AI – Page 3

by David Adlberger - 10. March 2025

Prototyping I: Image Extender – Image sonification tool for immersive perception of sounds from images and new creation possibilities

Shift of intention of the project due to time plan:

By narrowing down the topic to ensure the feasibility of this project the focus or main purpose of the project will be the artistic approach. The tool will still combine the use of direct image to audio translation and the translation via sonification into a more abstract form. The main use cases will be generating unique audio samples for creative applications, such as sound design for interactive installations, brand audio identities, or matching image soundscapes and the possibility to be a versatile instrument for experimental media artists and display tool for image information.

By further research on different possibilities of sonification of image data and development of the sonification language itself the translation and display purpose is going to get more clear within the following weeks.

Testing of Google Gemini API for AI Object and Image Recognition:

The first testing of the Google Gemini Api started well. There are different models for dedicated object recognition and image recognition itself which can be combined to analyze pictures in terms of objects and partly scenery. These models (SSD, EfficientNET,…) create similar results but not always the same. It might be an option to make it selectable for the user (so that in a failure case a different model can be tried and may give better results). The scenery recognition itself tends to be a problem. It may be a possibility to try out different apis.

The data we get from this AI model is a tag for the recognized objects or image content and a percentage of the probability.

The next steps for the direct translation of it into realistic sound representations will be to test the possibility of using the api of freesound.org to search directly and automated for the recognized object tags and load matching audio files. These search calls also need to filter by copyright type of the sounds and a choosing rule / algorithm needs to be created.

object recognition: efficient float 16 model (Photo by Jason Oh on unsplash)

Research on sonification of images / video material and different approaches:

The world of image sonification is rich with diverse techniques, each offering unique ways to transform visual data into auditory experiences. The world of image sonification is rich with diverse techniques, each offering unique ways to map visual data into auditory experiences. One of the most straightforward methods is raster scanning, introduced by Yeo and Berger. This technique maps the brightness values of grayscale image pixels directly to audio samples, creating a one-to-one correspondence between visual and auditory data. By scanning an image line by line, from top to bottom, the system generates a sound that reflects the texture and patterns of the image. For example, a smooth gradient might produce a steady tone, while a highly textured image could result in a more complex, evolving soundscape. The process is fully reversible, allowing for both image sonification and sound visualization, making it a versatile tool for artists and researchers alike. This method is particularly effective for sonifying image textures and exploring the auditory representation of visual filters, such as “patchwork” or “grain” effects.(Yeo and Berger, 2006)

Principle raster scanning (Yeo and Berger, 2006)

In contrast, Audible Panorama (Huang et al. 2019) automates sound mapping for 360° panorama images used in virtual reality (VR). It detects objects using computer vision, estimates their depth, and assigns spatialized audio from a database. For example, a car might trigger engine sounds, while a person generates footsteps, creating an immersive auditory experience that enhances VR realism. A user study confirmed that spatial audio significantly improves the sense of presence. It contains a interesting concept regarding to choosing a random audio file from a sound library to avoid producing similar or same results. Also it mentions the aspect of postprocessing the audios which also would be a relevant aspect for the image extender project.

principle audible panorama (Huang et al. 2019)

Another approach, HindSight (Schoop, Smith, and Hartmann 2018), focuses on real-time object detection and sonification in 360° video. Using a head-mounted camera and neural networks, it detects objects like cars and pedestrians, then sonifies their position and danger level through bone conduction headphones. Beeps increase in tempo and pan to indicate proximity and direction, providing real-time safety alerts for cyclists.

Finally, Sonic Panoramas (Kabisch, Kuester, and Penny 2005) takes an interactive approach, allowing users to navigate landscape images while generating sound based on their position. Edge detection extracts features like mountains or forests, mapping them to dynamic soundscapes. For instance, a mountain ridge might produce a resonant tone, while a forest creates layered, chaotic sounds, blending visual and auditory art. It also mentions different approaches for sonification itself. For example the idea of using micro (timbre, pitch and melody) and macro level (rhythm and form) mapping.

principle sonic panoramas (Kabisch, Kuester, and Penny 2005)

Each of these methods—raster scanning, Audible Panorama, HindSight, and Sonic Panoramas—demonstrates the versatility of sonification as a tool for transforming visual data into sound and lead keeping these different approaches in mind for developing my own sonification language or mapping method. It also leads to further research by checking some useful references they used in their work for a deeper understanding of sonification and extending the possibilities.

References

Huang, Haikun, Michael Solah, Dingzeyu Li, and Lap-Fai Yu. 2019. “Audible Panorama: Automatic Spatial Audio Generation for Panorama Imagery.” In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–11. Glasgow, Scotland: ACM. https://doi.org/10.1145/3290605.3300851.

Kabisch, Eric, Falko Kuester, and Simon Penny. 2005. “Sonic Panoramas: Experiments with Interactive Landscape Image Sonification.” In Proceedings of the 2005 International Conference on Artificial Reality and Telexistence (ICAT), 156–163. Christchurch, New Zealand: HIT Lab NZ.

Schoop, Eldon, James Smith, and Bjoern Hartmann. 2018. “HindSight: Enhancing Spatial Awareness by Sonifying Detected Objects in Real-Time 360-Degree Video.” In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–12. Montreal, QC, Canada: ACM. https://doi.org/10.1145/3173574.3173717.

