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The	Potential	and	Risks	of	Working	
With	Conversation	Agents
BIPLAV	SRIVASTAVA*,	IBM
22ND MARCH	2019
1
*	Acknowledgements:	 All	collaborators	 of	cited	work
2019	Information
Technology	 Professional	 Conference	 (ITPC)
Organization	of	the	Talk
•Conversation	Agents	
•Potential
• Ignored	and	Important	Real-World	Scenarios
• Complementary	with	Other	Technology	Trends:	Using	Open	Data
•Risks
• Ignoring	Value	to	Users	in	Pursuit	of	Short-term	Cost	Saving
• Poor	Design	or	Capbility
• Not	Addressing	Fairness	and	Trust
•Some	Innovation	Opportunities
2
Recent	Work	on	Conversations	Agents	for	Decision	Support
Systems	and	Papers
•"Water	Advisor,	a	chat	based	advisor	to	taking	decisions	related	to	water	
taking	into	account	water	pollution".	Video	on	Youtube -
https://www.youtube.com/watch?v=z4x44sxC3zA (Sep	2017).	
• Jason	Ellis,	Biplav	Srivastava,	Rachel	K.	E.	Bellamy,	Andy	Aaron,	Water	Advisor	– A	
Data-Driven,	Multi-Modal,	Contextual	Assistant	to	Help	with	Water	Usage	 Decisions,	
at	Proc.	32nd	AAAI	Conference	on	Artificial	Intelligence	(AAAI-18),	New	Orleans,	
Lousiana,	USA,	Feb	2-7,	2018.	[Demonstration,	Water].
•"A	Cognitive	Assistant	for	Visualizing	and	Analyzing	Exoplanets".	Awarded	
AAAI	2018	Best	Demonstration	Award.Video	- http://ibm.biz/tyson-demo
(Feb	2018).
• Jeffrey	O.	Kephart,	Victor	C.	Dibia,	Jason	Ellis,	Biplav	Srivastava,	Kartik Talamadupula,	
Mishal Dholakia,	A	Cognitive	Assistant	for	Visualizing	and	Analyzing	Exoplanets,	at	
Proc.	32nd	AAAI	Conference	on	Artificial	Intelligence	(AAAI-18),	 New	Orleans,	
Lousiana,	USA,	Feb	2-7,	2018.	[Demonstration,	Astronomy]
•Chatbot interface	to	a	Commercial	Product	in	Talent	Management,	
https://www.ibm.com/talent-management/career-coach [Commercial,	
Human	Resources]
3
Workshops
• DEEP-DIAL	2019,	The	Second	AAAI	
Workshop	on	Reasoning	and	Learning	
for	Human-Machine	Dialogues,	
https://sites.google.com/view/deep-
dial-2019/home
• DEEP-DIAL	2018,	The	First	AAAI	
Workshop	on	Reasoning	and	Learning	
for	Human-Machine	Dialogues,	
https://www.zensar.com/deep-dial18
More	Details:	https://researcher.watson.ibm.com/researcher/view_person_subpage.php?id=9879
Conversation	Agents	
4
Background
•Conversation	agents	and	interfaces	(chatbots)	are	getting	easy	to	build	and	deploy
• Can	be	text-based	or	speech-based
• Usually	 multi-modal	 (i.e,	involving	 text,	speech,	 vision,	 document,	 maps)
•Current	chatbots typically	interact	with	a	single	user	at	a	time	and	conduct
◦ Informal	conversation,	 or
◦ Task-oriented	 activities	like	answer	a	user’s	 questions	 or	provide	 recommendations
5
Demonstrations
• Eliza,	http://www.manifestation.com/neurotoys/eliza.php3
• Mitsuku,	 https://www.pandorabots.com/mitsuku/
Current	State
•Handle	uncertainties	related	to	
• Natural	language
• Human	behavior
•Dialog	Management
• Reasoning	 on	data’s	abstract	representations	
(Inouye 2004)
• Learning	policies	 over	predictable	 nature	of	data
(Young	 et	al.		2013)	
• Statistical	machine	learning	 for	dialog	management:	
its	history	 (Crook	 2018)
6
References:
• May	A.I.	Help	You?,	https://www.nytimes.com/interactive/2018/11/14/magazine/tech-design-ai-chatbot.html
• 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.
