TOWARDS BUILDING PEOPLE-CENTRIC AI FOR BUSINESS - THE LONG HAUL

TOWARDS BUILDING PEOPLE-CENTRIC AI
FOR BUSINESS - THE LONG HAUL
Biplav Srivastava*, IBM
21st May 2020
(C) Biplav Srivastava, IBM, May 2020
1
* Acknowledgements: Joonas Tuhkuri (MIT), collaborators at IBM and MIT, and authors of cited work
2020 NYU Conference on “AI in the Workplace: Future Directions in People Analytics”
https://wp.nyu.edu/aiatwork/

Organization of the Talk
• Emerging Landscape
• The Problem: The Quality of Everyday Decisions
• The Imperative: Corona Virus Pandemic
• AI for Business: A Technology to Augment Human Decision Making
• AI and Workforce: The Fear
• Recent Case Studies: AI and Productivity
• Machine Learning (ML): Insurance recommendation
• Conversation Agents ("chatbots"): Loans and Health
• Discussion
• AI and COVID19
• Concluding Comments
(C) Biplav Srivastava, IBM, May 2020 2

Emerging Landscape

The Quality of Everyday Decisions
Major variability due to:
• Emotions
• Biases
• Increasing data volume
• Cognitive ability to process
• Decreases under stress
and constraints
• Decreases with age*
* Source: A Review of Decision-Making Processes: Weighing the Risks and
Benefits of Aging, Mara Mather,
https://www.ncbi.nlm.nih.gov/books/NBK83778/
Source: https://www.umassd.edu/fycm/decision-making/process/
Emerging Landscape: The Problem

Evidence #1:
Poor Medical
Adherence
Finding relevant guidance is hard,
one reason for non-adherence and high
costs in health
Sources:
• Medication Nonadherence, A Diagnosable and Treatable Medical Condition,
Zachary A. Marcum, Mary Ann Sevick, Steven M. Handler,
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3976600/, 2013.
• https://www.nytimes.com/2017/04/17/well/the-cost-of-not-taking-your-
medicine.html
Taking medicines
• 20 -30 % of medication prescriptions are never filled
• ~50 % of medications for chronic disease are not
taken as prescribed
Impact
• causes 125,000 deaths, at least 10 percent of
hospitalizations
• Costs the American health care system between
$100 billion and $289 billion a year.
Example:
Hard to
understand
medicine’s
information

Evidence #2: Matching Demand to Supply of Jobs is Inadequate
Demand-Supply Gap in Jobs Market [1] and Yet, Low Work Satisfaction/ Engagement [2]
6
Job search at a portal
Motivation
1. Source: Global Skills Trends, Training Needs and Lifelong Learning Strategies for
the Future of Work, ILO & OECD Report 2018,
http://www.g20.utoronto.ca/2018/g20_global_skills_trends_and_lll_oecd-ilo.pdf
2. Source: For 2016, job satisfaction: US – 32%, Global – 13%,
https://www.gallup.com/workplace/236495/worldwide-employee-engagement-
crisis.aspx
3. https://www.ilo.org/global/about-the-ilo/newsroom/news/WCMS_743036/lang--
en/index.htm
4. https://www.bls.gov/news.release/empsit.nr0.htm
(C) Biplav Srivastava, IBM, May 2020Emerging Landscape: The Problem
■ Finding jobs was generally hard around the world (Dec
2019), except for in tight labor markets like US (3.5%
unemployment)
■ Workforce satisfaction/ engagement was generally
low around the world – people did not find jobs they
were match for [1,2]
■ COVID-19 impact [3]:
– Nearly half of global workforce at risk of losing
livelihoods in informal sector
– 9-12% job loss in the formal sector around the world
– 14.7% unemployment in US by end of April 2020 [4]

