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Ensuring Human Control & Responsibility
While Increasing Automation: Design Principles for Supporting Insight & Reducing Errors
Ben Shneiderman [email protected] @benbendc
Founding Director (1983-2000), Human-Computer Interaction Lab
Professor, Department of Computer Science
Member, Institute for Advanced Computer Studies
University of Maryland
College Park, MD 20742
Enabling Teams of Humans
to Harness Networks of Machines
Ben Shneiderman [email protected] @benbendc
Founding Director (1983-2000), Human-Computer Interaction Lab
Professor, Department of Computer Science
Member, Institute for Advanced Computer Studies
University of Maryland
College Park, MD 20742
Interdisciplinary research community
- Computer Science & Info Studies
- Psych, Socio, Poli Sci & MITH
(www.cs.umd.edu/hcil)
Design Issues
• Input devices & strategies
• Keyboards, pointing devices, voice
• Direct manipulation
• Menus, forms, commands
• Output devices & formats
• Screens, windows, color, sound
• Text, tables, graphics
• Instructions, messages, help
• Collaboration & Social Media
• Help, tutorials, training
• Search
www.awl.com/DTUI
Fifth Edition: 2010
• Visualization
HCI Pride: Serving 5B Users
Mobile, desktop, web, cloud
Diverse users: novice/expert, young/old, literate/illiterate,
abled/disabled, cultural, ethnic & linguistic diversity, gender,
personality, skills, motivation, ...
Diverse applications: E-commerce, law, health/wellness,
education, creative arts, community relationships, politics,
IT4ID, policy negotiation, mediation, peace studies, ...
Diverse interfaces: Ubiquitous, pervasive, embedded, tangible,
invisible, multimodal, immersive/augmented/virtual, ambient,
social, affective, empathic, persuasive, ...
Integrating Humans, Machines & Networks
1) key research goals as they relate to the abilities of humans,
machines and networks to share the cognitive load to make
decisions
2) relevant milestones that have been reached in subfields
3) relevant impediments to achieving technological breakthroughs
4) systems-integration challenges to improving data-to-decision
capabilities
5) the scope & character of international approaches
6) policy implications of international research for the U.S.
Enabling Teams of Humans
to Harness Networks of Machines
My position:
Teams & Humans have initiatives, goals, and responsibility
Machines & Networks are remarkable tools,
operated, maintained & designed by teams & humans
Enabling Teams of Humans
to Harness Networks of Machines
My position:
Teams & Humans have initiatives, goals, and responsibility
Machines & Networks are remarkable tools,
operated, maintained & designed by teams & humans
More effective systems recognize
the differences between humans & machines
Clarifying responsibility supports continuous improvement
Visual presentations provide high bandwidth,
while simple clicks/gestures enable rapid operation
Enabling Teams of Humans
to Harness Networks of Machines
Research agenda:
- better design for individuals
- easier collaboration for groups
- effective social mechanisms for
teams/organizations/communities
Visual design promotes:
- sense-making
- situation awareness
- accurate decision-making in stressful distracting situations
Metrics expand:
- peta-flops, tera-bytes & giga-hertz
- peta-contribs, tera-thank-yous & giga-collabs
Information Visualization
• Visual bandwidth is enormous
• Human perceptual skills are remarkable
• Trend, cluster, gap, outlier...
• Color, size, shape, proximity...
• Goals
• Detect errors & understand source data
• Develop & test hypotheses
• Make insights & support decisions
• Collaborate & persuade
• Challenges
• Meaningful visual displays of massive data
• Interaction: widgets & window coordination
• Process models for discovery
Information Visualization & Visual Analytics
• Visual bands
• Human percle
• Trend, clus..
• Color, size,..
• Three challe
• Meaningful vi
• Interaction: w
• Process mo
1999
Information Visualization & Visual Analytics
• Visual bandwidth is enormous
• Human perceptual skills are remarkable
• Trend, cluster, gap, outlier...
• Color, size, shape, proximity...
