Tag: AI

Is AI Emotion Detection Ready for Prime Time?

Artificial Intelligence (AI) solutions that aim to recognize human emotions can provide useful insights for hiring, marketing, and other purposes. But their use also raises serious questions about accuracy, bias, and privacy. To learn about three common barriers that need to be overcome for AI emotion detection to become more mainstream, read on.

By using machine learning to mimic human intelligence, AI can execute everything from minimal and repetitive tasks to those requiring more “human” cognition. Now, AI solutions are popping up that go as far as to interpret human emotion. In solutions where AI and human emotion intersect, does the technology help, or deliver more trouble than value?

While we are starting to see emotion detection using AI in various technologies, several barriers to adoption exist, and serious questions arise as to whether the technology is ready to be widely used. AI that aims to interpret or replace human interactions can be flawed because of underlying assumptions made when the machine was trained. Another concern is the broader question of why anyone would want to have this technology used on them. Is the relationship equal between the organization using the technology and the individual on whom the technology is being used? Concerns like these need to be addressed for this type of AI to take off.

Let’s explore three common barriers to emotion detection using AI:

Barrier #1: Is AI emotion detection ethical for all involved?

Newly launched AI-based solutions that track human sentiment for sales, human resources, instruction, and telehealth can help provide useful insights by understanding people’s reactions during virtual conversations.

While talking through the screens, the AI tracks the sentiment of the person, or people, who are taking the information in, including their reactions and feedback. The person being tracked could be a prospective customer, employee, student, patient, etc., where it’s beneficial for the person leading the virtual interaction to better understand how the individual receiving the information is feeling and what they could be thinking.

This kind of AI could be viewed as ethical in human resources, telehealth, or educational use cases because it could benefit both the person delivering the information and those receiving the information to track reactions, such as fear, concern, or boredom. In this situation, the software could help deliver a better outcome for the person being assessed. However, few other use cases are available where it is advantageous for everyone involved to have one person get a “competitive advantage” over another in a virtual conversation by using AI technology.

Barrier #2:  Can discomfort and feelings of intrusion with AI emotion detection be overcome?  

This brings us to the next barrier – why should anyone agree to have this software turned on during a virtual conversation? If someone knows of an offset in control during a virtual conversation, the AI software comes across as incredibly intrusive. If people need to agree to be judged by the AI software in some form or another, many could decline just because of its invasive nature.

People are becoming more comfortable with technology and what it can do for us; however, people still want to feel like they have control of their decisions and emotions.

Barrier #3: How do we know if the results of emotion detection using AI are accurate?

We put a lot of trust in the accuracy of technology today, and generally, we don’t always consider how technology develops its abilities. The results for emotion-detecting AI depend heavily on the quality of the inputs that are training the AI. For example, the technology must consider not only how human emotion varies from person to person but the vast differences in body language and non-verbal communication from one culture to another. Users also will want to consider the value and impact of the recommendations that come out of the analysis and if it drives the desired behaviors that were intended.

Getting accurate data from using this kind of AI software could help businesses better meet the needs of customers and employees, and health and education institutions deliver better services. AI can pick up on small nuances that may otherwise be missed entirely and be useful in job hiring and other decision making.

But inaccurate data could alter what would otherwise have been a genuine conversation. Until accuracy improves, users should focus on whether the analytics determine the messages correctly and if overall patterns exist that can be used for future interactions. While potentially promising, AI emotion detection may still have some learning to do before it’s ready for prime time.

Contact us for questions or to discuss this topic further.

Learn more about recent advances in technology in our webinar, Building Successful Digital Product Engineering Businesses. Everest Group experts will discuss the massive digital wave in the engineering world as smart, connected, autonomous, and intelligent physical and hardware products take center stage.

Is AI Emotion Detection Ready for Prime Time?

