Derrick Aarons

The convergence of Artificial Intelligence (AI), big data methods, and microsystems engineering makes AI-based algorithms for computational neuroscience one of the fastest growing fields of neuro-medical research (IBC, 2021). AI algorithms in clinical neuroscience research may be used to detect early signs of Alzheimer’s disease and mental illness (IBC, 2021), however, ethical issues such as incidental findings and privacy concerns, transparency and bias, and algorithm discrimination arise (Social and Human Sciences Commission, 2021).

Let us commence our re-thinking about lifestyles by looking at the essence of Article 5 of the Universal Declaration on Bioethics and Human Rights, which states that “the autonomy of persons to make decisions, while taking responsibility for those decisions, is to be respected.” Next, we should recognize that lifestyle may be regarded as the way or manner of living for an individual or a group. It reflects the set of values, attitude, habits, and behaviour associated with the person or group, and includes intangible factors such as an individual’s preferences, and outlook on life. In our context, we are concerned about good health, and so would wish everyone to have what we regard as a healthy lifestyle. However, we must be cognizant of the conditions and circumstances under which people live, as well as their personal interests and circumstances under which people live, as well as their personal interests and their behavioural orientations.

Some important concepts in this topic include:  The effects of social media on individual health and public health  Variety - the different types of social media from diverse kinds of sources  The exchange of data through apps and social networks. Data may be classified as: - Personal; - Primary; - Secondary; - Anonymized; and - Metadata.  Velocity – reflecting the very high speed at which data may be collected over social media and processed.  Validity – referring to the quality of the data sent over social media and answers the question whether the data really show what they were meant to show regarding both content and precision.  Value – draws attention to the meaning of the data for a specific question. Here, the context of the data must be taken into account.