Decision Imperative: Corona Virus Pandemic
Decisions Need to be Made
• About disease
• Understand disease
• Tackle disease
• Understand impact to society: economy, supply
chain
• Advise on actions to take
• Individual
• Group
• Societal policy
Resource: https://github.com/biplav-s/covid19-info/wiki/Important-Information-About-COVID19
Emerging Scenario Around the
World*
• Millions of cases, hundreds of thousands of deaths
• Businesses disrupted, millions going out of
business
• Millions loosing jobs
* Numbers changing continuously; see reference for
details

Before and After: Decision Support
■ Today’s tools: Static, non-interactive, non-contextual, lack
explanations
■ Future tools: Dynamic to data, interactive, contextual,
explaining with data, anywhere, multi-modal, social (group
dependency), societally relevant, …
8
Future has potential to improve people’s lives, promote
well-being and reduce waste
(C) Biplav Srivastava, IBM, May 2020Emerging Landscape: The Problem

A Quick Summary of Artificial Intelligence
for Business
9(C) Biplav Srivastava, IBM, May 2020Emerging Landscape: AI for Business

Advanced AI Techniques (Analytics) like Reasoning & Machine Learning
make use of data and models to provide insight to guide decisions
Models
Analytics
Data
Insight
Data sources:
Business automation
Instrumentation
Sensors
Web 2.0
Expert knowledge
“real world physics”
Model:
a mathematical or
algorithmic
representation of
reality intended to
explain or predict some
aspect of it
Decision executed
automatically or
by people

Analytics Landscape
Degree of Complexity
CompetitiveAdvantage
Standard Reporting
Ad hoc reporting
Query/drill down
Alerts
Simulation
Forecasting
Predictive modeling
Optimization
What exactly is the problem?
What will happen next if ?
What if these trends continue?
What could happen…. ?
What actions are needed?
How many, how often, where?
What happened?
Stochastic Optimization
Based on: Competing on Analytics, Davenport and Harris, 2007
Descriptive
Prescriptive
Predictive
How can we achieve the best outcome?
How can we achieve the best outcome
including the effects of variability?

Example: Talks at NYU Conference
■ Are they useful? (Descriptive)
– Answering needs an assessment about the event
■ If it happens next time, how many will attend? (Predictive)
– Above + Answering needs an assessment about unknowns (e.g., future)
■ Should you attend? (Prescriptive)
– Above + Answering needs understanding the goals and current status of the
individual

Clarity: Data-Driven Competitive Analysis
Sheema Usmani, Mariana Bernagozzi, Yufeng Huang, Michelle Morales,
Amir Sabet Sarvestani, Biplav Srivastava,
Clarity: Data-driven Automatic Assessment of Product Competitiveness,
IAAI/AAAI 2020, Deployed Application Award
Market Intelligence using NLP
13Emerging Landscape: AI for Business (C) Biplav Srivastava, IBM, May 2020
AI for Business Example

14 © 2020 IBM Corporation
Competitive Analysis: Before & After
Today’s Manual Process New Process
Identify top competitor(s) for
product X, e.g. product Y
Read through thousands of
reviews on product X and Y
Keep track of topics/themes
of interest for each product
Decide whether a mention
represented positive/negative
feedback by manually
annotating each mention
Use the gathered data to
make a decision on whether or
not product X is more
competitive than product Y,
along the dimensions/themes
considered.
Repeat: for every data source,
theme, and timeframe
Input:
Product name and
drivers of
performance
Output:
Competitiveness
metric and
visualizations
Steps
1. Prepare review data of products p1 to pN from sources
d1 to dM (offline)
2. Process request for analysis for product pi (online)
3. Visualize analysis results (online, optional)
Emerging Landscape: AI for Business (C) Biplav Srivastava, IBM, May 2020