Yeo, Woon Seung, and Jonathan Berger. 2006. “Application of Raster Scanning Method to Image Sonification, Sound Visualization, Sound Analysis and Synthesis.” In Proceedings of the 9th International Conference on Digital Audio Effects (DAFx-06), 311–316. Montreal, Canada: DAFx.

by David Adlberger - 5. March 2025

Explore II: Image Extender – Image sonification tool for immersive perception of sounds from images and new creation possiblities

The Image Extender project bridges accessibility and creativity, offering an innovative way to perceive visual data through sound. With its dual-purpose approach, the tool has the potential to redefine auditory experiences for diverse audiences, pushing the boundaries of technology and human perception.

The project is designed as a dual-purpose tool for immersive perception and creative sound design. By leveraging AI-based image recognition and sonification algorithms, the tool will transform visual data into auditory experiences. This innovative approach is intended for:

1. Visually Impaired Individuals
2. Artists and Designers

The tool will focus on translating colors, textures, shapes, and spatial arrangements into structured soundscapes, ensuring clarity and creativity for diverse users.

Core Functionality: Translating image data into sound using sonification frameworks and AI algorithms.
Target Audiences: Visually impaired users and creative professionals.
Platforms: Initially desktop applications with planned mobile deployment for on-the-go accessibility.
User Experience: A customizable interface to balance complexity, accessibility, and creativity.

Working Hypotheses and Requirements

Hypotheses:
1. Cross-modal sonification enhances understanding and creativity in visual-to-auditory transformations.
2. Intuitive soundscapes improve accessibility for visually impaired users compared to traditional methods.
Requirements:
- Develop an intuitive sonification framework adaptable to various images.
- Integrate customizable settings to prevent sensory overload.
- Ensure compatibility across platforms (desktop and mobile).

Subtasks

1. Project Planning & Structure

Define Scope and Goals: Clarify key deliverables and objectives for both visually impaired users and artists/designers.
Research Methods: Identify research approaches (e.g., user interviews, surveys, literature review).
Project Timeline and Milestones: Establish a phased timeline including prototyping, testing, and final implementation.
Identify Dependencies: List libraries, frameworks, and tools needed (Python, Pure Data, Max/MSP, OSC, etc.).

2. Research & Data Collection

Sonification Techniques: Research existing sonification methods and metaphors for cross-modal (sight-to-sound) mapping and research different other approaches that can also blend in the overall sonification strategy.
Image Recognition Algorithms: Investigate AI image recognition models (e.g., OpenCV, TensorFlow, PyTorch).
Psychoacoustics & Perceptual Mapping: Review how different sound frequencies, intensities, and spatialization affect perception.
Existing Tools & References: Study tools like Melobytes, VOSIS, and BeMyEyes to understand features, limitations, and user feedback.

object detection from python yolo library

3. Concept Development & Prototyping

Develop Sonification Mapping Framework: Define rules for mapping visual elements (color, shape, texture) to sound parameters (pitch, timbre, rhythm).
Simple Prototype: Create a basic prototype that integrates:
- AI content recognition (Python + image processing libraries).
- Sound generation (Pure Data or Max/MSP).
- Communication via OSC (e.g., using Wekinator).
Create or collect Sample Soundscapes: Generate initial soundscapes for different types of images (e.g., landscapes, portraits, abstract visuals).

example of puredata with rem library (image to sound in pure data by Artiom
Constantinov)

4. User Experience Design

UI/UX Design for Desktop:
- Design intuitive interface for uploading images and adjusting sonification parameters.
- Mock up controls for adjusting sound complexity, intensity, and spatialization.
Accessibility Features:
- Ensure screen reader compatibility.
- Develop customizable presets for different levels of user experience (basic vs. advanced).
Mobile Optimization Plan:
- Plan for responsive design and functionality for smartphones.

5. Testing & Feedback Collection

Create Testing Scenarios:
- Develop a set of diverse images (varying in content, color, and complexity).
Usability Testing with Visually Impaired Users:
- Gather feedback on the clarity, intuitiveness, and sensory experience of the sonifications.
- Identify areas of overstimulation or confusion.
Feedback from Artists/Designers:
- Assess the creative flexibility and utility of the tool for sound design.
Iterate Based on Feedback:
- Refine sonification mappings and interface based on user input.

6. Implementation of Standalone Application

Develop Core Application:
- Integrate image recognition with sonification engine.
- Implement adjustable parameters for sound generation.
Error Handling & Performance Optimization:
- Ensure efficient processing for high-resolution images.
- Handle edge cases for unexpected or low-quality inputs.
Cross-Platform Compatibility:
- Ensure compatibility with Windows, macOS, and plan for future mobile deployment.

7. Finalization & Deployment

Finalize Feature Set:
- Balance between accessibility and creative flexibility.
- Ensure the sonification language is both consistent and adaptable.
Documentation & Tutorials:
- Create user guides for visually impaired users and artists.
- Provide tutorials for customizing sonification settings.
Deployment:
- Package as a standalone desktop application.
- Plan for mobile release (potentially a future phase).

Technological Basis Subtasks:

Programming: Develop core image recognition and processing modules in Python.
Sonification Engine: Create audio synthesis patches in Pure Data/Max/MSP.
Integration: Implement OSC communication between Python and the sound engine.
UI Development: Design and code the user interface for accessibility and usability.
Testing Automation: Create scripts for automating image-sonification tests.

Possible academic foundations for further research and work:

Chatterjee, Oindrila, and Shantanu Chakrabartty. “Using Growth Transform Dynamical Systems for Spatio-Temporal Data Sonification.” arXiv preprint, 2021.

