Automating autism: Disability, discourse, and Artificial Intelligence

As Artificial Intelligence (AI) systems shift to interact with new domains and populations, so does AI ethics: a relatively nascent subdiscipline that frequently concerns itself with questions of “fairness” and “accountability.” This fairness-centred approach has been criticized for (amongst other things) lacking the ability to address discursive, rather than distributional, injustices. In this paper I simultaneously validate these concerns, and work to correct the relative silence of both conventional and critical AI ethicists around disability, by exploring the narratives deployed by AI researchers in discussing and designing systems around autism. Demonstrating that these narratives frequently perpetuate a dangerously dehumanizing model of autistic people, I explore the material consequences this might have. More importantly, I highlight the ways in which discursive harms—particularly discursive harms around dehumanization—are not simply inadequately handled by conventional AI ethics approaches, but actively invisible to them. I urge AI ethicists to critically and immediately begin grappling with the likely consequences of an approach to ethics which focuses on personhood and agency, in a world in which many populations are treated as having neither. I suggest that this issue requires a substantial revisiting of the underlying premises of AI ethics, and point to some possible directions in which researchers and practitioners might look for inspiration

Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach

Ovaj rad postavlja temelje za novi zadatak u ADORE protokolu - Free-play analize. Ključni problem kojega je potrebno riješiti je izraditi precizan, robustan i brz detektor objekata. Ovaj rad razmatra pristup dubokog učenja glede detekcije i klasifikacije objekata u zadatku praćenja ponašanja djeteta. Ovaj zadatak se primjenjuje u kontekstu dijagnoze autizma, koji se oslanja na ADOS protokol sa svojim standardiziranim igračkama. Ponašanje djeteta se odred̄uje pomoću djetetove igre sa igračkama, te za potrebe detekcije standardiziranog seta igračaka i djeteta, razvijen je novi skup podataka. Takod̄er, u ovom radu su se razvili algoritmi i mehanizmi za odred̄ivanje djetetove pažnje na temelju igračaka s kojima se dijete igra. U ovom se radu raspravlja o izazovima s kojima se susretalo tokom rada i takod̄er sa rješenjima istih, te se otvara prostor za nastavak razvoja robusnog i preciznog sustava za analizu pažnje djeteta.This research sets the ground for the new task in ADORE protocol - the free play observation. The core problem to solve is to develop accurate, robust and fast object detector. This research have proposed Deep Learning approach for object detection and classification for the child behavior observation task. The task is used in the context of autism diagnostics, which uses the ADOS protocol with it’s standardized toys. Child behavior has been determined by it’s playing with the toys, and in order to detect the toys and the child, a new dataset has been developed. Also, this research had developed algorithms and mechanism to determine child’s attention based on the toys that child is playing with. This thesis discuses the challenges encountered in the research and their solutions, and as well sets the work for continuing the development of robust and accurate attention analyzer

Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach

Ovaj rad postavlja temelje za novi zadatak u ADORE protokolu - Free-play analize. Ključni problem kojega je potrebno riješiti je izraditi precizan, robustan i brz detektor objekata. Ovaj rad razmatra pristup dubokog učenja glede detekcije i klasifikacije objekata u zadatku praćenja ponašanja djeteta. Ovaj zadatak se primjenjuje u kontekstu dijagnoze autizma, koji se oslanja na ADOS protokol sa svojim standardiziranim igračkama. Ponašanje djeteta se odred̄uje pomoću djetetove igre sa igračkama, te za potrebe detekcije standardiziranog seta igračaka i djeteta, razvijen je novi skup podataka. Takod̄er, u ovom radu su se razvili algoritmi i mehanizmi za odred̄ivanje djetetove pažnje na temelju igračaka s kojima se dijete igra. U ovom se radu raspravlja o izazovima s kojima se susretalo tokom rada i takod̄er sa rješenjima istih, te se otvara prostor za nastavak razvoja robusnog i preciznog sustava za analizu pažnje djeteta.This research sets the ground for the new task in ADORE protocol - the free play observation. The core problem to solve is to develop accurate, robust and fast object detector. This research have proposed Deep Learning approach for object detection and classification for the child behavior observation task. The task is used in the context of autism diagnostics, which uses the ADOS protocol with it’s standardized toys. Child behavior has been determined by it’s playing with the toys, and in order to detect the toys and the child, a new dataset has been developed. Also, this research had developed algorithms and mechanism to determine child’s attention based on the toys that child is playing with. This thesis discuses the challenges encountered in the research and their solutions, and as well sets the work for continuing the development of robust and accurate attention analyzer

Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach

Ovaj rad postavlja temelje za novi zadatak u ADORE protokolu - Free-play analize. Ključni problem kojega je potrebno riješiti je izraditi precizan, robustan i brz detektor objekata. Ovaj rad razmatra pristup dubokog učenja glede detekcije i klasifikacije objekata u zadatku praćenja ponašanja djeteta. Ovaj zadatak se primjenjuje u kontekstu dijagnoze autizma, koji se oslanja na ADOS protokol sa svojim standardiziranim igračkama. Ponašanje djeteta se odred̄uje pomoću djetetove igre sa igračkama, te za potrebe detekcije standardiziranog seta igračaka i djeteta, razvijen je novi skup podataka. Takod̄er, u ovom radu su se razvili algoritmi i mehanizmi za odred̄ivanje djetetove pažnje na temelju igračaka s kojima se dijete igra. U ovom se radu raspravlja o izazovima s kojima se susretalo tokom rada i takod̄er sa rješenjima istih, te se otvara prostor za nastavak razvoja robusnog i preciznog sustava za analizu pažnje djeteta.This research sets the ground for the new task in ADORE protocol - the free play observation. The core problem to solve is to develop accurate, robust and fast object detector. This research have proposed Deep Learning approach for object detection and classification for the child behavior observation task. The task is used in the context of autism diagnostics, which uses the ADOS protocol with it’s standardized toys. Child behavior has been determined by it’s playing with the toys, and in order to detect the toys and the child, a new dataset has been developed. Also, this research had developed algorithms and mechanism to determine child’s attention based on the toys that child is playing with. This thesis discuses the challenges encountered in the research and their solutions, and as well sets the work for continuing the development of robust and accurate attention analyzer