Artificial Intelligence (AI) solutions that aim to recognize human emotions can provide useful insights for hiring, marketing, and other purposes. But their use also raises serious questions about accuracy, bias, and privacy. To learn about three common barriers that need to be overcome for AI emotion detection to become more mainstream, read on.

By using machine learning to mimic human intelligence, AI can execute everything from minimal and repetitive tasks to those requiring more “human” cognition. Now, AI solutions are popping up that go as far as to interpret human emotion. In solutions where AI and human emotion intersect, does the technology help, or deliver more trouble than value?

While we are starting to see emotion detection using AI in various technologies, several barriers to adoption exist, and serious questions arise as to whether the technology is ready to be widely used. AI that aims to interpret or replace human interactions can be flawed because of underlying assumptions made when the machine was trained. Another concern is the broader question of why anyone would want to have this technology used on them. Is the relationship equal between the organization using the technology and the individual on whom the technology is being used? Concerns like these need to be addressed for this type of AI to take off.

Let’s explore three common barriers to emotion detection using AI:

Barrier #1: Is AI emotion detection ethical for all involved?

Newly launched AI-based solutions that track human sentiment for sales, human resources, instruction, and telehealth can help provide useful insights by understanding people’s reactions during virtual conversations.

While talking through the screens, the AI tracks the sentiment of the person, or people, who are taking the information in, including their reactions and feedback. The person being tracked could be a prospective customer, employee, student, patient, etc., where it’s beneficial for the person leading the virtual interaction to better understand how the individual receiving the information is feeling and what they could be thinking.

This kind of AI could be viewed as ethical in human resources, telehealth, or educational use cases because it could benefit both the person delivering the information and those receiving the information to track reactions, such as fear, concern, or boredom. In this situation, the software could help deliver a better outcome for the person being assessed. However, few other use cases are available where it is advantageous for everyone involved to have one person get a “competitive advantage” over another in a virtual conversation by using AI technology.

Barrier #2:  Can discomfort and feelings of intrusion with AI emotion detection be overcome?  

This brings us to the next barrier – why should anyone agree to have this software turned on during a virtual conversation? If someone knows of an offset in control during a virtual conversation, the AI software comes across as incredibly intrusive. If people need to agree to be judged by the AI software in some form or another, many could decline just because of its invasive nature.

People are becoming more comfortable with technology and what it can do for us; however, people still want to feel like they have control of their decisions and emotions.

Barrier #3: How do we know if the results of emotion detection using AI are accurate?

We put a lot of trust in the accuracy of technology today, and generally, we don’t always consider how technology develops its abilities. The results for emotion-detecting AI depend heavily on the quality of the inputs that are training the AI. For example, the technology must consider not only how human emotion varies from person to person but the vast differences in body language and non-verbal communication from one culture to another. Users also will want to consider the value and impact of the recommendations that come out of the analysis and if it drives the desired behaviors that were intended.

Getting accurate data from using this kind of AI software could help businesses better meet the needs of customers and employees, and health and education institutions deliver better services. AI can pick up on small nuances that may otherwise be missed entirely and be useful in job hiring and other decision making.

But inaccurate data could alter what would otherwise have been a genuine conversation. Until accuracy improves, users should focus on whether the analytics determine the messages correctly and if overall patterns exist that can be used for future interactions. While potentially promising, AI emotion detection may still have some learning to do before it’s ready for prime time.

Contact us for questions or to discuss this topic further.

Learn more about recent advances in technology in our webinar, Building Successful Digital Product Engineering Businesses. Everest Group experts will discuss the massive digital wave in the engineering world as smart, connected, autonomous, and intelligent physical and hardware products take center stage.

Federated Learning: Privacy by Design for Machine Learning | Blog

With cyberattacks and data breaches at all-time highs, consumers are increasingly skeptical about sharing their data with enterprises, creating a dilemma for artificial intelligence (AI) that needs massive data to thrive. The nascent technology of federated learning offers an ideal growing alternative for healthcare, life sciences, banking, finance, manufacturing, advertising, and other industries to unleash the full potential of AI without compromising the privacy of individuals. To learn how you can have all the data you need while protecting consumers, read on.  