Chion, Michel. Audio-Vision. New York: Columbia University Press, 1994.

Görne, Tobias. Sound Design. Munich: Hanser, 2017.

Hermann, Thomas, Andy Hunt, and John G. Neuhoff, eds. The Sonification Handbook. Berlin: Logos Publishing House, 2011.

Schick, Adolf. Schallwirkung aus psychologischer Sicht. Stuttgart: Klett-Cotta, 1979.

Sigal, Erich. “Akustik: Schall und seine Eigenschaften.” Accessed January 21, 2025. mu-sig.de.

Spence, Charles. “Crossmodal Correspondences: A Tutorial Review.” Attention, Perception, Psychophysics, 2011.

Ziemer, Tim. Psychoacoustic Music Sound Field Synthesis. Cham: Springer International Publishing, 2020.

Ziemer, Tim, Nuttawut Nuchprayoon, and Holger Schultheis. “Psychoacoustic Sonification as User Interface for Human-Machine Interaction.” International Journal of Informatics Society, 2020.

Ziemer, Tim, and Holger Schultheis. “Three Orthogonal Dimensions for Psychoacoustic Sonification.” Acta Acustica United with Acustica, 2020.

by Veronika Poštrak - 25. January 202514. March 2025

1.10 AI Companions vs. Traditional Therapy

Can Technology Replace Human Connection?

The rise of AI companions has sparked a significant debate: can technology truly replace human therapists in addressing mental health issues? AI-driven systems like Woebot and Wysa offer cognitive-behavioral therapy (CBT) techniques, providing instant support to users. However, while these AI companions are effective in alleviating feelings of loneliness and offering immediate assistance, they still fall short in replicating the depth of human connection provided by traditional therapy.

AI as a Complementary Tool

AI companions offer several advantages, such as accessibility, 24/7 availability, and anonymity, making them valuable tools for individuals who may not have immediate access to human therapists. For instance, 48% of people in the U.S. reported experiencing some form of mental health issue, and AI solutions could help bridge the gap where human therapists are unavailable or overwhelmed by demand. However, they lack the nuanced empathy and relational depth that human therapists bring to therapeutic conversations. Research indicates that while AI companions can provide immediate relief, they do not guarantee substantial long-term improvements in mental health.

The Future of Mental Health Care

Rather than replacing human therapists, AI companions could become part of a hybrid model. AI can handle initial assessments and offer support between therapy sessions, while human therapists provide ongoing treatment for deeper emotional and psychological issues. This collaborative approach can provide a more comprehensive mental health support system, blending the best of both worlds. For example, AI companions have been shown to reduce loneliness among seniors, enhancing their overall well-being.

Effectiveness of AI in Addressing Mental Health Issues

AI companions have demonstrated effectiveness in managing certain mental health conditions:

Anxiety and Depression: AI-driven applications can provide immediate support and coping strategies for individuals experiencing anxiety and depression. They offer tools like mood tracking, mindfulness exercises, and cognitive-behavioral techniques to help users manage symptoms.

Stress Management: AI companions can assist in stress reduction by guiding users through relaxation techniques, meditation, and providing real-time feedback on stress levels.

However, AI companions are less effective in addressing:

Severe Mental Health Disorders: Conditions such as schizophrenia, bipolar disorder, and severe personality disorders require comprehensive treatment plans that include medication management and intensive psychotherapy, areas where AI companions currently fall short.

Crisis Situations: In cases of acute mental health crises, such as suicidal ideation or severe self-harm, immediate human intervention is crucial. AI companions are not equipped to handle such emergencies and may not provide the necessary support.

Sources

“AI In Mental Health: Opportunities And Challenges In Developing Intelligent Digital Therapies.” Forbes. Accessed: Jan. 25, 2024. [Online.] Available: https://www.forbes.com/sites/bernardmarr/2023/07/06/ai-in-mental-health-opportunities-and-challenges-in-developing-intelligent-digital-therapies/
“AI Therapists vs. Human Therapists: Complementary Roles in Mental Health.” mindpeace.ai. Accessed: Jan. 25, 2024. [Online.] Available: https://mindpeace.ai/blog/ai-therapists-vs-human-therapists
“Artificial intelligence in mental health care.” American Psychological Association. Accessed: Jan. 25, 2024. [Online.] Available: https://www.apa.org/practice/artificial-intelligence-mental-health-care
“Exploring the Pros and Cons of AI in Mental Health Care.” Active Minds. Accessed: Jan. 25, 2024. [Online.] Available: https://www.activeminds.org/blog/exploring-the-pros-and-cons-of-ai-in-mental-health-care/
“Can AI Companions Help Heal Loneliness? | Eugenia Kuyda | TED.” YouTube. Accessed: Jan. 25, 2024. [Online.] Available: https://www.youtube.com/watch?v=-w4JrIxFZRA
Lee, E. E., Torous, J., De Choudhury, M., Depp, C. A., Graham, S. A., Kim, H. C., Paulus, M. P., Krystal, J. H., & Jeste, D. V. (2021). Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(9), 856-864. https://doi.org/10.1016/j.bpsc.2021.02.001
“Mental Health Apps and the Role of AI in Emotional Wellbeing.” Mya Care. Accessed: Jan. 25, 2024. [Online.] Available: https://myacare.com/blog/mental-health-apps-and-the-role-of-ai-in-emotional-wellbeing
Thakkar, A., Gupta, A., & De Sousa, A. (2024). Artificial Intelligence in Positive Mental Health: A Narrative Review. Frontiers in Digital Health, 6, 1280235. https://doi.org/10.3389/fdgth.2024.1280235
” ‘They thought they were doing good but it made people worse’: why mental health apps are under scrutiny.” The Guardian. Accessed: Jan. 25, 2024. [Online.] Available: https://www.theguardian.com/society/2024/feb/04/they-thought-they-were-doing-good-but-it-made-people-worse-why-mental-health-apps-are-under-scrutiny
“Why Some Mental Health Apps Aren’t Helpful?” Greater Good Magazine. Accessed: Jan. 25, 2024. [Online.] Available: https://greatergood.berkeley.edu/article/item/why_some_mental_health_apps_arent_helpful