•Hype	around	potential
•User	feedback	is	mixed
• Novelty	 value	for	chit-chat	but	concerns	 about	
usability	 (e.g.,	Tay)
• Deployed	 for	customer	support	 commonly	 but	usage	
is	often	low	(compared	 to	other	channels),	 capability	
is	limited	 (usually	 single	turn),	and	not	considered	
the	preferred	channel	 of	choice	 for	most	users
Chatbots in	Dynamic	Environment
•Data	changes,	e.g.	sensor	data
•Groups	of	people,	who	come	and	go	in	
environment
•Multi-modal	interfaces,	i.e.,	modes	beyond	
conversation,	like	map,	graphics	and	documents
•Dialog	Management
• Combination	 of	learning	and	reasoning	
7
Current	Practice	of	Water	Advice
Advisories to public for Flint Residents, MI, USA Physical signage at a lake in
Washington, USA
8
Guide	every	day	people,	who	may	be	non-experts,	with	a	multi-modal	assistant	to	take	data-based	
decisions	specific	to	their	needs,	leveraging	complex	water	quality	data.
Audience
• General	Public	that	wants	to	understand	water	quality	at	a	specific	location	(e.g.,	swimming)
• Professionals	with	responsibility	for	regions	(e.g.,	public	health)
Before	and	After
Now:	Static,	non-interactive,	non-contextual,	lacks	data	details
Future:	Anywhere,	interactive,	explain	with	data,	contextual
Problem	and	Objective
9
Demo:	Water	Advisor
10
https://www.youtube.com/watch?v=z4x44sxC3zA
Jason	 Ellis,	Biplav	Srivastava,	 Rachel	K.	E.	Bellamy,	Andy	 Aaron,	Water	Advisor	 – A	Data-Driven,	 Multi-Modal,	
Contextual	 Assistant	 to	Help	with	Water	Usage	Decisions,	 at	Proc.	32nd	AAAI	Conference	 on	Artificial	
Intelligence	(AAAI-18),	 New	Orleans,	 Lousiana,	 USA,	Feb	2-7,	 2018.	[Demonstration,	Water].
Water	Advisor	Interface
Illustrative	Example:
Water	Advisor
Modes	 (R	to	L):
1. Conversation
2. Map
3. Documents
11
Architecture
Find	query	relevant	data	(one	
or	more	sources)Water	Pollution	 Data
(Quantitative)
Regulations:	
Region	x
Purpose
Qualitative	 Result:
Yes,	No,	Uncertain
+
Explanation
Query:	Is	it	fine	to	<SWIM>	 at	<OSSINING>	 <today>	?
[PURPOSE]	 	[LOCATION]	 	[TIME]
Represent	regulations	based	
on	purpose	and	region
Reason	about	query	
relevant	regulation
Determine	quantitative	
decision	plane
(multi-parameters	for	purpose,	
handle	missing	data)
Map	to	qualitative	
decision
Create	Explanation
12
Data	Needed
• Mapping	of	activities	(e.g.,	bathing,	drinking,	fishing)	to	water	
quality	parameters
• Created	and	made	open-source	at:	
https://github.com/biplav-s/water-info
• Novelty:	None	existed	before
• Regulations	applicable	to	geography	and	activity,	plus	water	quality	
parameters
• Demo:	US	EPA,	Michigan,	New	York,	India	
• Challenge:	under-specified,	conflicting
• Actual	water	quality	data
• Demo:	US	Geological	Survey	(USGS)
Data,	Analysis	and	(AI)	Challenges
13
Sample	Standards:	Just	in	Drinking	Water
Source: https://en.wikipedia.org/wiki/Drinking_water_quality_standards
11
Water	Data	from	USGS Mapping	Made	Available	on	Github
15
AI	Technical	Issues
Dimensions General Water	Specific
Learning Off-the-shelf	 trained	intents Water	quality	trends
Representation Representation	 of	raw data Activity purpose	 and	related	parameters,	
water safety	limits
Reasoning Rule-based	 handling	 of	missing	
values
Location	and activity	based	regulation	
selection,	 interpreting	safe	limits	for	a	
parameter
Execution Controlling	 interaction	modules,	
asking	questioning	 and	parsing	
responses
Generating	error	rates,
system	confidence	 and	usability	 rules
Human	Usability	
Factors
Using	error	rates	of	conversation	
modules	 to	control	questioning
strategy
Using	missing	 data	to	control water	
advice
in	generated	natural	output.