Privacy preservation with federated learning

The infinite number of massive data breaches that have stripped individuals of their privacy has made the public more aware of the need to protect their data. In the absence of strong governance and guidelines, people are more skeptical than ever about sharing their personal data with enterprises.

This new data-conscious paradigm poses a problem for artificial intelligence (AI) that thrives on huge amounts of data. Unless we can figure out a way to train machines on significantly smaller data sets, protecting the privacy and data of users will remain key obstacles to intelligent automation.

Federated learning (FL) is emerging as a solution to overcome this problem. Broadly speaking, Federated learning is a method of training machine learning models in a way that the user data does not leave its location, keeping it safe and private. This differs from the traditional centralized machine learning methods that require the data to be aggregated in a centralized location.

Federated learning is a mechanism of machine learning, wherein the process of learning takes place in a decentralized manner across a network of nodes/edge devices, and the results are aggregated in a central server to create a unified model. It essentially comprises decoupling the activity of model training with centralized data storage.

The Mechanism of Federated Learning

By training the same model across an array of devices, each with its own set of data, we get multiple versions of the model, which, when combined, create a more powerful and accurate global version for deployment and use.

In addition to training algorithms in a private and secure manner, Federated learning provides an array of other benefits such as:

  • Training across data silos
  • Training on heterogeneous data
  • Lower communication costs
  • Reduced liability

Federated learning applicability and use cases

Based on an Everest Group framework, we have found Federated learning is most suitable and being adopted at higher rates in industrials where data is an extremely critical asset that is present across different locations in a distributed fashion and privacy is paramount.

Federated learning is especially beneficial for industries that have strict data residency requirements. This makes the healthcare and life-sciences industries perfect candidates for its adoption. Federated learning can help facilitate multi-institution collaborations between medical institutions by helping them overcome regulatory hurdles that prevent them from sharing patient data by pooling data in a common location.

The next industry ripe for the adoption of Federated learning is the banking and financial sectors. For instance, it can be used to develop a more comprehensive and accurate fraud analytics solution that is based on data from multiple financial entities.

Another industry where we see high applicability of Federated learning is the manufacturing industry. By ensuring collaboration between different entities across the supply chain, using Federated learning techniques, there is a case to build a more powerful model that can help increase the overall efficiency across the supply chain.

Federated learning also might find increased use in interest-based advertising. With the decision to disable third-party cookies by major internet browsers, marketers are at a loss for targeted advertising and engagement. With Federated Learning, marketers can replace individual identifiers with cohorts or group-based identifiers. These cohorts are created by identifying people with common interests based on individual user data such as browsing habits stored on local machines.

An ecosystem on the rise

Since Google introduced the concept of Federated learning in 2016, there has been a flurry of activity. Given that this is a nascent technology, the ecosystem is currently dominated by big tech and open-source players. We see hyperscalers taking the lead with Microsoft and Amazon Web Services (AWS) making investments to activate Federated learning, followed by Nvidia and Lenovo who are looking at the market from a hardware perspective.

Another segment of players working in this arena are startups that are using Federated learning to build industry-specific solutions. AI companies such as Owkin and Sherpa.ai are pioneering this technology and have developed Federated learning frameworks that are currently operational at a few enterprises’ locations.

The adoption and need for Federated learning depend on the industry and vary with the use case. Everest Group has developed a comprehensive framework to help you assess and understand the suitability of Federated learning for your use-case in our Latest Primer for Federated Learning. The framework is built on four key parameters that include data criticality, privacy requirement, regulatory constraint, and data silo/ diversity.

Federated learning provides us with an alternative way to make AI work in a world without compromising the privacy of individuals.

If you are interested in understanding the suitability of federated learning for your enterprise, please share your thoughts with us at [email protected].

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