by Veronika Poštrak - 24. January 202514. March 2025

1.9 The Emotional Intelligence of AI: Can Chatbots Truly Understand Us?

As AI technology advances, chatbots are evolving to recognize emotional cues, providing support in mental health, companionship, and conversational interfaces. By integrating techniques such as natural language processing (NLP), sentiment analysis, and machine learning, these systems aim to simulate empathy and create meaningful interactions. However, the development of empathetic AI comes with challenges, including technological limitations, ethical concerns, and potential risks of over-dependence.

Advancements in Empathetic Algorithms

Empathetic algorithms are designed to detect, interpret, and respond to human emotions using methods such as NLP, voice tone recognition, and facial expression analysis. For example: Woebot employs cognitive-behavioral therapy (CBT) techniques to guide users through stress and anxiety management, leveraging emotional cues from conversations. Wysa uses sentiment analysis to provide customized mindfulness exercises and mood tracking tools for emotional resilience.

Beyond mental health, empathetic algorithms are being integrated into other sectors like education and customer service, tailoring interactions based on emotional cues to improve engagement and satisfaction.

Chatbots as Relationship Simulators

LLMs such as GPT power chatbots like Replika AI and Character AI, which simulate human-like relationships. Replika AI enables users to design virtual companions for friendship, mentorship, or even romantic connections, raising questions about emotional reliance and blurred boundaries between humans and machines. Character AI allows users to interact with AI representations of fictional or historical figures, blending entertainment with relationship simulation.

These developments reflect themes from the movie Her, where an AI operating system becomes a deeply personal companion. While such systems offer emotional support, they highlight risks like over-dependence, which could potentially hinder real-life emotional interactions.

The Role of Empathy in AI

Empathetic AI is transforming human-AI interactions by making them more intuitive and emotionally aligned. However, achieving true emotional intelligence in machines remains a significant challenge:

Complex Emotions: Emotions are shaped by individual, cultural, and situational factors, making them difficult for AI to interpret consistently.

Simulated Empathy: Current AI systems simulate empathy by mimicking human responses rather than genuinely understanding emotions.

Ethical Concerns: Privacy risks arise from AI’s reliance on sensitive emotional data, making transparency and data security essential.

Applications and Insights from Research

Recent studies emphasize how empathetic algorithms can enhance human emotional intelligence by fostering emotional awareness and resilience. For instance:

Educational AI systems: Tailor learning environments to students’ emotional states, adapting content based on signs of frustration or confusion.
Healthcare applications: Use empathetic AI to assess patients’ emotional needs and deliver personalized support, improving outcomes for individuals with anxiety or depression.

Despite these advancements, challenges such as cultural biases in emotion recognition and the need for interdisciplinary collaboration remain key areas for growth.

Sources

“Character.ai: Young people turning to AI therapist bots.” BBC. Accessed: Jan. 24, 2025. [Online.] Available: https://www.bbc.com/news/technology-67872693?utm_source=chatgpt.com
” ‘Maybe we can role-play something fun’: When an AI companion wants something more.” BBC. Accessed: Jan. 24, 2025. [Online.] Available: https://www.bbc.com/future/article/20241008-the-troubling-future-of-ai-relationships?utm_source=chatgpt.com
“Replika CEO Eugenia Kuyda says it’s okay if we end up marrying AI chatbots.” The Verge. Accessed: Jan. 24, 2025. [Online.] Available: https://www.theverge.com/24216748/replika-ceo-eugenia-kuyda-ai-companion-chatbots-dating-friendship-decoder-podcast-interview?utm_source=chatgpt.com
Velagaleit, S. B., Choukaier, D., Nuthakki, R., Lamba, V., Sharma, V., & Rahul, S. (2024). Empathetic Algorithms: The Role of AI in Understanding and Enhancing Human Emotional Intelligence. Journal of Electrical Systems, 20-3s, 2051–2060. https://doi.org/10.52783/jes.1806
“Woebot Health – Mental Health Chatbot.” Woebot Health. Accessed: Jan. 24, 2025. [Online.] Available: https://woebothealth.com/
“Wysa – Everyday Mental Health.” Wysa. Accessed: Jan. 24, 2025. [Online.] Available: https://www.wysa.com/

by Veronika Poštrak - 17. January 202514. March 2025

1.7 Privacy vs. Personalization: Navigating Ethical Challenges in AI Mental Health Apps

AI-driven mental health apps offer a remarkable combination of personalization and accessibility, providing users with tailored experiences based on their unique needs. For example, apps like Talkspace utilize AI to detect crisis moments and recommend immediate interventions, while platforms such as Wysa offer personalized exercises based on user interactions. However, these benefits come with significant privacy and ethical challenges. To deliver personalized support, such tools rely on sensitive data such as user emotions, behavioral patterns, and mental health histories. This raises critical questions about how this data is collected, stored, and used.