Illustrative Output
Clarity Score and Trends
Drivers and Raw Scores

Evaluation and Impact
■ The system has been running for over a year and used by
over 1500 people performing over 160 competitive
analyses involving over 800 products
■ In-lab evaluation
– Scores consistent with Gartner’s Magic Quadrant
■ Products v/s Vendor ranking
– Clarity scores consistent with
Net Promoter Score (NPS) of 50 products
■ In-field evaluation
– High user satisfaction
■ Net Promoter Score (NPS) of 52;
Scale -100 to 100

References for AI
17
Articles and Papers
• New York Time, AI Special Issues,
https://www.nytimes.com/spotlight/artificial-
intelligence, April 2020
• McKinsey, Notes from the AI Frontier modeling the
impact of AI on the world economy,
https://www.mckinsey.com/featured-
insights/artificial-intelligence/notes-from-the-ai-
frontier-modeling-the-impact-of-ai-on-the-world-
economy, 2018
• Biplav Srivastava, Understanding AI and Cognitive
Systems – a Perspective on Its Potential and
Challenges While Putting Them to Work with
People, AI & Cognitive Systems, Issue 4, Vol 2-
Issue 1, 2018.
Textbook
• AI – A Modern Approach (AIMA), S. Russell
& P. Norvig, http://aima.cs.berkeley.edu/
Tools and demos
• Code sample in AIMA book
• Learning tools and model libraries
• https://ai.google/tools/
• Watson library:
https://www.ibm.com/watson/products
-services/
• Exciting startups:
https://www.prowler.io/
• Interchange standards:
https://onnx.ai/
(C) Biplav Srivastava, IBM, May 2020Emerging Landscape: AI for Business

AI and Workforce
18Emerging Landscape: AI and Workforce (C) Biplav Srivastava, IBM, May 2020

Right AI for Workforce ?
■ The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand, Daron
Acemoglu, P Luz Ángela Restrepo, National Bureau of Economic Research, 2019
■ Summary:
https://idei.fr/sites/default/files/IDEI/documents/tnit/newsletter/newsletter_tnit_2
019.pdf
■ Full paper:
https://economics.mit.edu/files/18782

Traditional Driver for Automation
■ Increase labor productivity, i.e., value added per worker
■ Conventional Wisdom
– Tends to raise the demand for labor in the long run; hence, employment and wages.
– Technological progress might benefit workers with different skills unequally and productivity
improvements in one sector may lead to job loss in that sector.
– Other sectors will expand and contribute to employment and wage growth for all workers
■ Assumption
– Innovation pace is fast
– Workers can train for newer jobs fast
■ Caveat
– Productivity focused on near-term costs; e.g., does not consider long-term environment or
social cost of automation

The Reality for Automation
■ May not increase labor productivity
■ Automation replace cheaper capital (machines) in a range of tasks performed by human but is not
more productive than the labor they substitute (“so-so” technologies)
– “With so-so technologies, labor demand declines because of the displacement that
automation creates, but does not rebound due to the lack of powerful productivity gains”
■ Other trends
– Rate of innovation is slowing; government funding for research slowing
– Displaced workers need more time to be re-skilled
■ Consequence
– Reduce overall labor demand
– Put pressure on wages
■ Example: Industrial robots in automotive industry

Wrong and Right Automation
Doubtful (Wrong?)
■ Stagnating labor demand,
■ Declining labor share in national income,
■ Rising inequality
■ Lower productivity growth
■ Consider economic, environmental and
social outcomes
Examples: Automotive, Mining?
Right
■ Industries with perennial under-investment
■ Better economic or social outcomes.
Needs:
■ Personalized attention
■ Changing environment / knowledge
■ Demands scale
Examples: Education, Healthcare, Underwater
exploration

Incentives Promote Automation
■ Does the US Tax Code Favor Automation?, Daron Acemoglu, Andrea Manera,
Pascual Restrepo
– Optimal taxation of capital and labor would raise employment by 4.02% from
2010s tax rates
– Proposes automation tax to reduce the equilibrium level of automation