Ethical	Issues Biases,	 adversarial	examples,	
privacy	violations,	 safety	challenges	
and	reproducibility	 concerns
Preference	encoded	 in	rules	based	 on	
activities:	recreation	over	drinking
16
References	– Conversation	Agents
Articles
•Chatbots:	In-depth	Conversational	 Bots	Guide	[2019	update],	https://blog.aimultiple.com/chatbot/
Papers
•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.
17
Potential of	Conversation	Agents	in	
Helping	People
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
19
Everyday	Scenarios	- People
•Travel:	“Which	train	can	I	take	to	office?”
• Needs	information	 about	locations,	 train	schedules	 and	status,	personal	 schedule
• Category:	information	 seeking
•Health:	“Who	can	I	see	now	for	my	pain	in	the	stomach?”
• Needs	information	 about	location,	 likely	medical	situation,	 medical	specialties,	 doctors	and	health	care	
providers	 in	the	vicinity,	 insurance	 and	payment	 situation,	 availability	of	services
• Category:	information	 seeking,	 choosing	 among	alternatives
•Social:	“How	do	I	meet	my	visiting	friend	with	family	at	an	evening?”
• Needs	information	 about	schedule	 of	friend’s	 family	and	mine,	location	 of	home	 and	friend’s	 stay,	
capacity	of	home	 and	restaurants	in	the	area
• Category:	information	 seeking,	 choosing	 among	alternatives,	 collaboration
20
Everyday	Scenarios	- Business
•Guidance	
• During	data	science
• Rogers	Jeffrey	Leo	John,	Navneet Potti,	Jignesh M.	Patel,			Ava:	From	Data	to	Insights	Through	Conversations.	CIDR	2017
• Skilling	 and	professional	 development
•Collaboration	and	Mediation	Decisions
• Hiring	a	candidate
• Scheduling	 an	activity,	 e.g.,	medical	 operation
• Merger	and	Acquisitions
21
Demo:	
Environment	for	Astronomy	Exploration
22
http://ibm.biz/tyson-demo
• Jeffrey	 O.	Kephart,	 Victor	C.	Dibia,	Jason	 Ellis,	Biplav	Srivastava,	Kartik Talamadupula,	 Mishal Dholakia,	 An	
Embodied	 Cognitive	 Assistant	for	Visualizing	 and	Analyzing	 Exoplanet	Data,	IEEE	Internet	Computing	 Journal	
(Special	 Issue	on	Cognitive	 Services	 and	Intelligent	Chatbots),	 March	2019.		[AI-Chatbots,	 Astronomy]	
• Jeffrey	O.	Kephart,	Victor	C.	Dibia,	 Jason	Ellis,	 Biplav	Srivastava,	Kartik Talamadupula,	 Mishal Dholakia,	 A	
Cognitive	 Assistant	 for	Visualizing	 and	Analyzing	 Exoplanets,	 at	Proc.	32nd	 AAAI	Conference	 on	Artificial	
Intelligence	(AAAI-18),	 New	Orleans,	 Lousiana,	 USA,	Feb	2-7,	 2018.	[Demonstration,	Astronomy]
Appropriateness	of	Chatbots
For	- conditions	when	a	chatbot is	
preferable:
ü When	a	person	wants	to	explore	an	unknown	
area	and	needs	to	be	guided.