Image Source: Government Technology Insider

Ensuring privacy in these apps requires robust safeguards, including encryption, secure data storage, and compliance with regulations like GDPR in Europe and HIPAA in the United States. These laws mandate transparency, requiring developers to clearly explain how user data is handled. Companies like Headspace exemplify these practices by encrypting user data, limiting employee access, and providing users with the option to control data-sharing settings. Headspace also rigorously tests its AI for safety, particularly in detecting high-risk situations, and connects users to appropriate resources when needed.

Beyond privacy, ethical concerns about fairness and inclusivity in AI algorithms are prominent. If the data used to train these algorithms isn’t diverse, the resulting tools may be less effective, or even harmful, for underrepresented groups. For example, biases in language or cultural context can lead to misunderstandings or inappropriate recommendations, potentially alienating users. To address this, platforms must ensure their datasets are diverse and representative, integrate cultural sensitivity into their development processes, and conduct ongoing audits to identify and rectify biases. Headspace’s AI Council, a group of clinical and diversity experts, serves as a model for embedding equity and inclusivity in AI tools.

Transparency is another key pillar for ethical AI in mental health. Users must be informed about how the AI works, the types of data it collects, and its limitations. For example, AI is not a replacement for human empathy, and users should be made aware of when to seek professional help. Clear communication builds trust and empowers users to make informed choices about their mental health.

While AI-driven mental health apps can enhance engagement and outcomes through personalization, the trade-off between privacy and functionality must be carefully managed. Ethical design practices, such as secure data handling, bias mitigation, and transparent user communication, are essential for balancing these priorities. By addressing these challenges proactively, developers can ensure that these tools support mental health effectively while respecting users’ rights and diversity.

Sources

“AI principles at Headspace.” Headspace. Accessed: Jan. 14, 2025. [Online.] Available: https://www.headspace.com/ai
Basu, A., Samanta, S., Sur, S., & Roy, A. Digital Is the New Mainstream. Kolkata, India: Sister Nivedita University, 2023.
“Can AI help with mental health? Here’s what you need to know.” Calm. Accessed: Jan. 14, 2025. [Online.] Available: https://www.calm.com/blog/ai-mental-health
Coghlan, S., Leins, K., Sheldrick, S., Cheong, M., Gooding, P., & D’Alfonso, S. (2023). To chat or bot to chat: Ethical issues with using chatbots in mental health. Digital Health, 9, 1–11. https://doi.org/10.1177/20552076231183542
Hamdoun, S., Monteleone, R., Bookman, T., & Michael, K. (2023). AI-based and digital mental health apps: Balancing need and risk. IEEE Technology and Society Magazine, 42(1), 25–36. https://doi.org/10.1109/MTS.2023.3241309
Valentine, L., D’Alfonso, S., & Lederman, R. (2023). Recommender systems for mental health apps: Advantages and ethical challenges. AI & Society, 38(4), 1627–1638. https://doi.org/10.1007/s00146-021-01322-w

by Veronika Poštrak - 4. January 202514. March 2025

1.6 How AI Is Reshaping Mental Health Support

Artificial intelligence is revolutionizing mental health care by breaking down barriers like cost, stigma, and accessibility. With features like chatbots, biofeedback, and voice analysis, AI offers innovative solutions for mental health support. While AI can’t replace human therapists, its ability to complement traditional care makes it a valuable tool.

Venture capital reports reveal that mental health is the fastest-growing marketplace category, with a growth rate exceeding 200% in 2023. This surge reflects a rising demand for accessible mental health solutions as AI continues to play a critical role in meeting that need.

How AI Powers Mental Health Apps

AI-Driven Chatbots

AI chatbots provide immediate, tailored support for users in need:

Wysa offers CBT-based exercises and mindfulness prompts, creating a safe space for users to manage stress and anxiety.
Woebot adapts its conversations to users’ emotions, providing tools for real-time mental health management.
Cass combines emotional support and psychoeducation, offering adaptive responses that cater to individual needs.

In May 2024, Inflection AI launched Pi, a bot designed for emotional support and conversational companionship. Unlike other chatbots, Pi openly acknowledges its limitations, avoiding the pretense of being human while focusing on honest and straightforward interactions.

Wearables and Biofeedback

Wearable devices enhance AI’s ability to provide real-time insights into users’ mental states:

Moodfit and Spring Health use wearable data, like heart rate and stress levels, to deliver personalized mental health strategies.
Kintsugi analyzes vocal biomarkers to detect signs of anxiety or depression, offering users actionable insights based on their voice patterns.

These integrations bridge the gap between physical and emotional health, empowering users to take control of their well-being.

Opportunities in AI Mental Health Care

AI’s advantages lie in its ability to make mental health support more accessible, personalized, and inclusive:

Immediate and affordable: tools like Headspace’s Ebb and Wysa provide around-the-clock support at a fraction of the cost of traditional therapy.
Engagement and effectiveness: a 2022 review found that AI tools could improve engagement and reduce symptoms of anxiety and depression. However, experts emphasize that AI works best as a supplement, not a substitute, for traditional therapy. As Dr. Chris Mosunic of Calm explains, “Having a human in the driver’s seat with improved therapy AI tools might be just the right blend to maximize engagement, efficacy, and safety.”
Personalized support: apps like Woebot and Youper adapt their recommendations to the user’s changing emotional needs, creating a more tailored experience.

