Urgent public attention  is needed to make sense of the expanding use of algorithmic and  data-driven technologies in the mental health context. On the one hand,  well-designed digital technologies that offer high degrees of public  involvement and can be used to promote good mental health and crisis  support in communities. They can be employed safely, reliably and in a  trustworthy way, including to help build relationships, allocate  resources, and promote human flourishing. On the other hand,  there is clear potential for harm. The list of ‘data harms’ in the  mental health context is growing longer, in which people are in worse  shape than they would be had the activity not occurred.  Examples in  this report include the hacking of psychotherapeutic records and the  extortion of victims, algorithmic hiring programs that discriminate  against people with histories of mental healthcare, and criminal justice  and border agencies weaponising data concerning mental health against  individuals. Issues also come up not where technologies are misused or  faulty, but where technologies like biometric monitoring or surveillance  work as intended, and where the very process of ‘datafying’ and  digitising individuals’ behaviour – observing, recording and logging  them to an excessive degree – carry the potential for inherent harm. Part 1 of this report charts the rise  of algorithmic and data-driven technology in the mental health context.  It outlines issues which make mental health unique in legal and policy  terms, particularly the significance of involuntary or coercive  psychiatric interventions in any analysis of mental health and  technology. The section makes a case for elevating the perspective of  people with lived experience of profound psychological distress, mental  health conditions, psychosocial disabilities, and so on, in all activity  concerning mental health and technology. Part 2 looks at  prominent themes of accountability. Eight key themes are discussed –  fairness and non-discrimination, human control of technology,  professional responsibility, privacy, accountability, safety and  security, transparency and explainability, and promotion of public  interest. International law, and particularly the Convention on the  Rights of Persons with Disabilities, is also discussed as a source of  data governance. Case studies throughout show the diversity of  technological developments and draw attention to their real-life  implications. Many case studies demonstrate instances of harm. The case  studies also seek to ground discussion in the actual agonies of existing  technology rather than speculative worries about technology whose  technical feasibility is often exaggerated in misleading and harmful  ways (for example, Elon Musk’s claim that his ‘AI-brain chips will  “solve” autism and schizophrenia’).