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Artificial Intelligence for Societal Impact


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Advances in machine learning have impacted our daily and work lives and we see numerous applications of these technologies in online products and services. Through our work at Microsoft Research India, we are finding that ML can have an equally transformative effect on societally important problems such as healthcare, public awareness and education. This talk discusses the potentials and challenges of using AI for societal impact projects and presents two case studies—helping health workers ensure adherence to tuberculosis medication and on enabling better online support for mental health.

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Artificial Intelligence for Societal Impact

  1. 1. AI FOR SOCIETAL IMPACT Amit Sharma Researcher, Microsoft Research India @amt_shrma MICROSOFT AXLE, AI FOR GOOD
  2. 2. Artificial Intelligence is having a transformative impact
  3. 3. AI for Societal Impact? Can we use recent advances in Machine learning and data science to help with societal problems? • Healthcare • Education • Financial Inclusion • Governance • Public awareness • Environmental Sustainability
  4. 4. Yes, but correlations and predictions can lead us astray. Can we use recent advances in Machine learning and data science to help with societal problems? •Medication adherence forTuberculosis •Counselling support in mental health forums
  5. 5. • Suffers from same limitations Image credit: Emre Kiciman
  6. 6. PART I: HELPINGTB HEALTH WORKERS BE MORE EFFECTIVE In collaboration with Everwell, a startup based on a Microsoft Research project, 99Dots. Learning to Prescribe Interventions forTuberculosis Patients using Digital Adherence Data. Killian et al. (2019)
  7. 7. Helping patients adhere toTB treatment is important but difficult •TB is the leading infectious cause of death globally •TB treatment takes 6 months or more •Poor adherence to treatment increases risk of relapse, drug resistance, and death •India’s governmentTB program has used Directly Observed Treatment (DOT) to monitor adherence, but effort-intensive for patients and providers
  8. 8. Possible solution: 99Dots, a digital adherence technology
  9. 9. Background: How 99Dots works * Slide content sourced from Everwell.
  10. 10. Combination of Caller ID and numbers called shows that doses are in patient’s hands. Background: How 99Dots works * Slide content sourced from Everwell.
  11. 11. Background: Rapid growth with Govt. of India * Slide content sourced from Everwell.
  12. 12. Background: Reminders & analytics on 99Dots ICT enables remote observation + analytics:  Reduces patient burden  Increases provider efficiency  Enables differentiated care Two of your patients have missed doses today: Raj & Om SMS Message: Two of your patients have missed doses today: Raj & Om [0000] Please take pills SMS Message: [0000] Please take pills Reminders to Patients Alerts to Providers Analytics for Supervisors * Slide content sourced from Everwell.
  13. 13. How canAI/Machine learning be useful?
  14. 14. Q1: Can we predict a patient’s adherence in advance? Q2: Can we predict final treatment outcome (after 6 months) based on initial patient adherence (first few weeks)? GOAL: help health workers prioritize their visits to patients.
  15. 15. Data • Accessed anonymized data on pastTB patients who had used 99Dots • For each patient, collected their: • Adherence pattern: Calls made to 99Dots • Treatment outcome at the end of 6 months: • Positive: Cured/Treatment Completed • Negative: Lost to follow up,Treatment Failure, Death • Preprocessing • Many people change numbers • Some calls are manually added • Some patients’ treatment ends early • Real-world data is messy!
  16. 16. What does adherence look like for patients with a positive treatment outcome?
  17. 17. What does adherence look like for patients with a negative treatment outcome?
  18. 18. 2 Key questions • How to help health workers reprioritize their interventions? • Looking at a week’s data, can we predict adherence for the next week? • Looking at first few weeks, can we predict the final treatment outcome?
  19. 19. Machine learning task • Input (t-7,t) • demographic features (age, gender, location) • Call details (number of calls, time of calls, days between calls, etc.) • Output (t, t+7) • Number of calls in the next week
  20. 20. Obtain nearly 0.85 AUC.
  21. 21. Tale ofTwo worlds • Person makes no calls in week 1, intervention, starts making calls in week 2 • Person makes no calls in week 1, intervention, no calls in week 2
  22. 22. A causal model for interventions Person’s Behavior (t) Health worker’s intervention Call to 99Dots (t) Person’s Behavior (t-1) Call to 99Dots (t-1)
  23. 23. Domain-based filtering solution • 99Dots records suggested attention level for each patient • High: 4 or more calls missed in the last week • Medium: 1 to 4 calls missed in the last week • Low: No missed calls • Medium -> High? • Given last week’s data, can we predict whether a person moves from Medium to High attention ?
  24. 24. Three models: 1. Number of calls missed 2. Random Forest model 3. Deep neural network (LSTM+dense layer)
  25. 25. Complex model, but are able to save more missed doses
  26. 26. PART II: COUNSELLING SUPPORT FOR MENTAL HEALTH In collaboration withTalklife, a global mental health forum Moments of Change: Analyzing Peer-Based Cognitive Support in Online Mental Health Forums. Pruksachatkun, Pendse and Sharma (ACM CHI 2019)