Challenges and Ethical Considerations

While AI offers promising solutions, it also presents challenges:

Limited empathy: AI tools often lack the emotional depth of human therapists, which can leave users feeling unsupported in complex situations.
Bias and inclusivity: non-diverse training data can lead to biased responses, potentially failing marginalized communities that rely more heavily on these tools due to systemic barriers.
Privacy concerns: AI tools require access to sensitive data. Apps like Talkspace use encryption to protect user information, but trust in data security remains a significant hurdle.

As these tools evolve, balancing innovation with ethical responsibility will be critical – a topic that will be explored further in upcoming articles.

Sources

A. Fiske, P. Henningsen, & A. Buyx. (2019). Your robot therapist will see you now: Ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. Journal of Medical Internet Research, 21(5), e13216. https://doi.org/10.2196/13216
A. Thakkar, A. Gupta, & A. De Sousa. (2024). Artificial intelligence in positive mental health: A narrative review. Frontiers in Digital Health, 6. https://doi.org/10.3389/fdgth.2024.1280235
“Can AI help with mental health? Here’s what you need to know.” Calm. Accessed: Jan. 4, 2025. [Online.] Available: https://www.calm.com/blog/ai-mental-health
“Meet Ebb | AI Mental Health Companion.” Headspace. Accessed: Jan. 4, 2025. [Online.] Available: https://www.headspace.com/ai-mental-health-companion
P. Gual-Montolio, I. Jaén, V. Martínez-Borba, D. Castilla, & C. Suso-Ribera. (2022). Using artificial intelligence to enhance ongoing psychological interventions for emotional problems in real- or close to real-time: A systematic review. International Journal of Environmental Research and Public Health, 19(13), 7737. https://doi.org/10.3390/ijerph19137737
“Rise of AI therapists.” VML. Accessed: Jan. 4, 2025. [Online.] Available: https://www.vml.com/insight/rise-of-ai-therapists

by jakob.schnurrer - 3. January 202526. January 2025

04 Bias in Ai

Taking a little detour from my actual topic, I wanted to explore an issue of our time, bias in Ai. A topic that comes up a lot, when reading about Ai. I wanted to know, what can be done about it and how it could be avoided. Could this have an additional impact on our society?

Artificial Intelligence (AI) is transforming industries, and (UX) Design is no exception. Ai already has the ability to deliver high quality design work and is going to continue to evolve. It’s reshaping how we approach design, offering tools that enhance efficiency, streamline workflows, and even generate creative outputs, it’s already capable to deliver high quality design work. While AI excels at analyzing data, creating prototypes, and even predicting user behavior, the heart of UX design lies in empathy, problem-solving, and collaboration, skills uniquely human in nature. (cf. Medium A)

Ai can analyze vast amounts of user data to uncover patterns and insights that inform design decisions, helping designers better understand their audience. It can also generate initial design drafts or prototypes, saving time and allowing designers to focus on refining creative and strategic elements. Predictive algorithms powered by AI can anticipate user behavior, enabling the creation of more intuitive and personalized experiences. By automating repetitive tasks and offering data-driven insights, AI empowers designers to elevate their craft while maintaining a human-centered approach. (cf. Medium A)

But what if the data the Ai gets is already biased towards a certain user group, making it’s outputs biased as well a therefore influencing UX work. Addressing bias in AI is not just a technical challenge; it’s an ethical imperative that impacts the lives of millions.

Examples of Bias in Ai

Healthcare Disparities:
An algorithm used in U.S. hospitals was found to favor white patients over black patients when predicting the need for additional medical care. This bias arose because the algorithm relied on past healthcare expenditures, which were lower for black patients with similar conditions, leading to unequal treatment recommendations.
Gender Stereotyping in Search Results
A study revealed that only 11% of individuals appearing in Google image searches for “CEO” were women, despite women constituting 27% of CEOs in the U.S. This discrepancy highlights how Ai can perpetuate gender stereotypes.
Amazon’s Hiring Algorithm
Amazon’s experimental recruiting tool was found to be biased against female applicants. The Ai, trained on resumes submitted over a decade, favored male candidates, reflecting the industry’s male dominance and leading to discriminatory hiring practices. (cf. Levity)

How does bias in Ai form?

Bias in Ai often forms due to the way data is collected, processed, and interpreted during the development cycle. Training datasets, which are meant to teach AI models how to make decisions, may not adequately represent all demographics, leading to underrepresentation of minority groups. Historical inequities embedded in this data can reinforce stereotypes or amplify disparities. Additionally, the way problems are defined at the outset can introduce bias; for instance, using cost-saving measures as a proxy for patient care needs can disproportionately affect underserved communities. Furthermore, design choices in algorithms, such as prioritizing overall accuracy over subgroup performance, can lead to inequitable outcomes. These biases, when unchecked, become deeply ingrained in AI systems, affecting their real-world applications.