• Three challenges
• Meaningful visual displays of massive da
• Interaction: widgets & window coordinati
• Process models for discovery
1999 2004
Information Visualization & Visual Analytics
• Visual bandwidth is enormous
• Human perceptual skills are remarkable
• Trend, cluster, gap, outlier...
• Color, size, shape, proximity...
• Three challenges
• Meaningful visual displays of massive data
• Interaction: widgets & window coordination
• Process models for discovery
1999 2004 2010
Business takes action
• General Dynamics buys MayaViz
• Agilent buys GeneSpring
• Google buys Gapminder
• Oracle buys Hyperion
• Microsoft buys Proclarity
• InfoBuilders buys Advizor Solutions
• SAP buys (Business Objects buys
Xcelsius & Inxight & Crystal Reports )
• IBM buys (Cognos buys Celequest) & ILOG
• TIBCO buys Spotfire
Spotfire: Retinol’s role in embryos & vision
Spotfire: DC natality data
http://registration.spotfire.com/eval/default_edu.asp
10M - 100M pixels: Large displays
100M-pixels & more
1M-pixels & less Small mobile devices
Information Visualization: Mantra
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
• Overview, zoom & filter, details-on-demand
Information Visualization: Data Types
• 1-D Linear Document Lens, SeeSoft, Info Mural
• 2-D Map GIS, ArcView, PageMaker, Medical imagery
• 3-D World CAD, Medical, Molecules, Architecture
• Multi-Var Spotfire, Tableau, Qliktech, Visual Insight
• Temporal LifeLines, TimeSearcher, Palantir, DataMontage
• Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap
• Network Pajek, UCINet, NodeXL, Gephi, Tom Sawyer
I
nfo
Viz
S
ciV
iz .
infosthetics.com visualcomplexity.com eagereyes.org
flowingdata.com perceptualedge.com datakind.org
visual.ly Visualizing.org infovis.org
Obama Unveils “Big Data” Initiative (3/2012)
Big Data challenges:
• Developing scalable algorithms
for processing imperfect data in
distributed data stores
• Creating effective human-
computer interaction tools for
facilitating rapidly customizable
visual reasoning for diverse
missions.
http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_press_release_final_2.pdf ̀
Temporal Data: TimeSearcher 1.3
• Time series
• Stocks
• Weather
• Genes
• User-specified
patterns
• Rapid search
Temporal Data: TimeSearcher 2.0
• Long Time series (>10,000 time points)
• Multiple variables
• Controlled precision in match
(Linear, offset, noise, amplitude)
LifeLines: Patient Histories
www.cs.umd.edu/hcil/lifelines
LifeLines2: Contrast+Creatine
LifeLines2: Align-Rank-Filter & Summarize
LifeFlow: Aggregation Strategy
Temporal
Categorical Data
(4 records)
LifeLines2 format
Tree of Event
Sequences
LifeFlow Aggregation
www.cs.umd.edu/hcil/lifeflow
LifeFlow: Interface with User Controls
Treemap: Gene Ontology
www.cs.umd.edu/hcil/treemap/
+ Space filling
+ Space limited
+ Color coding
+ Size coding
- Requires learning
(Shneiderman, ACM Trans. on Graphics, 1992 & 2003)
www.smartmoney.com/marketmap
Treemap: Smartmoney MarketMap
Market falls steeply Feb 27, 2007, with one exception
Market falls steeply Sept 22, 2011, some exceptions
Market mixed, February 8, 2008
Energy & Technology up, Financial & Health Care down
Market rises, September 1, 2010, Gold contrarians
Market rises, March 21, 2011, Sprint declines
