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About bitter-sweet.ai

bitter-sweet.ai is a project exploring the psychosocial impact of artificial intelligence on mental health and society. It offers a space for popular science writing, reflections and insights from practice and literature.

The name is a reminder that AI is rarely purely sweet or purely bitter — it can support human flourishing, but also create new pressures, injustices and subtle forms of psychological strain.

Key Sources

Studies and reports we build upon.

The impact of artificial intelligence on learners’ critical thinking and cognitive offloading

Abbas M., Jam F.A., Khan T.I. (2024)

Study showing AI tools can reduce critical thinking skills through cognitive offloading.

Power Hungry Processing: Watts Driving the Cost of AI Deployment?

Luccioni A.S., Viguier S., Ligozat A.L. (2023)

Analysis of energy consumption across AI model types — text, image, and generative models.

Making AI Less Thirsty: Uncovering and Addressing the Secret Water Footprint of AI Models

Li P., Yang J., Islam M.A., Ren S. (2023)

Research quantifying the water consumption of training and running large language models.

Artificial intelligence and illusions of understanding in scientific research

Messeri L., Crockett M.J. (2024)

Nature article on how AI creates illusions of understanding, reducing intellectual diversity.

WHO guidance on the ethics and governance of AI for health

World Health Organization (2021)

WHO framework for ethical AI deployment in healthcare contexts.

The Potentially Large Effects of Artificial Intelligence on Economic Growth

Hatzius J., Briggs J., Kodnani D., Pierdomenico G. (2023)

Goldman Sachs analysis estimating AI could automate 300 million full-time jobs globally.

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

Buolamwini J., Gebru T. (2018)

Landmark study revealing racial and gender bias in commercial facial recognition systems.

Dissecting racial bias in an algorithm used to manage the health of populations

Obermeyer Z., Powers B., Vogeli C., Mullainathan S. (2019)

Science paper showing a widely-used healthcare algorithm systematically underestimated Black patients' health needs.

Highly accurate protein structure prediction with AlphaFold

Jumper J., Evans R., Pritzel A. et al. (2021)

The Nature paper describing AlphaFold 2, which solved the 50-year protein folding problem. Nobel Prize 2024.

International evaluation of an AI system for breast cancer screening

McKinney S.M., Sieniek M., Godbole V. et al. (2020)

Nature study showing AI outperformed radiologists in breast cancer detection.

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

Bender E.M., Gebru T., McMillan-Major A., Shmitchell S. (2021)

Influential FAccT paper warning about environmental costs and biases of large language models.

AI Index Report 2025

Stanford HAI (2025)

Comprehensive annual report tracking AI development, investment, regulation, and societal impact.

Future of Jobs Report 2025

World Economic Forum (2025)

WEF projection: 92M jobs displaced, 170M created by 2030 due to AI and automation.

Learning skillful medium-range global weather forecasting

Lam R., Sanchez-Gonzalez A., Willson M. et al. (2023)

Science paper on DeepMind's GraphCast: AI weather forecasting more accurate than traditional methods.

Dermatologist-level classification of skin cancer with deep neural networks

Esteva A., Kuprel B., Novoa R.A. et al. (2017)

Nature paper demonstrating AI matching dermatologist accuracy in skin cancer classification.