ü When	log	of	interaction	between	user	and	
agent	needs	to	be	retained	for	legal	or	
compliance	reasons.	A	chat	history	is	easier	to	
re-trace	than	other	interaction	alternatives	
due	to	its	sequential	nature.	
ü When	the	person	is	interacting	with	a	human-
like	physical	system,	like	robot,	and	expects	it	
to	be	able	to	interact	via	conversation.
ü When	the	content	underlying	the	conversation	
changes	frequently	and	a	user	will	expect	
guidance	or	explanation	about	the	results.
Against	- conditions	when	a	chatbot
is	not	preferable
• When	the	user	knows	about	the	domain	
and	a	sequential	interaction	style	will	be	
frustrating.
• When	the	content	underlying	the	
conversation	is	relatively	unchanged.	A	
user	thus	would	grow	familiarity	with	
content	over	time	and	will	prefer	
expedited	interaction.
• When	the	system	is	not	capable	to	
understand	a	person’s	conversation	
style/	accent.	In	such	a	case,	typing	or	
entering	information	will	be	more	
accurate	and	less	frustrating	to	the	user.
23
Conversation	Agents	with	Open	Data
550+Data	Catalogs	of	Public	Data
As	on	19	March	2019
Chatbot for	Kansas	City	Open	Data:	Example
https://chatfuel.com/bot/OpenDataKC
26
Kansas	City	
Open	Data:		
Search	
Interface	
Example
27
Chatbots for	Better	Interaction	With	Data
• Description:	 https://sites.google.com/view/deep-dial-2019/competitive-demo-call
• Scenario:	 A	chatbot that	helps	 a	user	find	information	 about	subway	 stations	in	New	York	City.	
Information	 about	stations,	 their	facilities	and	train	routes	serving	them	are	available	 publicly.	
• New	York	City:
• Data:	https://data.ny.gov/Transportation/NYC-Transit-Subway-Entrance-And-Exit-Data/i9wp-a4ja
• App:	https://subbot.eu-gb.mybluemix.net/
• Vidoe:	https://vimeo.com/325321516
28
Reference:
Biplav	Srivastava,	Decision-support	 for	the	Masses	 by	Enabling	 Conversations	 with	Open	Data,	
At	https://arxiv.org/abs/1809.06723,	 Sep	2018.
Decision	Support	with	Trains	Data	and	
Machine	Learning-Based	Insights
India	runs	the:	
•fourth	largest	railway	transport	network	size	in	the	world	carrying	over	8	billion	passengers	per	
year	(2016).
•routes	length	of	66,687		kilometers	with	11,	122		locomotives,	7,	216		stations	in	2016
However,	the	travel	experience	of	passengers	is	frequently	marked	by	delays,	i.e.,	late	arrival	of	
trains	at	stations,	causing	inconvenience.
29
Illustration:	Delay	of	Two	Trains,	One	Premium	and	Another	
Typical,	at	Destinations	Over	a	Year	Shows	Delays	Are	Endmic
Train	12305,	Kolkata	Rajdhani,	is	a	premium	train
which	runs	for	nearly	18	hours	to	cover	1529	km	(in	5	states
that	speak	 Hindi,	Bengali	and	English),	stops	at	10	stations
and	was	late	over	1	hour	at	destination	for	19% of	the	days
between	1-Oct-2017	and	1-Sep-2018.
Train	13050,	Amritsar	Howrah	Express,	runs	for	nearly	46	hours	
over	3	days	to	cover	1922	km	(in	7	states	that	speak	Hindi,	Bengali,	
Punjabi		and	English),	stops	at	112	stations	and	was	late	over	1	
hour	at	destination	for	63%	of	the	days	during	the	same	period.