newsmap.jp
Treemap: Newsmap (Marcos Weskamp)
Treemap: WHC Emergency Room (6304 patients in Jan2006)
Group by Admissions/MF, size by service time, color by age
Treemap: WHC Emergency Room (6304 patients in Jan2006) (only those service time >12 hours)
Group by Admissions/MF, size by service time, color by age
www.hivegroup.com
Treemap: Supply Chain
www.hivegroup.com
Treemap: Nutritional Analysis
www.spotfire.com
Treemap: Spotfire Bond Portfolio Analysis
Treemap: NY Times – Car&Truck Sales
www.cs.umd.edu/hcil/treemap/
Treemap (Voronoi): NY Times - Inflation
www.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html
VisualComplexity.com : Manuel Lima
Discovery Process: Systematic Yet Flexible
Preparation
• Own the problem & define the schedule
• Data cleaning & conditioning
• Handle missing & uncertain data
• Extract subsets & link to related information
SocialAction
• Integrates statistics & visualization
• 4 case studies, 4-8 weeks (journalist, bibliometrician, terrorist analyst, organizational analyst)
• Identified desired features, gave strong positive feedback about benefits of integration
Perer & Shneiderman, CHI2008, IEEE CG&A 2009 www.cs.umd.edu/hcil/socialaction
www.centrifugesystems.com
Network from Database Tables
NodeXL:
Network Overview for Discovery & Exploration in Excel
www.codeplex.com/nodexl
NodeXL:
Network Overview for Discovery & Exploration in Excel
www.codeplex.com/nodexl
NodeXL: Import Dialogs
www.codeplex.com/nodexl
Tweets at #WIN09 Conference: 2 groups
‘GOP’ tweets, clustered (red-Republicans)
Twitter networks: #SOTU
WWW2010 Twitter Community
Twitter Network for “TTW”
Twitter Network for #CI2012
Pittsburgh Metro
Westinghouse Electric
Pharmaceutical/Medical
No Location Philadelphia
Innovation Clusters: People, Locations, Companies
11,000 nodes
26,000 links
Pittsburgh Metro
Westinghouse Electric
Pharmaceutical/Medical
No Location Philadelphia
Innovation Clusters: People, Locations, Companies
Patent
Tech
SBIR (federal)
PA DCED (state)
Related patent
2: Federal agency
3: Enterprise
5: Inventors
9: Universities
10: PA DCED
11/12: Phil/Pitt metro cnty
13-15: Semi-rural/rural cnty
17: Foreign countries
19: Other states
Pittsburgh Metro
Westinghouse Electric
Pharmaceutical/Medical
No Location Philadelphia
Navy
Innovation Clusters: People, Locations, Companies
CHI2010 Twitter Community
www.codeplex.com/nodexl/
Flickr networks
Analyzing Social Media Networks with NodeXL
I. Getting Started with Analyzing Social Media Networks
1. Introduction to Social Media and Social Networks
2. Social media: New Technologies of Collaboration
3. Social Network Analysis
II. NodeXL Tutorial: Learning by Doing
4. Layout, Visual Design & Labeling
5. Calculating & Visualizing Network Metrics
6. Preparing Data & Filtering
7. Clustering &Grouping
III Social Media Network Analysis Case Studies
8. Email
9. Threaded Networks
10. Twitter
11. Facebook
12. WWW
13. Flickr
14. YouTube
15. Wiki Networks
www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
Social Media Research Foundation
Researchers who want to
- create open tools
- generate & host open data
- support open scholarship
Map, measure & understand
social media
Support tool projects to
collection, analyze & visualize
social media data.