Evolution of Tasks within Occupations
■ Method
– Job posting data: Provides information
about the prevalence of tasks within each
occupation.
– US Bureau of Labor Statistics (BLS):
annual statistics of the average wages and
number of employees for 964 occupations
– Normalize the task data by the share of
workers employed in each occupation to
derive the unique task-shares dynamics
data for each task-occupation pair
– Evolution of the task-shares within each
occupation (from resume)
– Report trends in low-medium-high wage
ranges
■ Key results
– Share of “Artificial Intelligence” and “Big
Data” rises in high wage task clusters
– “SQL Databases and Programming,”
“Java,” and “JavaScript & jQuery” had
high share but it has been falling
24
Source:
Paper - Subhro Das, Sebastian Steffen, Wyatt Clarke, Prabhat Reddy, Erik Brynjolfsson, Martin Fleming. “Learning Occupational Task-
Shares Dynamics for the Future of Work”. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020.
Blog: https://www.ibm.com/blogs/research/2020/02/aaai-future-of-work/
Emerging Landscape: AI and Workforce (C) Biplav Srivastava, IBM, May 2020

Evolution of Tasks by Wages in Select Industries
■ Method
– Build Autoregressive integrated
moving average (ARIMA) model for
task cluster families using data of
72 months (2010-2015)
– Predict for 2016-2018.
■ Insights
– < 5% mean absolute percentage
error (MAPE)
– Such predictive models can help
understand re-skilling needs of
existing employees and market
demand for students
25
Source:
Paper - Subhro Das, Sebastian Steffen, Wyatt Clarke, Prabhat Reddy, Erik Brynjolfsson, Martin Fleming. “Learning Occupational Task-
Shares Dynamics for the Future of Work”. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020.
Blog: https://www.ibm.com/blogs/research/2020/02/aaai-future-of-work/
Terminology Illustration:
• Python is a task
• Scripting Languages task cluster,
• Information Technology task cluster family
(industry).

Workforce: Job Satisfaction Remains Low
• Job dis-satisfaction remains around 48% among
workers globally (2013) [1]
• Employee engagement (Gallup 2016) [2]
• US – 32%
• Global – 13%
• 54% employees satisfied in US (2019) [3]
• With growing population, more people are ready to
join the workforce
• Some regions are facing skill scarcity
• Other regions are facing job demand glut
• COVID-19 is leading to job losses [4,5]
• Growing jobs is a critical economic and
societal issue in many parts of the world
26
Source:
1. https://www.chartcourse.com/global-survey-reveals-staggering-
results-on-job-satisfaction/
2. https://www.gallup.com/workplace/236495/worldwide-
employee-engagement-crisis.aspx
3. https://www.conference-
board.org/press/pressdetail.cfm?pressid=9160
4. https://www.ilo.org/global/about-the-
ilo/newsroom/news/WCMS_743036/lang--en/index.htm
5. https://www.bls.gov/news.release/empsit.nr0.htm

AI and Workforce
• AI is leading to job impact [1-4]. Work that is:
– clerical, repetitive, precise, and perceptual
can increasingly be automated
– More creative, dynamic, and human
oriented tends to be less automatable
• In 2018, task hours by humans: machines was
71%:29%, across 12 industries. By 2022, this
will shift to 58%:42% [4]
• Employers plan to meet skilling gaps with
• Hiring of new skilled workers
• Seeking to automate tasks needing advanced
skills
• Re-skilling employees
• By 2022, no less than 54% of all employees
will require significant re- and upskilling [4]
27
References
1. Daron Acemoglu, Pascual Restrepo, Artificial
Intelligence, Automation and Work, 2018. At
https://www.nber.org/papers/w24196
2. What can machine learning do? Workforce
implications, Erik Brynjolfsson, Tom Mitchell,
Science 22 Dec 2017: Vol. 358, Issue 6370, pp.
1530-1534 DOI: 10.1126/science.aap8062
3. Inferring Work Task Automatability from AI Expert
Evidence, Paul Duckworth, Logan Graham,
Michael A. Osborn, AIES 2020;
http://logangraham.xyz/research/automation
4. The Future of Jobs Report 2018,
https://www.weforum.org/reports/the-future-of-
jobs-report-2018

