PSYC 301 - New Frontiers in Brain Health - AI

what is anticipatory ethics (2)

• process of accessing the potential ethical implications of emerging issues to guide their responsible development

• thinking abt the ethics before it is realised

issues with considering anticipatory ethics too soon (2)

• can limit expansion of knowledge

• hard to guess and forecast problems

issues with considering anticipatory ethics too late (2)

• research might go to waste and no room to make changes

• harm might have already happened

what are the 5 features of big data

• volume

• variety

• velocity

• veracity

• value

volume (2)

• number of data points

• e.g. heart rate is measured every second

variety (2)

• data may cross different types

• can be structures or unstructured

velocity (2)

• pace of data generation

• e.g. MRI has lower velocity as it is only done once

veracity (2)

• data quality and accuracy

• measuring what it is intended to measure

value (2)

• potential to create benefits and insights

• can the data be used for that particular purpose

example of the big 5 in health anxiety on Reddit around COVID (5)

• volume: posts from 820,000 users

• variety: multiple boards (r/depression, r/suicide) but all text

• velocity: fixed dataset, collected once

• veracity: automated data processing

• value: could identify vulnerable groups and mental health themes in real time

current brain landscape for brain health data (4)

• patient data

• operations data

• population data

• social determinants of health data

artificial intelligence

artificial systems that appear to think like humans, doing tasks, deciding, categorising, recognising etc

what is an example of an AI system

machine learning

machine learning (2)

• system that can learn from experiences or dataset without direct human programming

• it involves training models on patterns in one set of data (training data) so that they can apply what they have learned to new data

what can machine learning produce

“black box” results that are difficult for human interpreter to explain

what are the 2 types of machine learning

• supervised learning

• unsupervised learning

supervised learning (3)

• models are trained on known, labelled data, having the “right answer”

• requires a huge volume of data and human labour

• e.g. training a machine learning algorithm on a set of patient records on who do vs do not develop psychosis

unsupervised learning (3)

• models learn from unlabelled data

• requires even greater processing power but less human labour

• e.g. twitter posts containing the word dementia

AI applications in neuroscience examples (5)

• risk prediction

• clinical decision making

• neurotech

• brain modelling

• diagnosis and prognostication

risk prediction (2)

• goal was to predict AD diagnosis using brain scans

• trained machine learning model using labelled MRI data (healthy vs AD) to predict neural activity and identify most predictive brain regions

clinical decision making (2)

• goal was to surgically remove epileptogenic brain region to treat seizures using intracranial EEG

• proposed machine learning to use unlabelled features of raw iEEG output to identify seizure origin

neurotech (2)

• goal was to control limb prosthesis with neural activity

• train machine learning model on the mapping between neural activity and limb movement

brain modelling (3)

• goal was to understand how rat brains represent space

• trained machine learning model to navigate spaces with training data that stimulate real rodent behaviour and neural activity

• model developed representations resembling real rat enthorhinal cortex

diagnosis and prognostication (3)

• wanted to solve the problem of needing to triage acute neurological illness quickly irl

• created supervised machine learning training on heads CTs and radiology annotations

• it accelerated time to diagnosis in stimulated environment

ethical issues around AI in neuroscience (3)

• accountability

• bias and discrimination

• privacy

what are the two types of accountability

• culpability

• moral accountability

culpability (2)

• it is the responsibility based on intention, knowledge or control

• AI introduces potential harms no one person could predict or prevent

moral accountability (2)

• duty to explain one’s reasons and actions to others, being answerable for an action

• AI processes may be unexplainable to their users, the “black box” problem

bias and discrimination (2)

• groups that are understudied will go on to be underrepresented in AI models which may result in receiving lower quality care

• models are normally trained on younger populations as well as more common conditions

privacy (3)

• AI outputs can be sensitive, such as future disease risk, personal preferences, emotional states

• question of who should have access to this data should be controlled esp when predictions include uncertainty

• by linking data, AI may re- identify previously anonymous individuals - there is also a lack of guidelines around informed consent for expanded/ later use

what is within machine learning

generative AI

generative AI

systems that generate new text, images or code based on prior input

example of generative AI

large language models (e.g. ChaptGPT)

ethical issues with generative AI (3)

• emerging harms and benefits

• emotional alignment

• lack of transparency

emerging harms and benefits (3)

• still unclear of whether there is a net benefit or net harm

• high profile of harm with vulnerable populations

• these tools may halt suicide ideation

emotional alignment (2)

• can be used to engage in mental health or engage socially

• tools designed to match users can be positive or negative

lack of transparency (2)

• not clear who is building them

• there is legal ambiguity when data can or can’t be shared