smrfoundation.org
Sense-Making Loop
Thomas & Cook: Illuminating the Path (2004)
Sense-Making Loop: Expanded
Thomas & Cook: Illuminating the Path (2004)
Discovery Process: Systematic Yet Flexible
Preparation
• Own the problem & define the schedule
• Data cleaning & conditioning
• Handle missing & uncertain data
• Extract subsets & link to related information
Preparation
• Own the problem & define the schedule
• Data cleaning & conditioning
• Handle missing & uncertain data
• Extract subsets & link to related information
Purposeful exploration – Hypothesis testing
• Range & distribution
• Relationships & correlations
• Clusters & gaps
• Outliers & anomalies
• Aggregation & summary
• Split & trellis
• Temporal comparisons & multiple views
• Statistics & forecasts
Discovery Process: Systematic Yet Flexible
Preparation
• Own the problem & define the schedule
• Data cleaning & conditioning
• Handle missing & uncertain data
• Extract subsets & link to related information
Purposeful exploration – Hypothesis testing
• Range & distribution
• Relationships & correlations
• Clusters & gaps
• Outliers & anomalies
• Aggregation & summary
• Split & trellis
• Temporal comparisons & multiple views
• Statistics & forecasts
Situated decision making - Social context
• Annotation & marking
• Collaboration & coordination
• Decisions & presentations
Discovery Process: Systematic Yet Flexible
CHMNI Agenda
• Brain-computer interface
• Machine learning
• Natural language dialogue
• Sensing & perception
• Software agents
• Cognitive & social science
CHMNI Agenda: Extended
• Brain-computer interface
• Machine learning
• Natural language dialogue
• Sensing & perception
• Software agents
• Cognitive & social science
• Visualization
• Collaboration
• Social media networks
• Persuasion & Motivation
• Trust & Responsibility
• History-keeping & Logging
• Continuous improvement
Research Agenda in Visualization
• Presenting complex information to diverse users on small mobile devices
• Offering busy users timely information in the right format to support rapid and accurate decision
making
• Providing decision-makers powerful temporal and geo-spatial tools to detect patterns or subtle
changes over years.
• Thorough data cleaning to cope with missing values, duplicated records, incorrect data entry,
patient name entity resolution, and proper date-time stamps.
• Efficient and effective anonymization and de-identification algorithms so data sets can be made
more widely available, while protecting patient privacy.
• Scaling visualization techniques to billions of records by filtering and dynamically forming
aggregated values. This supports retrospective analyses by researchers who may seek to
compare across patients, physicians, hospitals, time periods, and geographic regions.
• Systematic yet flexible visual analytics processes that promote complete coverage of data and
analyses questions, while preserving the option of exploration in depth when novel insights are
found.
• Logging of complex sequences of visual analytics operations so users know what was done to
produce results, can save these workflows to reapply to new datasets, and can share the
workflows with colleagues.
• Collaborative insight gathering methods so that multiple analysts can work independently and
then integrate their findings. Team-oriented medical decision-making is especially challenging
since legal liability is involved so all team members must signal their concurrence.
• Better guidelines for presenting insights to diverse audiences. Interactive visualization results,
even color-coded sortable tables, when well-designed can be enormously helpful to many users.
UN Millennium Development Goals
• Eradicate extreme poverty and hunger
• Achieve universal primary education
• Promote gender equality and empower women
• Reduce child mortality
• Improve maternal health
• Combat HIV/AIDS, malaria and other diseases
• Ensure environmental sustainability
• Develop a global partnership for development
To be achieved by 2015
30th Anniversary Symposium
May 22-23, 2013
www.cs.umd.edu/hcil
For More Information
• Visit the HCIL website for 650 papers & info on videos
www.cs.umd.edu/hcil
• Conferences & resources: www.infovis.org
• See Chapter 14 on Info Visualization
Shneiderman, B. and Plaisant, C., Designing the User Interface:
Strategies for Effective Human-Computer Interaction:
Fifth Edition (2010) www.awl.com/DTUI
• Edited Collections:
Card, S., Mackinlay, J., and Shneiderman, B. (1999)
Readings in Information Visualization: Using Vision to Think
Bederson, B. and Shneiderman, B. (2003)
The Craft of Information Visualization: Readings and Reflections
For More Information
• Treemaps • HiveGroup: www.hivegroup.com
• Smartmoney: www.smartmoney.com/marketmap
• HCIL Treemap 4.0: www.cs.umd.edu/hcil/treemap
• Spotfire: www.spotfire.com
• TimeSearcher: www.cs.umd.edu/hcil/timesearcher
• NodeXL: nodexl.codeplex.com
• Hierarchical Clustering Explorer: www.cs.umd.edu/hcil/hce
• LifeLines2: www.cs.umd.edu/hcil/lifelines2
• Similan: www.cs.umd.edu/hcil/similan