motions of actors on the screen, and the 180 and 30-degree principles for preserving spatial connections between subjects and the camera, are two more film editing precepts that arose through extensive experience by filmmakers.

Over time, these principles evolved into heuristics that regulate shot selection, editing, and rhythm and tempo.

Joseph Mascelli's Five C's of Cinematography (1965), for example, has become a large knowledge base for making judgments regarding camera angles, continuity, editing, closeups, and composition.

These human-curated guidelines and human-annotated movie stock material and snippets gave birth to the first artificial intelligence film editing systems.

IDIC, created by Warren Sack and Marc Davis at the MIT Media Lab in the early 1990s, is an example of a system from that era.

IDIC is based on Herbert Simon, J. C. Shaw, and Allen Newell's General Issue Solver, an early artificial intelligence software that was supposed to answer any general problem using the same fundamental method.

IDIC was used to create fictitious Star Trek television trailers based on a human-specified narrative plan focusing on a certain plot element.

Several film editing systems depend on idioms, or standard techniques for editing and framing recorded action in certain contexts.

The idioms themselves will differ depending on the film's style, the setting, and the action to be shown.

In this manner, experienced editors' expertise may be accessed using case-based reasoning, with prior editing recipes being used to tackle comparable present and future challenges.

Editing for combat sequences, like regular character talks, follows standard idiomatic route methods.

This is the method used by Li-wei He, Michael F. Cohen, and David H. Salesin in their Virtual Cinema tographer, which uses expert idiom knowledge in the editing of fully computer-generated video for interactive virtual environments.

He's group created the Declarative Camera Control Language (DCCL), which formalizes the control of camera locations in the editing of CGI animated films to match cinematographic traditions.

Researchers have lately begun experimenting with deep learning algorithms and training data extracted from existing collections of well-known films with good cinematographic quality to develop recommended best cuts of new films.

Many of the latest apps may be used with mobile, drone, or portable devices.

Short and interesting films constructed from pictures taken by amateurs with smartphones are projected to become a preferred medium of interaction over future social media due to easy automated video editing.

Photography is presently filling that need.

In machinima films generated with 3D virtual game engines and virtual actors, automatic film editing is also used as an editing technique.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Workplace Automation.

Further Reading

Galvane, Quentin, Rémi Ronfard, and Marc Christie. 2015. “Comparing Film-Editing.” In Eurographics Workshop on Intelligent Cinematography and Editing, edited by William H. Bares, Marc Christie, and Rémi Ronfard, 5–12. Aire-la-Ville, Switzerland: Eurographics Association.

He, Li-wei, Michael F. Cohen, and David H. Salesin. 1996. “The Virtual Cinematographer: A Paradigm for Automatic Real-Time Camera Control and Directing.” In 

Proceedings of SIGGRAPH ’96, 217–24. New York: Association for Computing Machinery.

Ronfard, Rémi. 2012. “A Review of Film Editing Techniques for Digital Games.” In Workshop on Intelligent Cinematography and Editing. https://hal.inria.fr/hal-00694444/.

Artificial Intelligence - What Are Mobile Recommendation Assistants?

 

Mobile Recommendation Assistants, also known as Virtual Assistants, Intelligent Agents, or Virtual Personal Assistants, are a collection of software features that combine a conversational user interface with artificial intelligence to act on behalf of a user.

They may deliver what seems to a user as an agent when they work together.

In this sense, an agent differs from a tool in that it has the ability to act autonomously and make choices with some degree of autonomy.

Many qualities may be included into the design of mobile suggestion helpers to improve the user's impression of agency.

Using visual avatars to represent technology, incorporating features of personality such as humor or informal/colloquial language, giving a voice and a legitimate name, constructing a consistent method of behaving, and so on are examples of such tactics.

A human user can use a mobile recommendation assistant to help them with a wide range of tasks, such as opening software applications, answering questions, performing tasks (operating other software/hardware), or engaging in conversational commerce or entertainment (telling stories, telling jokes, playing games, etc.).

Apple's Siri, Baidu's Xiaodu, Amazon's Alexa, Microsoft's Cortana, Google's Google Assistant, and Xiaomi's Xiao AI are among the mobile voice assistants now in development, each designed for certain companies, use cases, and user experiences.

A range of user interface modali ties are used by mobile recommendation aides.

Some are completely text-based, and they are referred regarded as chatbots.

Business to consumer (B2C) communication is the most common use case for a chatbot, and notable applications include online retail communication, insurance, banking, transportation, and restaurants.

Chatbots are increasingly being employed in medical and psychological applications, such as assisting users with behavior modification.

Similar apps are becoming more popular in educational settings to help students with language learning, studying, and exam preparation.

Facebook Messenger is a prominent example of a chatbot on social media.