Sometimes, the problem the Ai is supposed to solve is framed using flawed metrics. For instance, one widely used healthcare algorithm prioritized reducing costs over patient needs, disproportionately disadvantaging Black patients who required higher acuity care. (cf. Nature) When training datasets lack of diversity or reflect on historical inequities, Ai models learn to replicate these biases. Also, a well-designed system can fail in real-world settings if deployed in wrong environments it wasn’t optimized for. (cf. IBM) Decisions made during model training, like ignoring subgroup performance—can result in inequitable outcomes. (cf. Levity)

How to address bias in Ai

To avoid bias in Ai thoughtful planning and governance is important. Many organizations rush Ai efforts, leading to costly issues later. Ai governance establishes policies, practices, and frameworks for responsible development, balancing benefits for businesses, customers, employees, and society. Key components of governance include methods to ensure fairness, equity, and inclusion. Counterfactual fairness for example addresses bias in decision-making even with sensitive attributes like gender or race. Transparency practices help ensure unbiased data and build trustworthy systems. Furthermore a “human-in-the-loop” system can be incorporated to allow human oversight to approve or refine Ai-generated recommendations. (cf. IBM)

Reforming science and technology education to emphasize ethics and interdisciplinary collaboration is also crucial, alongside establishing global and local regulatory frameworks to standardize fairness and transparency. However, some challenges demand broader ethical and societal deliberation, highlighting the need for multidisciplinary input beyond technological solutions. (cf. Levity)

by Veronika Poštrak - 3. December 202414. March 2025

1.1 Designing Interfaces and AI for Calm and Well-being

A Digital Path to Mental Health Support

In today’s fast-paced world, stress and anxiety are part of daily life for many people. Finding mental health support has never been more important, but traditional therapy is often expensive and hard to access. That’s where technology comes in. Apps like Calm, Headspace, Wysa, BetterHelp, and Talkspace are helping millions of people take care of their mental health by offering tools like guided meditation, mood tracking, AI-powered chatbots, and even direct access to therapists.

Smartphones have made mental health care more accessible than ever. Mobile health apps offer a private and convenient way to improve mental well-being, breaking down barriers like cost, access, and stigma. There are now hundreds of thousands of health apps, with a significant number focused on mental health, and they’re popular with both users and clinicians. However, there are still concerns about security, privacy, and how effective these apps really are.

One of the most exciting developments in this space is the use of chatbots, AI-powered tools that allow users to have conversations about their mental health. These chatbots are available 24/7, creating a safe space for users to share sensitive information without fear of judgment. They can also provide immediate support when human connections aren’t available. But they’re not perfect. Sometimes their responses are too simplistic or even wrong, which can frustrate users. And while some people find chatbots comforting, others may rely on them too much, which could lead to feelings of isolation.

These tools have incredible potential, but there’s still room to improve. How can we design these apps to be even more effective? How do we make them feel personal and calming? Can design and artificial intelligence work together to create better tools for mental health support? These are the questions I’ll explore in this blog series, focusing on how thoughtful design, like intuitive layouts, smooth transitions, and calming animations, can make a difference. I’ll also look at how AI can act as a “digital companion” that provides personalized and empathetic support.

Central Research Questions

This project focuses on two key questions:

How can UX/UI design elements make mental health apps more calming and accessible?
What role can AI play in providing personalized and empathetic mental health support?

To answer these questions, I’ll look at how clear navigation and interactive features can help users feel more relaxed and supported. I’ll also explore how chatbots and AI systems can create a sense of trust and connection by feeling more human and empathetic. Finally, I’ll consider ethical issues, like protecting user privacy while using data to personalize the experience.

Why This Matters for Designers

Good design isn’t just about making something look nice, it’s about solving problems and improving people’s lives. Mental health apps are a great example of how design can make a real difference. Micro-interactions, like a gentle animation when you complete a task, can help users feel supported and motivated. These small touches might seem minor, but they create a sense of care and connection.

Colors also play an important role. Calming shades of blue and green can help users feel more relaxed, while warm tones, used sparingly, can create feelings of safety and comfort. Simplicity is key: clear, uncluttered layouts can help users navigate the app without feeling overwhelmed.

Information architecture – how content and features are organized, is another critical piece. A well-designed app might prioritize frequently used tools like mood tracking or journaling, while keeping other features easily accessible but out of the way. This reduces mental load and ensures users can focus on their well-being.

What makes this project especially exciting is the opportunity to design for emotional connection. It’s not just about functionality, it’s about creating an experience that feels personal and meaningful. With mental health challenges on the rise, designers have a chance to create tools that genuinely help people feel better.

Challenges I Expect to Face

Designing mental health apps comes with unique challenges. Personalization is essential, but it requires sensitive user data, which raises concerns about privacy and security. People need to feel confident their information is safe, so building trust is a top priority.

Another challenge is finding the right balance between simplicity and functionality. Apps need enough features to be useful, but too many can overwhelm users. Testing and user feedback will be crucial to getting this right.

The design also needs to avoid overstimulation. Too many animations, notifications, or bright colors can cause stress instead of reducing it. Ensuring the design feels calm and supportive is key.

Chatbots, while promising, present their own challenges. Poorly designed responses can frustrate users or even cause harm in a crisis. Making chatbots feel empathetic and reliable, while avoiding over-dependence, will require thoughtful design and testing.

Why This Matters to Me

Have you ever used ChatGPT to ask for advice or encouragement, like it’s a therapist? I have. It made me realize how much potential AI has to provide meaningful support. Mental health is something we all deal with at some point, and the idea of creating tools that make support more accessible feels deeply personal to me.

This project isn’t just about building an app, it’s about creating something that feels like a companion. A tool that understands what users need, offers comfort, and helps them feel calmer and more in control. Combining thoughtful design with AI to make a real impact on people’s lives is what excites me most about this project.

