Tuesday, June 26, 2018

Facial recognition, values, and the human brain

By Elisabeth Hildt

Image courtesy of Pixabay.
Research is not an isolated activity. It takes place in a social context, sometimes influenced by value assumptions and sometimes accompanied by social and ethical implications. A recent example of this complex interplay is an article, “Deep neural networks can detect sexual orientation from faces” by Yilun Wang and Michal Kosinski, accepted in 2017 for publication in the Journal of Personality and Social Psychology.

In this study on face recognition, the researchers used deep neural networks to classify the sexual orientations of persons depicted in facial images uploaded on a dating website. While the discriminatory power of the system was limited, the algorithm was reported to have achieved higher accuracy in the setting than human subjects. The study can be seen in the context of the “prenatal hormone theory of sexual orientation,” which claims that gay men and women tend to have gender-atypical facial morphology.

Tuesday, June 19, 2018

Disrupting diagnosis: speech patterns, AI, and ethical issues of digital phenotyping

By Ryan Purcell, PhD

Jim Schwoebel, presenter at April The Future Now: (NEEDS)
Diagnosing schizophrenia can be complex, time-consuming, and expensive. The April seminar on The Future Now: (NEEDs) Neuroscience and Emerging Ethical Dilemmas at Emory focused on one innovative effort to improve this process in the flourishing field of digital phenotyping. Presenter and NeuroLex founder and CEO Jim Schwoebel had witnessed his brother struggle for several years with frequent headaches and anxiety, and saw him accrue nearly $15,000 in medical expenses before his first psychotic break. From there it took many more years and additional psychotic episodes before Jim’s brother began responding to medication and his condition stabilized. Unfortunately, this experience is not uncommon; a recent study found that the median period from the onset of psychotic symptoms until treatment is 74 weeks. Naturally, Schwoebel thought deeply about how this had happened and what clues might have been seen earlier. “I had been sensing that something was off about my brother’s speech, so after he was officially diagnosed, I looked more closely at his text messages before his psychotic break and saw noticeable abnormalities,” Schwoebel told Psychiatric News. For Schwoebel, a Georgia Tech alum and co-founder of the neuroscience startup accelerator NeuroLaunch, this was the spark of an idea. Looking into the academic literature he found a 2015 study led by researchers from Columbia University who applied machine learning to speech from a sample of participants at high risk for psychosis. They found that the artificial intelligence correctly predicted which individuals would transition to psychosis over the next several years.

Tuesday, June 12, 2018

Ethical Concerns Surrounding Psychiatric Treatments: Do Academics Agree with the Public?

By Laura Y. Cabrera, Rachel McKenzie, Robyn Bluhm

Image courtesy of the
U.S. Airforce Special Operations Command.
Treatments for psychiatric disorders raise unique ethical issues because they aim to change behaviors, beliefs, and affective responses that are central to an individual’s sense of who they are. For example, interventions for depression aim to change feelings of guilt and worthlessness (as well as depressed mood), while treatments for obsessive-compulsive disorder try to diminish both problematic obsessive beliefs and compulsive behaviors. In addition to the specific mental states that are the target of intervention, these treatments can also affect non-pathological values, beliefs, and affective responses. The bioethics and neuroethics communities have been discussing the ethical concerns that these changes pose for individual identity [1,2], personality [3,4], responsibility [5], autonomy [6,7], authenticity [8], and agency [9,10]. 

Tuesday, June 5, 2018

Participatory Neuroscience: Something to Strive For?

By Phoebe Friesen

Image courtesy of Pixabay.
In the last few decades, there has been an increasing push towards making science more participatory by engaging those who are part of or invested in the community that will be impacted by the research in the actual research process, from determining the questions that are worth asking, to contributing to experimental design, to communicating findings to the public. Some of this push stems from the recognition that research is always value-laden and that the values guiding science have long been those of an elite and unrepresentative few (Longino, 1990). This push also has roots in feminist standpoint theory, which recognizes the way in which marginalized individuals may have an epistemic advantage when it comes to identifying problematic assumptions within a relevant knowledge project (Wylie, 2003). Additionally, many have noted how including the voices of those likely to be impacted by research can support the process itself (e.g. by identifying meaningful outcome measures) (Dickert & Sugarman, 2005). As a result, participatory research is becoming widely recognized as having both ethical and epistemic advantages. The field of neuroscience, however, which takes the brain as its primary target of investigation, has been slow to take up such insights. Here, I outline five stages of participatory research and the uptake of neuroscientific research in each, discuss the challenges and benefits of engaging in such research, and suggest that the field has an obligation, particularly in some cases, to shift towards more participatory research.