In 2023, a Belgian man reportedly ended his life after developing eco-anxiety and confiding in an AI chatbot over six weeks about the future of the planet. Without those conversations, his widow reportedly told the Belgian outlet La Libre, "he would still be here". In April this year, a 35-year-old Florida man was shot and killed by police in another chatbot-related incident: his father later told media that the man had come to believe an entity named Juliet was trapped inside ChatGPT, and then killed by OpenAI. When the man, who reportedly struggled with bipolar disorder and schizophrenia, was confronted by police, he allegedly charged at them with a knife. The wide availability of chatbots in the past few years has apparently led some to believe there is a ghost in the machine – one that is conscious, capable of loving and being loved.

Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and causes of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-cause attributions. We present an empirical Natural Language Processing (NLP) approach to detect annotation inconsistencies and adopt a cross-validation-like paradigm to identify problematic instances. We analyzed 267,804 suicide death incidents between 2003 and 2020 from the NVDRS. Our results showed that incorporating the target state's data into training the suicide-crisis classifier brought an increase of 5.4% to the F-1 score on the target state's test set and a decrease of 1.1% on other states' test set. To conclude, we demonstrated the annotation inconsistencies in NVDRS's death investigation notes, identified problematic instances, evaluated the effectiveness of correcting problematic instances, and eventually proposed an NLP improvement solution.

The world is witnessing a severe mental health crisis. According to a report by the WHO, around 56 million Indians suffer from depression, and 38 million experience some kind of anxiety disorder. The country's mental health workforce is also gravely understaffed with there being a severe shortage of psychiatrists and psychologists compared to the number of people suffering from mental illness. Artificial Intelligence (AI) can prove to be a powerful tool in the fight against the mental health crisis. Reduces the burden on mental health professionals: AI can provide immense support to mental health professionals.

Another week, another privacy horror show: Crisis Text Line, a nonprofit text message service for people experiencing serious mental health crises, has been using "anonymized" conversation data to power a for-profit machine learning tool for customer support teams. Crisis Text Line's response to the backlash focused on the data itself and whether it included personally identifiable information. But that response uses data as a distraction. That's the real travesty--when the price of obtaining mental health help in a crisis is becoming grist for a machine learning mill. And it's not just users of CTL who pay; it's everyone who goes looking for help when they need it most.

"Hi, How are you doing today?" 'What's going on in your world right now?" "How do you feel?" All these questions might seem like simple questions of a caring friend. But, in the present days, they can also be the start of a conversation. "With the global pandemic, mental health has become not only a broader societal issue but a top workplace challenge. It has a profound impact on individual performance, team effectiveness, and organizational productivity.

This article is part of the Policing and Technology Project, a collaboration between Future Tense and the Tech, Law, & Security Program at American University Washington College of Law that examines the relationship between law enforcement, police reform, and technology. There's a man pacing back and forth in the grocery store parking lot, evidently agitated, shouting at the sky. On the phone, a police dispatcher reassures you that someone is coming over to help--and so is a drone. Soon, you hear the telltale buzz of a drone overhead. Through its camera, someone is watching the agitated man in the parking lot, feeding information back to emergency services.

An algorithm to predict which people may experience a mental health crisis has been trialled in the UK and found effective enough for routine use. A version that would track people's mobile phone calls, messages and location in a bid to improve accuracy is now being considered. Birmingham and Solihull Mental Health NHS Foundation Trust worked with Alpha, a division of Spanish telecomms firm Telefonica, which owns O2, to see if there was any benefit in automatically flagging the people thought most at risk of experiencing a mental health crisis to NHS staff. The results of the Predictive Analytics project, released under freedom of information rules, suggest there is. The project ran between November 2018 and May 2019. Alpha developed a machine-learning algorithm fed with historical patient data to predict who could face an imminent crisis.

Years from now, there may be a shortage of psychiatrists in the United States as their mental health system is already overburdened and the growth in demand for the services of psychiatrists outpaces supply, according to a 2017 report from the National Council for Behavioral Health. Some proponents say that, by then, artificial intelligence, an unlikely tool, may be ready to help the mental health practitioners mitigate the impact of the deficit. Artifical intelligence and medicine are made for each other, it has shown promise in diagnosing disease, interpreting images and even zeroing in on treatment plans. Even though psychiatry is a unique human field in many ways, requiring emotional intelligence and perception that computers can't stimulate, even here, AI could have an impact. The field could benefit from AI's ability to analyze data and pick up on patterns and warning signs that are very subtle, humans might never notice them.

Five years from now, the U.S.' already overburdened mental health system may be short as many as 15,600 psychiatrists as the growth in demand for their services outpaces supply, according to a 2017 report from the National Council for Behavioral Health. But some proponents say that, by then, an unlikely tool--artificial intelligence--may be ready to help mental health practitioners mitigate the impact of the deficit. Medicine is already a fruitful area for artificial intelligence; it has shown promise in diagnosing disease, interpreting images and zeroing in on treatment plans. Though psychiatry is in many ways a uniquely human field, requiring emotional intelligence and perception that computers can't simulate, even here, experts say, AI could have an impact. The field, they argue, could benefit from artificial intelligence's ability to analyze data and pick up on patterns and warning signs so subtle humans might never notice them.

Five years from now, the U.S.' already overburdened mental health system may be short as many as 15,600 psychiatrists as the growth in demand for their services outpaces supply, according to a 2017 report from the National Council for Behavioral Health. But some proponents say that, by then, an unlikely tool--artificial intelligence--may be ready to help mental health practitioners mitigate the impact of the deficit. Medicine is already a fruitful area for artificial intelligence; it has shown promise in diagnosing disease, interpreting images and zeroing in on treatment plans. Though psychiatry is in many ways a uniquely human field, requiring emotional intelligence and perception that computers can't simulate, even here, experts say, AI could have an impact. The field, they argue, could benefit from artificial intelligence's ability to analyze data and pick up on patterns and warning signs so subtle humans might never notice them.