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Ranak Trivedi, "Bridging the Gap Between Artificial Intelligence and Natural Connections" 

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Ranak Trivedi, "Bridging the Gap Between Artificial Intelligence and Natural Connections" 

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March 24, 2020

This event will highlight the challenges and opportunities in harnessing artificial intelligence (AI) technologies to serve the needs of individuals with disabilities and dependencies. AI can improve the lives of people with disabilities, such as smart devices supporting people with physical disabilities or sight loss. On the other hand, AI outputs can also reflect discriminatory biases present in the underlying data used to develop the algorithms. While this “garbage in, garbage out” principle is well documented in respect to AI and gender or race, it is understudied in respect to disability or dependencies.

Interdisciplinary panels of legal scholars, ethicists, AI developers, medical and service providers, and advocates with disabilities/ dependencies will explore best practices and guidelines for stakeholders, guided by ethical principles, legal considerations, and the needs of people with disabilities/ dependencies. Participants will seek to articulate clear criteria for developers and medical providers looking to harness the potential of AI to serve individuals with disabilities/ dependencies, including those whose disabilities/ dependencies are the result of aging, injury, or disease, and the caregivers -- including both professionals and unpaid friends and families -- who support some of these individuals.

This webinar was free and open the public.
For more information, visit our website at https://petrieflom.law.harvard.edu/events/details/artificial-intelligence-and-disability-dependency

March 24, 2020

This event will highlight the challenges and opportunities in harnessing artificial intelligence (AI) technologies to serve the needs of individuals with disabilities and dependencies. AI can improve the lives of people with disabilities, such as smart devices supporting people with physical disabilities or sight loss. On the other hand, AI outputs can also reflect discriminatory biases present in the underlying data used to develop the algorithms. While this “garbage in, garbage out” principle is well documented in respect to AI and gender or race, it is understudied in respect to disability or dependencies.

Interdisciplinary panels of legal scholars, ethicists, AI developers, medical and service providers, and advocates with disabilities/ dependencies will explore best practices and guidelines for stakeholders, guided by ethical principles, legal considerations, and the needs of people with disabilities/ dependencies. Participants will seek to articulate clear criteria for developers and medical providers looking to harness the potential of AI to serve individuals with disabilities/ dependencies, including those whose disabilities/ dependencies are the result of aging, injury, or disease, and the caregivers -- including both professionals and unpaid friends and families -- who support some of these individuals.

This webinar was free and open the public.
For more information, visit our website at https://petrieflom.law.harvard.edu/events/details/artificial-intelligence-and-disability-dependency

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Ranak Trivedi, "Bridging the Gap Between Artificial Intelligence and Natural Connections" 

  1. 1. Bridging the Gap Between Artificial Intelligence and Natural Connections Ranak B. Trivedi, PhD Assistant Professor, Department of Psychiatry and Behavioral Sciences, Stanford University Core Investigator, Center for Innovation to Implementation, VA Palo Alto March 24 2020 @ranaktrivedi
  2. 2. Outline • The Need. • The Promise. • The Gaps. • The Recommendations.
  3. 3. The Need. • Worldwide, 15% of people are differently abled • Largest predictor of disability is age • 16M individuals are living with cognitive disability in the US • By 2050, the percentage of the world’s population 60 y+ will be 2B • Familysize is declining • 2010: 7:1 informal caregivers: 80 y+ • 2050: 3:1 informal caregivers 80 y+
  4. 4. Goals AI Method Examples Monitoring Health, Assessing Risk Machine learning, NLP, speech recognition Wearables (e.g.,Apple Watch), Smart Clothing Managing Physical and Mental Health Conversational AI, NLP, speechrecognition, chatbots Omada Health, Livingo Coordinating Care Robotics, Conversational AI Voice Activated Digital Assistants, EllieQ Assisting with ADL Robotics Robear Companionship Conversational AI, Social Assistive Robotics Voice Activated Digital Assistants (e.g., Amazon Echo),Paro Monitoring Falls Biometric remote monitoring Biotricity The Promise: AI tools are transforming care
  5. 5. Gap 1: Limited Attention to Social Process • Social support improves longevity, health, and mitigates adverse effects of chronic and serious conditions. • Two types of social support: Emotional, Instrumental • Current status • Wearables focus on individual change • Simplistic focus e.g., gamification and virality • Absence of principles from socialtheories
  6. 6. Gap 2: Limited Focus on Informal Caregivers • In USA, 50M adults provide $470 billion worth of unpaid services • Average of 18 hours/week • Limited attention to the needs of caregivers • Remote monitoring provides actionable information to caregivers, but not “how” or emotional support $398.1 B $345.4 B $321.4 B
  7. 7. The Recommendations. 1) Harness the power of social processes • Motivation: Intrinsic vs Extrinsic • Social contagion: Behaviors and therefore behavior change can spread 2) Rely on behavior and socialtheory: • Technology-enabled caregivingat home (TECH) • Dyadic health behavior change model 3) Embed culturalconsiderations in design • Engage diverse teams and stakeholders at all stages of development • Engage caregivers from different cultural backgrounds
  8. 8. The Gerontologist, Volume 60, Issue Supplement_1, March 2020, Pages S41–S49, https://doi.org/10.1093/geront/gnz178 The content of this slide may be subject to copyright: please see the slide notes for details. Technology-enabled caregiving in the home (TECH).
  9. 9. Patient Stress Depression Self-efficacy Social Support Functional Limitations Stress Coping Caregiver Stress Depression Self-efficacy Caregiver Burden Social Support Stress Coping Self-management Stress Management Depressive Symptoms Medication Adherence Symptom Management Diet Exercise Relationship Dyadic Coping Communication Reciprocity Collaboration Biomarkers Quality of life Hospitalizations Mortality Cost Dyadic Health Behavior Change Model (Trivedi et al 2016)
  10. 10. Summary • All individuals, but especially those who are differently abled and dependent, rely on social connections • AI should harness the power of these connections into applications • AI should do this with sensitivity to culturaldifferences
  11. 11. A heartfelt smile, a gentle tone, A thoughtful word, a tender touch, A passing act of kindness done- ‘Tis all, but is much. These are not things that win applause, No earthly fame awaiteth such, But surely by the heavenly laws, They are accounted much. -Anonymous
  12. 12. Thank you Ranak Trivedi, PhD ranakt@Stanford.edu https://framilyveteranhealth.sites.stanford.edu @ranaktrivedi

Hinweis der Redaktion

  • From 2000 to 2050, the percentage of the world’s population who is 60 years of age and older will almost double from about 12% to 22% (from 605 million to 2 billion). 

    in 2010, more than 7 potential informal caregivers aged 45-64 years were available for every person who is in a high-risk group of 80 years or older, a ratio of 7:1. This number is expected to drop to 4:1 by 2030 and 3:1 in 2050.
  • Limited engagement of caregivers when developing tools that would directly/indirectly benefit these individuals
  • Figure 1. Technology-enabled caregiving in the home (TECH).

    Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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