  27. 27. Mental health:A neglected problem • 10-15% of Indians are suffering from common mental disorders • Depresssion, Anxiety, Post-Traumatic Stress Disorder, … • 5000 psychiatrists and 2000 clinical psychologists, almost all in urban India • 0.06% of govt. health budget spent on mental health National Mental Health Survey of India 2015–2016. Murthy. Prevalence of Mental Disorders in India and Other South Asian Countries (2017). Ranjan and Asthana.
  28. 28. Varied reasons Agrarian distress Violence against women and adolescents Traumatic experiences Urban lifestyle Work pressure, exam pressure Lack of job opportunities, …
  29. 29. How can AI/machine learning help?
  30. 30. How can AI/Machine learning help? • Help us understand how people support each other for mental health issues • What are generalizable signs of effective support? • How can we route experienced counsellors to people who need the most help?
  31. 31. Talklife: thousands of “counselling” conversations online • A social network for peer support • People experiencing mental distress can post onTalklife and get support from their peers. • Global network, but also has Indian users Can we identify patterns of successful peer support conversations? “Moments of cognitive change”
  32. 32. Ground-truth: Moments of change • Various definitions in clinical psychology. • Need a quantitative definition. Moment of change: A change in sentiment for the original poster on a topic that they initially talked about.
  33. 33. How do we get labelled data? "Thank you very much sir! But every now and then somebody asksWhat's there in what you are doing?You at least had IIT label. I may be negative a bit but that's a bitter truth that IIT and DU can't be compared in any way " Sentiment -3 -2 -1 0 1 2 3 Topics ___________________________(Inter-rater reliability was 0.4437 Fleiss’ Kappa) Also collected high-precision, large-scale labels based on common phrases, e.g., “Thanks, I feel better now!”
  34. 34. Can we predict forum threads that have a moment of change?
  35. 35. What Factors May Feed into a MOC? (Baseline) ● LIWC (Linguistic Inquiry andWord Count)-based features [1] ● Punctuation-based Features ● Mental Health Language Based (swear words, n-grams for anxiety, depression, and suicide). [2] ● Metadata-based References [1] Pennebaker, J.W., Boyd, R. L., Jordan, K., and Blackburn, K.The development and psychometric properties of liwc2015.Tech. rep., 2015 [2] Baumgartner, J.Complete public reddit comments corpus.
  36. 36. Can identify moments of change with accuracy = 0.90 Wait, correlations can lead us astray!
  37. 37. What happens with different demographics? Indicates a difference in how people from different cultures express mental health, supported by past medical anthropological work.
  38. 38. Do we really understand the mechanisms of what is going on in these threads?
  39. 39. Post: “I don’t understand why my mom, or any of my friends like me. I’m such a bad person. I don’t deserve their love.” Collected label: [“mother”, “boyfriend”, “ “sister”, “dog”, “father”, “relationship”] Here’s an example
  40. 40. Take 2: Sense2Vec + Clustering-Based
  41. 41. Step 1: Merging Ears Ach e Eyes Ears Cold Legs Ears Legs Cold Eyes Ache
  42. 42. Step 2: Pruning; Iterate. Ears Teacher Eyes Ears Eyes
  43. 43. Detect topics and sentiment simultaneously
  44. 44. “Senti-Topic” can detect moments of change with 70-80% accuracy Given a thread conversation, does it contain a moment of change? • Accuracy > 0.8 Given a post, does it contain a moment of change? Do not use text of the post, but posts in the same thread before it • Accuracy > 0.7
  45. 45. Practical implications • Routing support: Experienced supporters or counsellors can be routed in real-time to conversations that are not going well. • Helping supporters be effective: Can identify supporters who are participating in conversations with successful moments of change and help to design personalized training for those who are less effective. • Cross-cultural implications: Need culture-specific models of prediction. Same model unlikely to work. • Currently trying to interpret the models to understand what topics, and linguistic markers of peer support are more associated with moments of change.
  47. 47. What’s different this time with AI? Technology and Societal Impact • Computers[1990s] • Internet [2000s] • Mobile phones [2010s] At Microsoft Research India, we have been working on technology for societal problems for more than a decade. “Technology is an amplifier of social forces.” -- KentaroToyoma, Geek Heresy
  48. 48. How to makeAI/ML useful: Our efforts at Microsoft Research -- Finding the right problem where ML can have impact --Working with domain experts/organizations that have deep expertise -- Focus on “Implement, Deploy, test” -- Have the right expectation of “success” MSR Collaborative projects with academia, social enterprises and NGOs. collaborative-projects-on-cloud-and-ai-technologies/
  49. 49. Thank you! Amit Sharma @amt_shrma MSR India Collaborative Projects 2018-19 Papers: 1. Learning to Prescribe Interventions for Tuberculosis Patients using Digital Adherence Data. Killian et al. (2019) 2. Moments of Change: Analyzing Peer-Based Cognitive Support in Online Mental Health Forums. Pruksachatkun, Pendse and Sharma (ACM CHI 2019)