While not all mobile recommendation assistants need voice-enabled interaction as an input modality (some, such web site chatbots, may depend entirely on text input), many contemporary examples do.

A Mobile Recommendation Assistant uses a number similar predecessor technologies, including a voice-enabled user interface.

Early voice-enabled user interfaces were made feasible by a command syntax that was hand-coded as a collection of rules or heuristics in advance.

These rule-based systems allowed users to operate devices without using their hands by delivering voice directions.

IBM produced the first voice recognition program, which was exhibited during the 1962 World's Fair in Seattle.

The IBM Shoebox has a limited vocabulary of sixteen words and nine numbers.

By the 1990s, IBM and Microsoft's personal computers and software had basic speech recognition; Apple's Siri, which debuted on the iPhone 4s in 2011, was the first mobile application of a mobile assistant.

These early voice recognition systems were disadvantaged in comparison to conversational mobile agents in terms of user experience since they required a user to learn and adhere to a preset command language.

The consequence of rule-based voice interaction might seem mechanical when it comes to contributing to real humanlike conversation with computers, which is a feature of current mobile recommendation assistants.

Instead, natural language processing (NLP) uses machine learning and statistical inference to learn rules from enormous amounts of linguistic data (corpora).

Decision trees and statistical modeling are used in natural language processing machine learning to understand requests made in a variety of ways that are typical of how people regularly communicate with one another.

Advanced agents may have the capacity to infer a user's purpose in light of explicit preferences expressed via settings or other inputs, such as calendar entries.

Google's Voice Assistant uses a mix of probabilistic reasoning and natural language processing to construct a natural-sounding dialogue, which includes conversational components such as paralanguage ("uh", "uh-huh", "ummm").

To convey knowledge and attention, modern digital assistants use multimodal communication.

Paralanguage refers to communication components that don't have semantic content but are nonetheless important for conveying meaning in context.

These may be used to show purpose, collaboration in a dialogue, or emotion.

The aspects of paralanguage utilized in Google's voice assistant employing Duplex technology are termed vocal segre gates or speech disfluencies; they are intended to not only make the assistant appear more human, but also to help the dialogue "flow" by filling gaps or making the listener feel heard.

Another key aspect of engagement is kinesics, which makes an assistant feel more like an engaged conversation partner.

Kinesics is the use of gestures, movements, facial expressions, and emotion to aid in the flow of communication.

The car firm NIO's virtual robot helper, Nome, is one recent example of the application of face expression.

Nome is a digital voice assistant that sits above the central dashboard of NIO's ES8 in a spherical shell with an LCD screen.

It can swivel its "head" automatically to attend to various speakers and display emotions using facial expressions.

Another example is Dr. Cynthia Breazeal's commercial Jibo home robot from MIT, which uses anthropomorphism using paralinguistic approaches.

Motion graphics or lighting animations are used to communicate states of communication such as listening, thinking, speaking, or waiting in less anthropomorphic uses of kinesics, such as the graphical user interface elements on Apple's Siri or illumination arrays on Amazon Alexa's physical interface Echo or in Xiami's Xiao AI.

The rising intelligence and anthropomorphism (or, in some circumstances, zoomorphism or mechanomorphism) that comes with it might pose some ethical issues about user experience.

The need for more anthropomorphic systems derives from the positive user experience of humanlike agentic systems whose communicative behaviors are more closely aligned with familiar interactions like conversation, which are made feasible by natural language and paralinguistics.

Natural conversation systems have the fundamental advantage of not requiring the user to learn new syntax or semantics in order to successfully convey orders and wants.

These more humanistic human machine interfaces may employ a user's familiar mental model of communication, which they gained through interacting with other people.

Transparency and security become difficulties when a user's judgments about a machine's behavior are influenced by human-to-human communication as machine systems become closer to human-to-human contact.

The establishment of comfort and rapport may obscure the differences between virtual assistant cognition and assumed motivation.

Many systems may be outfitted with motion sensors, proximity sensors, cameras, tiny phones, and other devices that resemble, replicate, or even surpass human capabilities in terms of cognition (the assistant's intellect and perceptive capacity).

While these can be used to facilitate some humanlike interaction by improving perception of the environment, they can also be used to record, document, analyze, and share information that is opaque to a user when their mental model and the machine's interface do not communicate the machine's operation at a functional level.

After a user interaction, a digital assistant visual avatar may shut his eyes or vanish, but there is no need to associate such behavior with the microphone's and camera's capabilities to continue recording.

As digital assistants become more incorporated into human users' daily lives, data privacy issues are becoming more prominent.

Transparency becomes a significant problem to solve when specifications, manufacturer data collecting aims, and machine actions are potentially mismatched with user expectations.

Finally, when it comes to data storage, personal information, and sharing methods, security becomes a concern, as hacking, disinformation, and other types of abuse threaten to undermine faith in technology systems and organizations.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Chatbots and Loebner Prize; Mobile Recommendation Assistants; Natural Language Processing and Speech Understanding.