30
Decision	Support	with	Trains	Data	and	
Machine	Learning-Based	Insights
•Video	of	train-delay	assistant:	https://www.youtube.com/watch?v=I-wtcAYLYr4
31
Reference:
• Ramashish Gaurav,	Biplav	Srivastava,	Estimating	Train	Delays	in	a	Large	Rail	Network	Using	a	Zero	
Shot	Markov	Model,	 IEEE	International	 Conference	 on	Intelligent	Transportation	 Systems	 (ITSC).	On	
Arxiv at:	https://arxiv.org/abs/1806.02825,	 April	2018 [Area:	Train	delay,	learning]
• Himadri Mishra,	 Ramashish Gaurav,	Biplav	Srivastava,	Train	Status	Assistant	 for	Indian	Railways,	On	
Arxiv at:	https://arxiv.org/abs/1809.08509,	 Sep	2018	[Chatbot,	 Train	delay	assistant]
Real	World	Applications
32
•See	IJCAI	2015	tutorial	on	AI	for	Smart	City	Innovations	 with	Open	Data
•Site:	https://sites.google.com/site/aismartcitytutorial/
•Description:	 https://www.linkedin.com/pulse/tutorial-ai-smart-city-innovations-open-data-
biplav-srivastava
•Slides:	 	http://www.slideshare.net/biplavsrivastava/ai-for-smart-city-innovations-with-open-
data-tutorial-50928083
Risks	with	Conversation	Agents
In	Appropriateness	of	Chatbots
For	- conditions	when	a	chatbot is	
preferable:
ü When	a	person	wants	to	explore	an	unknown	
area	and	needs	to	be	guided.
ü When	log	of	interaction	between	user	and	
agent	needs	to	be	retained	for	legal	or	
compliance	reasons.	A	chat	history	is	easier	to	
re-trace	than	other	interaction	alternatives	
due	to	its	sequential	nature.	
ü When	the	person	is	interacting	with	a	human-
like	physical	system,	like	robot,	and	expects	it	
to	be	able	to	interact	via	conversation.
ü When	the	content	underlying	the	conversation	
changes	frequently	and	a	user	will	expect	
guidance	or	explanation	about	the	results.
Against	- conditions	when	a	chatbot
is	not	preferable
• When	the	user	knows	about	the	domain	
and	a	sequential	interaction	style	will	be	
frustrating.
• When	the	content	underlying	the	
conversation	is	relatively	unchanged.	A	
user	thus	would	grow	familiarity	with	
content	over	time	and	will	prefer	
expedited	interaction.
• When	the	system	is	not	capable	to	
understand	a	person’s	conversation	
style/	accent.	In	such	a	case,	typing	or	
entering	information	will	be	more	
accurate	and	less	frustrating	to	the	user.
34
Popular	Press	on	Risks	with	Chatbots
•Jamie	Susskind,	Chatbots Are	a	Danger	to	Democracy,	
https://www.nytimes.com/2018/12/04/opinion/chatbots-ai-democracy-free-speech.html
• We	need	to	identify,	 disqualify	 and	regulate	chatbots before	they	destroy	 political	speech.
•CADE	METZ	and	KEITH	COLLINS,	To	Give	A.I.	the	Gift	of	Gab,	Silicon	Valley	Needs	to	Offend	You,	
https://www.nytimes.com/interactive/2018/02/21/technology/conversational-bots.html
35
Source:	Columbia	course	on	Responsible	Data	Science	by	Prof.	Julia	Stoyanovich
Data,	Bias	and	the	World	
We	Live	In
Middle	Language Google Yandex
tu *
Gender	distinction lost	or	
switched.
