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o Clear communication: Mental health professionals should clearly explain how the AI system functions, its
data sources, and its role in patient care.
• Non-maleficence (Do Not Harm)
o Minimise harm: The AI should undergo rigorous testing to ensure it does not disproportionately misclassify
or harm specific groups, such as low-income individuals.
o Human oversight: Predictions made by the AI should be reviewed by mental health professionals to mitigate
potential harm.
• Beneficence (Maximum Benefits)
o Benefit to all: The system should be retrained to assist all patients, especially those traditionally overlooked
or underserved, by including diverse data from varied socio-economic, racial, and demographic groups.
o Data diversity: The training dataset must be expanded to better represent all populations and improve the
system's accuracy.
• Justice
o Fairness: The AI should treat all groups equitably and avoid reinforcing existing disparities.
o Address bias: Developers must critically assess the dataset and address any systemic biases, such as those
related to race, gender, or class, in the AI model.
Proposed Solutions
• Improved data collection: Gather data from a wide range of individuals across different socio-economic and
racial backgrounds to build a more inclusive and balanced model.
• Bias detection and mitigation: Implement advanced techniques to identify and eliminate biases in the dataset
and the AI's algorithms.
• Human review: Mental health professionals should actively monitor and validate AI predictions to ensure
accuracy and fairness.
• Privacy protections: Ensure robust data security and privacy measures for personal data, especially from social
media. Patients should also have the ability to withdraw consent for data usage.
By applying these bioethics principles, the AI system can be made more accurate, fair, and respectful of patient
privacy. This will help ensure that the system benefits everyone, regardless of their background.
At a Glance
• Al is the top trending technology of this digital era. Most of the companies use Al to accomplish their mundane tasks
and achieve their company's long-term goals.
• Artificial Intelligence comprises three key domains: Statistical data, Computer Vision, and Natural Language Processing.
• Computer Vision is the domain of artificial intelligence that empowers computers to interpret, analyse and understand
an image or a video by collecting information from pixels.
• Natural language is the language used by humans to communicate with each other by writing or speaking.
• NLP works with two main types of data: text and speech.
• Some of the major challenges related to AI are Job Loss, Privacy risks, AI mistakes, Autonomous weapons, Bias and
discrimination, Environmental impact.
• Ethical frameworks provide a valuable tool that help us in dealing with complicated moral issues.
• Ethical Framework of AI is divided into two main types :Sector Based and Value Based.
• Sector-based Ethical Frameworks focus on an ethical challenge specific to a field or industry.
• Bioethics is an interdisciplinary framework used in healthcare to solve tough ethical problems.
• The four primary principles of bioethics are: Respect of Autonomy, Beneficence, Non- Maleficence, Justice.
Revisiting AI Project Cycle & Ethical Frameworks for AI 105