Impact of AI on Workforce – A Detailed Look
Source: Webb, Michael, The Impact of Artificial Intelligence on the
Labor Market (November 6, 2019). Available at SSRN:
https://ssrn.com/abstract=3482150 or
http://dx.doi.org/10.2139/ssrn.3482150
Method
■ Use NLP - text of job task descriptions and the text of
patents - to construct a measure of the exposure of tasks
to automation
■ Took tasks (verbs) from (O*Net); impact (nouns) using
patents (Google Patents)
Insights
■ Occupations exposed by robots
– Most: materials movers in factories and warehouses, and tenders
of factory equipment
– Least: payroll clerks, artistic performers, and clergy
■ Occupations exposed by software
– Most : broadcast equipment operators, plant operators, parking lot
attendants, and packers and packagers
– Least : barbers, podiatrists, and postal service mail carriers
■ Occupations exposed by AI
– Most : clinical laboratory technicians, chemical engineers,
optometrists, and power plant operators.
– Least : food preparation, postal service mail carriers, teaching
Emerging Landscape: AI and Workforce

Source: Webb, Michael, The Impact of Artificial Intelligence on the Labor Market (November 6, 2019).
Available at SSRN: https://ssrn.com/abstract=3482150 or http://dx.doi.org/10.2139/ssrn.3482150
Impact of AI on Workforce:
A Detailed Look
Method
■ Uses regression parameters (e.g., negative
relationship between exposure and wages) of
Robots and Software impact to AI
Insights: AI will lead to
■ Older workers being more exposed than younger
workers
■ Higher educated and experienced workers will be
more exposed
■ Wage will drop for most (i.e., marginal drop in ratio
of 90th to the 10th percentile of wage)
Emerging Landscape: AI and Workforce

Recent Case Studies:
AI and Productivity
30(C) Biplav Srivastava, IBM, May 2020

Decision-Support for Recommending
Health Plans
■ Managing Intelligence: Skilled Experts and AI in Markets for Complex Products,
Jonathan Gruber, Benjamin R. Handel, Samuel H. Kina, Jonathan T. Kolstad, NBER
Working Paper No. 27038, April 2020, http://www.nber.org/papers/w27038
(C) Biplav Srivastava, IBM, May 2020 31Recent Case Studies: ML for Insurance

Medicare Insurance Recommendation
(C) Biplav Srivastava, IBM, May 2020 32Recent Case Studies: ML for Insurance
■ Medicare
– Part A program: covers inpatient hospital expenses
– Part B: covers outpatient expenses
– Part D: prescription drug expenses
■ Private Medicare Advantage plans
– Original medicare plus additional benefits and
may charge additional premiums
– Covered on a county-by-county basis, and
through managed care networks.
■ Study considered an Exchange with MA-PD
plans: 59,000 MA-PD enrollees, their agents,
and their enrollment options in both 2015
and 2017.
■ AI recommendation engine available to agents
in 2017
2015 2017
Enrollees 31,090 27,739
Agents 835 732
Avg Plans in
Choice Set
12.43 12.47

Recommendation Procedure in 2017
1. Estimate total medical spending for each individual k
2. Translate predicted spending for each individual k into Out-Of-Pocket (OOP) costs for
each plan j available to individual k,
3. Translates OOP for each plan j available to individual k into a utility that is then
converted to a 100 point scale
■ Output:
– a list of plans in Green-Yellow-Red tier (using 100-point scale) indicating how well plans
match customer preferences, based on expected utility calculations
– Total cost: premium and predicted OOP costs for each plan available
33Recent Case Studies: ML for Insurance (C) Biplav Srivastava, IBM, May 2020