{..,"translated":	"O	hemşire.	O	bir Optisyendir.",
"oto":	"That	nurse.	Itu0026#39;s	an	Optic.",”
values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.0,	0.0,	1.0]}
{..,	"translated":	"O	bir Hemşire.	Bir Gözlükçü.",
"oto":	"Sheu0027s	a	nurse.	An	Optician.",
“values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.0,	0.5,	0.5]}
ru {..,	"translated":	"Он медсестра.	 Она Оптик.",
"oto":	"Heu0026#39;s	 a	nurse.	Sheu0026#39;s	an	Optician.",
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
{..,	"translated":	"Он является медсестра.	Она является Оптиком.",
"oto":	"He	is	a	nurse.	She	is	an	Optician.”,
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
it {..,	"translated":	"Lui è un	infermiere.	Lei	è un	ottico.",
"oto":	"He	is	a	nurse.	She	is	an	optician.”,
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
{..,	"translated":	"Lui è un	Infermiere.	Lei	è un	Ottico.",
"oto":	"He	is	a	Nurse.	She	is	an	Optician.",
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
es {..,"translated":	"El	es un	enfermero.	Ella	es una Óptica.",
"oto":	"He	is	a	nurse.	She	is	an	Optician.",	
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
{..,"translated":	"Él es una Enfermera.	Ella	es un	Oftalmólogo.",
"oto":	"He	is	a	Nurse.	She	is	an	Ophthalmologist.",
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
hi *
Gender	distinction replaced	by	
both	translators
{..,"translated":	"वह नस /ह ै। वह एक ऑि5ट7शयन ह ै",
"oto":	"sheu0026#39;s	a	nurse.	He	is	an	optician",
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
{..,"translated":	"वह एक नस /ह ै.	वह एक :काश<व=ानशा>ी.",
"oto":	"She	is	a	nurse.	He	is	a	optician.",
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
pt {..,	"translated":	"Ele é um	enfermeiro.	Ela	é uma óptica.",	
"oto":	"He	is	a	nurse.	Sheu0026#39;s	an	optician.",
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
{..,	"translated":	"Ele é uma Enfermeira.	Ela	é um	Oculista.",
"oto":	"He	is	a	Nurse.	She	is	an	Optician.",
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
fr {..,"translated":	"Il	est une infirmière.	Elle	est opticienne.",
"oto":	"He	is	a	nurse.	She	is	an	optician.",
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
{..,"translated":	"Il	est une Infirmière.	Elle	est un	Opticien.",
"oto":	"He	is	a	Nurse.	She	is	an	Optician.",
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.5,	0.5,	0.0]}
ar *
Gender	distinction lost	in
Translation	by	both
{..,"translated":	" ‫ھو‬‫ﻧﺎرس‬ .	 ‫وھﻲ‬‫ﺑﺻرﯾﺎت‬ .",
"oto":	"It	is	Nars.	They	are	optics.",
"values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.0,	0.0,	1.0]}
{..,	"translated":	" ‫ھو‬‫ﻣﻣرﺿﺔ‬ .	 ‫ھﻲ‬‫اﻟﻌﯾون‬ .",
"oto":	"Is	a	nurse.	Are	the	eyes.",
values":	["He",	"She",	"OTHER"],
"otoDistrib":	[0.0,	0.0,	1.0]}
"original": "He is a Nurse. She is a Optician. ” (“originalDistrib":	 [0.5,		0.5,	0.0])
Examples	of	Computational	/	AI	Services	and	Bias
Language	translator
◦ Some	possible	 biases:	 Sexual,	religious,	 racial
◦ Impact:	failure	to	recognize	gender	may	lead	to	selection	 of	wrong/indecent	 phrase	in	target	language	
which	can	cause	uproar
Medical	condition	detector
◦ Some	possible	 biases:	 Sexual,	racial
◦ Impact	:	failure	to	recognize	entities	 may	lead	to	mis-diagnosis
Image	caption	generator
◦ Some	possible	 biases:	 Sexual,	religious,	 racial
◦ Impact	:	failure	to	recognize	entities	 in	image	may	lead	to	selection	 of	wrong	phrases	 and	generation	 of	
wrong/indecent	 caption	 which	can	cause	uproar
Biplav Srivastava, Francesca Rossi, Towards Composable Bias Rating of AI Systems, 2018AI Ethics and Society
Conference (AIES 2018), New Orleans, Louisiana, USA, Feb 2-3, 2018. [AI Service Rating, Ethics]
Illustration:	A	Seemingly	
Innocuous	Chatbot
Potential	Issues
• Leak	information
• Abusive	 language	
• Complex	 response
Issues	to	Handle	to	Promote	Trust
Stakeholder	and	what	they	care	about
◦ Users:	Leaking	information,	abusive	language,	bias
◦ Designers:	abusive	language,	bias,	complex	response
◦ Data	providers:	bias
Types	of	issues
◦ Leaking	information:	agent	may	reveal	information	about	one	user	(A)	to	other	user	(B)	
without	user’s	(A’s)	permission
◦ Abusive	language:	agent	may	use	improper	language	in	the	context	of	conversation
◦ Bias:	agent	may	exhibit	behavior	considered	biased	with	respect	to	some	protected	variable
◦ Complex	response:	 agent	may	interact	in	a	style considered	incompatible	with	user	
◦ …
References	– Trust	/	Conversation	Agents
•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
• Srivastava,	B.,	and	Rossi,	F.	2018.	Towards	composable bias	rating	of	ai systems.	In	Proceedings	
of	the	2018	AAAI/ACM	Conference	on		AI	Ethics	and	Society	Conference,	(AIES	2018),	New	
Orleans,	Louisiana,	USA
• Galhotra,	S.;	Brun,	Y.;	and	Meliou,	A.	2017.	Fairness	testing:	Testing	software	for	discrimination.	
