What Is Emotion Recognition AI?
Emotion recognition AI is a type of machine learning model that often relies on computer vision technology to capture and analyze facial expressions to decipher moods in images and videos. However, it can also operate on audio snippets to determine the tone of voice or written text to assess the sentiment of language.
Use Cases for AI Emotion Recognition
Businesses, educators, consultants, and mental health care professionals are some of the groups that can use AI for emotion recognition.
Assessing Risk in the Office
Human resource teams can use algorithms to conduct sentiment analysis on email correspondence or in-app chats between team members. Alternatively, they can integrate their algorithm into their surveillance or computer vision system. Users can track mood to calculate metrics like turnover risk, burnout rate, and employee satisfaction.
Assisting Customer Service Agents
Retailers can use in-house AI customer service agents for end users or virtual assistants to resolve high-stress situations. Since their model can recognize mood, it can suggest de-escalation techniques or change its tone when it realizes a consumer is getting angry. Countermeasures like these may improve customer satisfaction and retention.
Helping Students in the Classroom
Educators can use this AI to keep remote learners from falling behind. One startup has already used its tool to measure muscle points on students’ faces while cataloging their speed and grades. This method determines their mood, motivation, strengths, and weaknesses. The startup’s founder claims they score 10% higher on tests when using the software.
Conducting In-House Market Research
Businesses can conduct in-house market research using an emotion recognition model. It can help them understand exactly how their target audience reacts to their product, service, or marketing material, giving them valuable data-driven insights. As a result, they may accelerate time-to-market and increase their revenue.
The Problem With Using AI to Detect Emotions
Research suggests accuracy is highly dependent on training information. One research group – attempting to decipher feelings from images – anecdotally proved this concept when their model achieved a 92.05% accuracy on the Japanese Female Facial Expression dataset and a 98.13% accuracy on the Extended Cohn-Kanade dataset.
The Current State of Emotion Recognition AI
Algorithmic sentiment analysis is the process of using an algorithm to determine if the tone of the text is positive, neutral, or negative. This technology is arguably the foundation for modern emotion detection models since it paved the way for algorithmic mood evaluations.
Ethical Considerations for AI Developers
According to one survey, 67% of respondents agree AI should be somewhat or much more regulated. To put people’s minds at ease, developers should minimize bias, ensure their models behave as expected, and improve outcomes. These solutions are possible if they prioritize ethical considerations during development.
1. Consensual Data Collection and Utilization
Consent is everything in an age where AI regulation is increasing. What happens if employees discover their facial expressions are being cataloged without their knowledge? Do parents need to sign off on education-based sentiment analysis or can students decide for themselves?
2. Anonymized Sentiment Analysis Output
Data anonymization is as much a privacy problem as it is a security issue. Developers should anonymize the emotion information they collect to protect the individuals involved. At the very least, they should strongly consider leveraging at-rest encryption.
3. Human-in-the-Loop Decision-Making
The only reason to use AI to determine someone’s emotional state is to inform decision-making. As such, whether it’s used in a mental health capacity or a retail setting, it will impact people. Developers should leverage human-in-the-loop safeguards to minimize unexpected behavior.
4. Human-Centered Feedback for AI Output
Even if an algorithm has nearly 100% accuracy, it will still produce false positives. Considering it’s not uncommon for models to achieve 50% or 70% – and that’s without touching on bias or hallucination issues – developers should consider implementing a feedback system.
The Consequences of Ignoring Ethics
Ethical considerations should be a priority for AI engineers, machine learning developers, and business owners because it affects them. Considering increasingly unsure public opinion and tightening regulations are at play, the consequences of ignoring ethics may be significant.
Emotion recognition AI has the potential to revolutionize the way we interact with technology and each other. However, its success depends on the ability of AI developers to overcome the ethical issues that come with its use. By prioritizing ethical considerations, AI engineers can create a more trustworthy and effective technology that benefits both individuals and society as a whole.