References & Further Reading:

Lee, Gary G., Hong Kook Kim, Minwoo Jeong, and Ji-Hwan Kim, eds. 2015. Natural Language Dialog Systems and Intelligent Assistants. Berlin: Springer.

Leviathan, Yaniv, and Yossi Matias. 2018. “Google Duplex: An AI System for Accomplishing Real-world Tasks Over the Phone.” Google AI Blog. https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html.

Viken, Alexander. 2009. “The History of Personal Digital Assistants, 1980–2000.” Agile Mobility, April 10, 2009.

Waddell, Kaveh. 2016. “The Privacy Problem with Digital Assistants.” The Atlantic, May 24, 2016. https://www.theatlantic.com/technology/archive/2016/05/the-privacy-problem-with-digital-assistants/483950/.

Quantum Revolution 2.0 - Who's in Charge?

 A number of social actors come to mind as candidates for guiding technology development in a manner that is consistent with our human values. 

However, if they were the only designers, two of the most often cited social players would certainly be overwhelmed: 

The ability of social decision-makers (politicians, corporate leaders, media workers, and others) to respond to the ever-accelerating dynamics of technological change is much too sluggish. 

This is due to a lack of understanding among our political, economic, and cultural leaders of the present level of scientific research, among other things. 

Scientists will be unable to regulate technological development as well. In reality, the reverse is true. 

They, like all other members of society, are primarily guided by market logic. 

If they create new technology based on their ideas, they might become millionaires today. 

Furthermore, they are constantly reliant on the government or other organizations to provide funding for their study. 

The free market is a third socially productive force. 

Until now, technology advancement has almost entirely followed the logic of market-based (or military) application. 

To put it another way, whatever was feasible and provided someone a financial (or military) edge was done. 

Can we expect that the processes of free market competition will best regulate technological development for the greater good? 

Allowing the free market to determine development would imply that Google, Facebook, and Amazon would decide whether quantum computers or greater artificial intelligence would be used. 

Even the most ardent advocates of free market philosophy may find it difficult to believe that this will work out nicely for all of us. 

In reality, when it comes to ethical problems, the market is a terrible arbitrator. 

To determine how much of future technology development should be left to the free market, we must first understand and identify the factors that prevent it from making the optimal choices for society as a whole. 

Aside from the possibility of billions of dollars in commerce, which would almost certainly lead to insurmountable conflicts of interest, there are additional issues with blindly trusting the forces of the free market: 

1. Externalities: 

One group's economic actions may have an effect on other groups—possibly even all individuals on the planet—without the actors bearing the full cost. 

Externalities are most noticeable in public products that do not have a market price. 

Environmental resources and general health are examples of this. 

Some examples include: 

• polluting the environment still costs the polluter little or nothing; 

• climate-damaging CO2 emissions are still not associated with higher costs for producers; 

• the safety risks associated with nuclear power generation or natural gas fracking are largely borne by the general public; and 

• while the widespread use of antibiotics in agriculture produces higher yields for livestock.

2. Rent-seeking: 

Powerful groups often succeed in altering political and economic norms to their own benefit, resulting in different kinds of governmental guarantees that do not improve or even worsen general societal well-being. 

Corruption is the most apparent example. 

3. Asymmetries in information: 

In 1970, economist Georg Akerlof demonstrated in his article "The Market for Lemons" that free markets cannot operate effectively unless buyers and sellers have equal access to information.

However, significant information access asymmetries can be found in a variety of markets, including the labor market, the market for financial products (which allows banks to charge exorbitant fees for their investment products), the healthcare and food markets, the energy market, and, most importantly in our context, the market for new scientific knowledge and technologies. 

Anyone who wishes to balance the benefits of a new technology against its dangers must first learn all there is to know about it. 

The creator and producer, on the other hand, are the ones who know the most about it, and they are more interested in the possibilities for profit than the dangers. 

In a free market system, lying is simply part of the game for profit-driven businesses. 

This involves spreading doubt about accepted scientific knowledge on a systematic basis. 

Akerloff was subsequently given the Nobel Prize in Economics in 2001 for this discovery. 

4. Cognitive Irrationalities: 

Standard economic theory implies that we are aware of our own best interests. 

Behavioral economics, on the other hand, has long shown that humans are much less reasonable than proponents of the free market would have us think. 

As a result, rather than long-term logical concerns, producers and consumers are often driven by short-term emotional impulses. 

These are the four reasons why the free market is inadequate for directing socially acceptable technology development. 

The exploitation logic of capitalism is a powerful force that works against distinction and ethical thought in the creation and use of new technology. 

~ Jai Krishna Ponnappan

You may also want to read more about Quantum Computing here.