In	Proceedings	of	the	2017	11th	Joint	Meeting	on	Foundations	 of	Software	Engineering,	
ESEC/FSE	2017,	498–510.	New	York,	NY,	USA:	ACM.
41
Some	Innovation	Opportunities
•I1:	Interaction	strategies	to	efficiently	answer	user’s	requests
•I2:	Designing	useful	chatbots at	scale
• Along	good	design	 principles
• Automatically	 from	data
42
© 2019 International Business Machines Corporation
I1:	Interaction	Strategies
Close-ended	question–eliciting	one	
feature	at	a	time
“what	industry	 do	you	want	to	work	in?”
“any	location	 preferences?”
Open-ended	questions–possibly	
eliciting	multiple	features
“tell	me	what	you	care	about	work?”
“what	factors	make	an	ideal	job	for	
you?”
Objective:	
§ Reduce	dialog	length	
§ Have	a	more	
engaging	
conversation
Yunfeng Zhang,Vera Liao,Biplav Srivastava,Towardsan optimal
dialog strategy forinformationretrieval using bothopen-ended and
close-ended questions,Proc.Intelligent UserInterfaces(IUI 2018,
Tokyo,Japan,March 2018 [Dialog Strategy].
© 2019 International Business Machines Corporation
Approach:	Dynamic	Interaction	Strategies
Yunfeng Zhang,Vera Liao,Biplav Srivastava,Towardsan
optimal dialogstrategy forinformationretrieval using both
open-endedand close-endedquestions,Proc.Intelligent User
Interfaces(IUI 2018,Tokyo,Japan,March 2018 [Dialog
Strategy].
•An	framework	 for	an	adaptive	questioning	strategy	selector,	 with	the	
goal	of	minimizing	dialog	length
• Alternate	between	asking	open- and	close-endedquestions	
• By	maximizing	expected	information	gain	(IG)	at	each	step
•A	simulation	experiment	 considering:
• Different	user	behaviors	
• Different	accuracies	of	the	NLP	system
• Different	traits	of	item-feature	datasets
© 2019 International Business Machines Corporation
Calculating	Expected	IG:	Close-ended	Questions
F1 F2 F3 …
Job	1 0 1 1 …
Job	2 0 1 0 …
Job	3 0 1 0 …
…
Chatbot:	Are	you	looking	for	a	job	with	Feature	3?
User:	Yes!
F1 F2 F3 …
Job	1 0 1 1 …
Job	2 0 1 0 …
Job	3 0 1 0 …
…
Change	of	information	entropy	after	knowing	the	feature	value	
• n:	number	of	candidate	items	in	the	current	state
• P(Ii):	a	priori	probability	that	the	ith item	is	the	queried	item
• P(Ii|F=v):	conditional	probability	of	Ii after	knowing	F’s	value	is v
Used	in	previous	work	(e.g.,	Komatani,	et	al.)