AI+Human Agents: Main Results
■ Cost of plans reduce by $278 on average for 2017
(when actual choices in 2017 compared with
choices based on 2015 choice model parameter
estimates.)
■ Call times lowered by roughly 20%
■ In 2015
– more than half of consumers leave $1,000
on the table
– consumers weight premiums 6.5 times more
than expected plan OOP spending.
■ In 2017
– Consumers weight premiums and OOP
equally
34
2015 2017
Call time 59.4 min 47.8
min
Average actual money left on the
table
$1,261 $895
% people enrolled in the plan with
the lowest expected cost
9.8 18.0
% of people enrolled in a plan that
was within $500 of the lowest
expected cost
24.2 47.4
2015 2017
Enrollees 31,090 27,739
Agents 835 732
Avg Plans in
Choice Set
12.43 12.47
Recent Case Studies: ML for Insurance (C) Biplav Srivastava, IBM, May 2020

Decision-Support with Conversation
Agents (”Chatbots”)
(C) Biplav Srivastava, IBM, May 2020 35Recent Case Studies: Chatbots

Conversation Agents for Decision Support
• Systems that engage one or more people in
conversations
• Usually multi-modal (i.e., involving text,
speech, vision, document, maps)
• Personalized or generic: User(s) can come
and go in environment
• Are getting easy to build and deploy
• Handle uncertainties related to
• Natural language
• Human behavior
Demonstrations
• Eliza, http://www.manifestation.com/neurotoys/eliza.php3
• Mitsuku, https://www.pandorabots.com/mitsuku/
Recent Case Studies: Chatbots

Characteristics and Potential
• Chatbots
• Support a natural mode of interaction
• Create a visible presence for an organization providing AI technology to users
• Provide a sequential, slow mode of interaction (compared to the parallel, visual
mode)
• Areas where people want help
• Retrieve information
• Contextual, user-specific, data access
• Making data accessible to people with disability
• Decision making: Helping choose among complex alternatives
• Collaboration and mediation: among people making complex decisions
37(C) Biplav Srivastava, IBM, May 2020Recent Case Studies: Chatbots

Chatbots for Loan Renewal
■ Chinese financial services company; renewal of loans in last month
– Outbound call, voice chats, and less than 2 mins duration.
– Trained voice-based AI agent based on voice calls from the best human sellers
■ Method
– Calls assigned to either humans or chatbots
– Disclosure of the bots: (a) not telling the consumer at all, (b) telling them at the
beginning of the conversation or (c) after the conversation, or (d) telling them after
they'd purchased something.
■ Results
– Artificial intelligence can improve sales by four times compared to some human
employees
– When customers know the conversational partner is not a human, they purchase less
because they think the bot is less knowledgeable and less empathetic
38
Source:
• Xueming Luo, Siliang Tong, Zheng Fang, Zhe Qu, Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot
Disclosure on Customer Purchases, Marketing Science (2019). DOI: 10.1287/mksc.2019.1192
• News: https://phys.org/news/2019-09-artificial-intelligence-sales-human-employees.html
Recent Case Studies: Chatbots in Finance (C) Biplav Srivastava, IBM, May 2020

Conversation Agents and Healthcare
■ Recent survey (2018) 14 different conversational agents published in medical
literature
– Half of the conversational agents supported consumers with health tasks such as self-
care. Patient safety was rarely evaluated in the included studies.
– The only Randomized Control Trial (RCT) evaluating the efficacy of a conversational
agent found a significant effect in reducing depression symptoms (effect size d = 0.44,
p = .04). (2017)
– Chatbots often had problem with detecting intent and generated responses
39Recent Case Studies: Chatbots in Health
References
1. Conversational agents in healthcare: a systematic review , Liliana Laranjo, Adam G Dunn, Huong Ly Tong, Ahmet Baki Kocaballi, Jessica
Chen, Rabia Bashir, Didi Surian, Blanca Gallego, Farah Magrabi, Annie Y S Lau, Enrico Coiera, Journal of the American Medical Informatics
Association, Volume 25, Issue 9, September 2018, Pages 1248–1258, https://doi.org/10.1093/jamia/ocy072,
https://academic.oup.com/jamia/article/25/9/1248/5052181
2. Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational
Agent (Woebot): A Randomized Controlled Trial, JMIR Ment Health. 2017 Apr-Jun; 4(2): e19. Published online 2017 Jun 6.
doi: 10.2196/mental.7785
(C) Biplav Srivastava, IBM, May 2020