What’s Coming Next

In the upcoming blog posts, I’ll explore topics like color psychology and how specific colors can create calming digital environments. I’ll also dive into micro-interactions and how small design details, like animations and transitions, can make apps feel more intuitive and relaxing.

Another focus will be analyzing successful mental health apps, such as Calm, BetterHelp, and Wysa, to understand what makes them work. I’ll also look closely at the potential and challenges of chatbots, exploring how they can provide round-the-clock support while addressing their current limitations, like handling crises and overly simplistic responses.

The ultimate goal is to develop a foundation of ideas for creating mental health apps that blend thoughtful design with AI. These could include guidelines or even a prototype that shows how these ideas come to life in a practical, user-friendly way.

Sources

“BetterHelp | Professional Therapy With A Licensed Therapist.” BetterHelp. Accessed: Dec. 2, 2024. [Online.] Available: https://www.betterhelp.com/
“Calm – The #1 App for Meditation and Sleep.” Calm. Accessed: Dec. 2, 2024. [Online.] Available: https://www.calm.com/
“Headspace: Meditation and Sleep Made Simple.” Headspace. Accessed: Dec. 2, 2024. [Online.] Available: https://www.headspace.com/
M. D. R. Haque & S. Rubya. (2023). An overview of chatbot-based mobile mental health apps: Insights from app description and user reviews. JMIR mHealth and uHealth, 11, e44838. https://doi.org/10.2196/44838
M. Neary & S. M. Schueller. (2018). State of the field of mental health apps. Cognitive and Behavioral Practice, 25(4), 531–537. https://doi.org/10.1016/j.cbpra.2018.01.002
“Talkspace – #1 Rated Online Therapy, 1 Million+ Users.” Talkspace. Accessed: Dec. 2, 2024. [Online.] Available: https://www.talkspace.com/
“Wysa – Everyday Mental Health.” Wysa. Accessed: Dec. 2, 2024. [Online.] Available: https://www.wysa.com/

by dorota.bicarova - 19. November 202425. January 2025

#02 Key factors influencing the energy and environmental impact of digitalisation.

1. Growth of data centers and cloud computing

Data centers are the base of the internet, powering everything from streaming platforms like Netflix to cloud services like Google Drive. These facilities require immense amounts of energy to store, process, and transmit data. According to the International Energy Agency (IEA), data centers account for 1% of global electricity demand, a figure expected to grow with the rise of cloud computing.

Events like the COVID-19 pandemic accelerated cloud adoption, as companies and individuals transitioned to remote work, leading to an explosion in virtual meetings, file sharing, and cloud storage. For example, Microsoft reported a 775% increase in demand for cloud services in some regions during 2020. While major providers like Amazon Web Services (AWS) and Google are investing heavily in renewable energy, the rapid growth of cloud usage continues to challenge sustainability efforts.

https://engineering.fb.com/wp-content/uploads/2018/05/data-center-shot.jpg

2. Video streaming boom

The increase in video streaming has become one of the largest contributors to online energy consumption. Services like Netflix, YouTube, and TikTok account for over 60% of internet traffic worldwide.

The release of high-profile events—such as Netflix’s “Squid Game” debut in 2021—demonstrates the scale of the issue. During its first four weeks, the series was streamed for 1.65 billion hours, consuming massive amounts of energy in data processing and transmission. These statistics underline the need for platforms to optimize streaming technologies and encourage users to adopt sustainable viewing habits, such as lowering video resolution where possible.

https://www.marca.com/en/lifestyle/tv-shows/2021/10/01/6157178546163f62728b45ae.html

3. Expansion of internet-connected devices (IoT)

The rise of Internet of Things (IoT) devices is a big deal. From smart speakers like Amazon Alexa to fitness trackers and smart thermostats, these gadgets are everywhere. By 2025, there could be 75 billion IoT devices worldwide, a massive jump from just 8 billion in 2017.

This boom in connected devices means more electricity use and more e-waste. Each device needs rare earth metals, complex manufacturing, and constant power, all adding to the carbon footprint of our digital world. Plus, the rollout of 5G to keep these devices running smoothly has pushed energy demands even higher with the need for more infrastructure.

4. Cryptocurrency mining

Cryptocurrency mining, particularly Bitcoin, is one of the most energy-intensive activities in the digital space. Bitcoin mining alone consumes more electricity annually than entire countries like Argentina, with estimates placing its energy usage at 121.36 terawatt-hours (TWh). Fun fact – The Eiffel Tower uses about 7.8 GWh annually to light up and operate. With 121.36 TWh , you could power the Eiffel Tower for over 15,500 years.

https://www.bbc.com/news/science-environment-56215787

5. AI and ChatGPT

AI systems like ChatGPT have a significant environmental footprint, driven by their high energy consumption. Each ChatGPT request uses about 2.9 watt-hours of electricity, which is ten times the energy required for a Google search (0.3 watt-hours). With 100 million weekly users sending around 15 prompts each, ChatGPT’s yearly energy use totals approximately 226.82 million watt-hours—enough to charge over 3 million electric vehicles or meet the energy needs of several small countries. Developing these AI models is also resource-intensive, training GPT-4 consumed more than 62 million kilowatt-hours, costing $8.2 million in electricity alone. These figures highlight the need for innovation in energy-efficient AI systems and a shift toward renewable energy sources. Balancing the rapid advancement of AI with environmental sustainability is becoming increasingly important.