© 2019 International Business Machines Corporation
Calculating	Expected	IG:	Open-ended	
questionsF1 F2 F3 …
Job	1 0 1 1 …
Job	2 0 0 1 …
Job	3 1 1 0 …
…
Chatbot:	Tell	me	about	your	ideal	job
User:	It	should	have	Feature	2	and	Feature	3,	but	not	
Feature	1
F1 F2 F3 …
Job	1 0 1 1 …
Job	2 0 0 1 …
Job	3 1 1 0 …
…
Assumptions:
• Large	number	of	items	(n),	large	number	of	features	(m)
• Describe	features	in	natural	language,	extracted	by	an	NLP	
system---binary	reporting
Factors	to	consider:
• 𝜆NF:	expected	number	of	features	to	be	reported	
• Rnlp:	recall	rate	of	the	NLP	system	for	feature	extraction
• Lj:	likelihood	of	reporting	feature	j
• 𝜌:	correlation	between	features	(to	avoid	over-estimation)
© 2019 International Business Machines Corporation
Take-aways:
• Ask	more	open-ended	 questions	with	more	cooperative	users
• Stick	with	close-ended	questioning	if	NLP	extraction	is	under-performing
• With	more	dispersed	IG	across	features,	need	to	ask	more	close-ended	questions	and	have	
longer	dialogs
• When	feature	correlation	is	high,	IGopen is	over-estimated.	System	can	make	sup-optimal	
decisions	and	ask	more	questions	than	the	close-ended-only	 baseline
I2:	Designing	Useful	Chatbots at	Scale
•Useful	interfaces
• Scheduling	 meetings	 between	a	group	of	people	 using	 Emails	[1]
• Personalized	 conversation	 agents	[2]
•Automatically	building	chatbots to	interact	with	a	data	source	[3]
48
References:
1. Justin	Cranshaw,	Emad	Elwany,	Todd	Newman,	Rafal Kocielnik,	Bowen	Yu,	Sandeep	Soni,	Jaime	Teevan,	
Andrés	Monroy-Hernández,	Calendar.help:	Designing	a	Workflow-Based	Scheduling	Agent	with	Humans	
in	the	Loop,	https://arxiv.org/abs/1703.08428,	March	2017.
2. Daniel,	F.;	Matera,	M.;	Zaccaria,	V.;	and	Dell’Orto,	A,	Toward	truly	personal	chatbots:	On	the	
development	of	custom	conversational	assistants.	In	Proceedings	of	the	1st	International	Workshop	on	
Software	Engineering	for	Cognitive	Services	,	SE4COG	’18,	31–36.	New	York,	NY,	 USA,	2018
3. ACM.Biplav Srivastava,	Decision-support	for	the	Masses	by	Enabling	Conversations	with	Open	Data,	At	
https://arxiv.org/abs/1809.06723,	Sep	2018.
Conclusion	and	Discussion
•Conversation	agent	(chatbots)	provide	an	unprecedented	opportunity	 to	deliver	value	to	people
• First	wave	of	applications	 have	shown	 promise	 in	simple	 tasks
• Users	do	not	use	chatbots as	first	preference
•The	field	serves	as	a	promising	platform	to	integrate	AI	sub-disciplines	for	goal-directed	
autonomy	(”software	robots”)
• Uses	techniques	 from	learning,	 reasoning,	 representation	 and	execution
• Also	needs	 to	work	with	human	 usability	 factors	and ethical	issues
•Lots	of	opportunities	for	innovation
49
Referenced	Demonstrations	
•Eliza	- http://www.manifestation.com/neurotoys/eliza.php3
•Mitsuku,	https://www.pandorabots.com/mitsuku/
•Water	Advisor	- https://www.youtube.com/watch?v=z4x44sxC3zA
•Astronomy	Advisor	- http://ibm.biz/tyson-demo
•Train	Status	Assistant
• India:	https://www.youtube.com/watch?v=a-ABv29H6XU
• New	York	City:	https://subbot.eu-gb.mybluemix.net/ https://vimeo.com/325321516
50

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