References – Conversation Agents
Articles
• Chatbots: In-depth Conversational Bots Guide [2019 update], https://blog.aimultiple.com/chatbot/
• Chatbots during Corona virus, https://www.technologyreview.com/2020/05/14/1001716/ai-chatbots-take-call-
center-jobs-during-coronavirus-pandemic/
Papers
• J. Harms, P. Kucherbaev, A. Bozzon and G. Houben, "Approaches for Dialog Management in Conversational
Agents," in IEEE Internet Computing, vol. 23, no. 2, pp. 13-22, 1 March-April 2019.
• Crook, P. 2018. Statistical machine learning for dialog management: its history and future promise. In AAAI
DEEP-DIAL 2018 Workshop, at https://www.dropbox.com/home/AAAI2018 -DEEPDIALWorkshop/Presentations-
Shareable?preview=Invited1-PaulCrook-AAAI DeepDialog Feb2018.pdf
• M. McTear, Z. Callejas, and D. Griol. Conversational interfaces: Past and present. In The Conversational Interface.
Springer, DOI: https://doi.org/10.1007/978-3-319-32967-3 4 , 2016.
• Young, S.; Gasic, M.; Thomson, B.; and Williams, J. D.2013. Pomdp-based statistical spoken dialog systems: A
review. Proceedings of the IEEE 101(5):1160–1179.
• Henderson, P.; Sinha, K.; Angelard-Gontier, N.; Ke, N. R.; Fried, G.; Lowe, R.; and Pineau, J. 2017. Ethical
challenges in data-driven dialogue systems. CoRR abs/1711.09050, AIES 2018
(C) Biplav Srivastava, IBM, May 2019 40Recent Case Studies: Chatbots

Discussion

AI-Based Decision-Support for COVID-19
• Understanding the disease
• Disease spread and simulation models
• Insights by visualization
• Tackling the disease
• Tracking people’s movement
• Fever detection via images
• Understanding mental depression from
social posts
• Fighting fake news
• Understanding impact
• Economic – job loss, industrial growth
• Supply Chain
• Risks
Resource: https://github.com/biplav-s/covid19-info/wiki/AI-and-COVID-19
• Individual actions
• Screening/ triage tools
• Group actions
• Models for when to open economy
• Contact tracing
• Matching producers and consumers: food, medical
supplies
• Policy actions
• Understanding impact of policy choices (e.g. lockdowns,
travel restrictions)
• Design of economic interventions
• AI Community’s Learning
• Data sources: Structured, Text – Research papers,
Image / Video
• Sharing and reuse of models and data is important
• Lots of hackathons
Discussion

Conclusion and Discussion
• Automation has been a driver for productivity, and AI continues the trend
• Wrong automation: So-so technologies focus on labor substitution
• Right automation: lead to productivity gain, demands focus on long-term social and
environmental impact too
• AI + workforce can be useful for:
• Improving quality of decision: Evidence is emerging from usage of machine learning as
well as chatbots; more needed
• Personalizing care at scale
• Handle challenging problems like climate, COVID19
• Careful experimentation needed to
• Understand AI impact on workforce and productivity gains
• Align incentives to balance economic, social and environmental impact
(C) Biplav Srivastava, IBM, May 2020 43Discussion

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