We use cookies, including third-party cookies from Google to serve personalized ads through AdSense, to operate this site and understand how it is used. By continuing to browse, you accept this use. See our Privacy Policy and Terms of Use for details, including how to opt out of personalized advertising.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
    7 Min Read
    chatgpt image jul 21, 2026, 04 34 30 pm
    4 Core Benefits of Predictive Maintenance after Vibration Analysis
    10 Min Read
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results -- AI-generated illustration
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results
    11 Min Read
    chatgpt image jul 13, 2026, 04 23 45 pm
    How Data Analytics Helps Companies Improve User Engagement
    19 Min Read
    chatgpt image jul 13, 2026, 03 59 46 pm
    How Data Analytics Improves Multi-Location Search Strategies
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Slouching Toward Creepiness: Analyzing Human-Computer Interaction
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Uncategorized > Slouching Toward Creepiness: Analyzing Human-Computer Interaction
Uncategorized

Slouching Toward Creepiness: Analyzing Human-Computer Interaction

Daniel Tunkelang
Daniel Tunkelang
4 Min Read
Slouching Toward Creepiness: Analyzing Human-Computer Interaction
Photo by ArtAxis on Pixabay (https://pixabay.com/photos/office-computer-collaboration-9978146/)
SHARE

One of the perks of blogging is that publishers sometimes send me review copies of new books. I couldn’t help but be curious about a book entitled “The Man Who Lied to His Laptop: What Machines Teach Us About Human Relationships“–especially when principal author Clifford Nass is the director of the Communications between Humans and Interactive Media (CHIMe) Lab at Stanford. He wrote the book with Corina Yen, the editor-in-chief of Ambidextrous, Stanford’s journal of design.

They start the book by reviewing evidence that people treat computers as social actors. Nass writes:

to make a discovery, I would find any conclusion by a social science researcher and change the sentence “People will do X when interacting with other people” to “People will do X when interacting with a computer”

They then apply this principle by using computers as confederates in social science experiments and generalizing conclusions about human-compter interaction to human-human interaction. It’s an interesting approach, and they present results about how people respond to praise and criticism, similar/opposite personalities, etc. You can get a taste of Nass’s writing from an article he published in the Wall Street Journal entitled “Sweet Talking Your Computer“.

The book is interesting and entertaining, and I won’t try to summarize all of its findings here. Rather, I’d like to explore its implications.

More Read

Operationalize Predictive Analytics for Significant Business Impact
Operationalize Predictive Analytics for Significant Business Impact
Economist Research: Decision-Making in Turbulent Times
The Practice Mentality
Do You Really Want More Companies Using Social Media?
Social Collaboration Barriers in SMBs and Enterprises

Applying the “computers are social actors” principle, they cite a variety of computer-aided experiments that explore people’s social behaviors. For example, they cite a Stanford study on how “Facial Similarity Between Voters and Candidates Causes Influence” , in which secretly morphing a photo of a candidate’s face to resemble the voter’s face induces a significantly positive effect on the voter’s preference. They also cite  another experiment on similarity attraction that varies a computer’s “personality” to be either similar or opposite to that of the experimental subject. A similar personality draws a more positive response than an opposite one, but the most positive response comes from the computer starts off with an opposite  personality and then adapts to conform to the personality of the subject. Imitation is flattery, and–as yet another of their studies shows–flattery works.

It’s hard for me to read results like these and not see creepy implications for personalized user interfaces. When I think about the upside of personalization, I envision a happy world where we see improvement in both effectiveness and user satisfaction. But clearly there’s a dark side where personalization takes advantage of knowledge about users to manipulate their emotional response. While such manipulation may not be in the users’ best interests, it may leave them feeling more satisfied. Where do we draw the line between user satisfaction and manipulation?

I’m not aware of anyone using personalization this way, but I think it’s a matter of time before we see people try. It’s not hard to learn about users’ personalities (especially when so many like taking quizzes!), and apparently it’s easy to vary the personality traits that machines project in generated text, audio, and video. How long will it before people put these together? Perhaps we are already there.

O brave new world that has such people and machines in it. Shakespeare had no idea.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Flat editorial illustration: The article explains that training robots for physical interaction requires three distinct data cate
Physical AI: What Data Do You Need to Train a Robot?
Artificial Intelligence Exclusive Robotics
What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
Analytics Big Data Exclusive Software
Flat editorial illustration: The article examines AI agents that escalate from legitimate data retrieval to attempted intrusions
OpenAI’s Government Website Incidents Raise a Hard Question for AI Agents: When Should They Stop?
Artificial Intelligence News Security
Flat editorial illustration: The article's core relationship is the alignment between customer behavioral data (visit frequency,
Data-Driven Loyalty: How Restaurants Use Behavioral Analytics to Optimize Revenue
Exclusive

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Supply Management BPO on the verge of overheating
Uncategorized

Supply Management BPO on the verge of overheating

6 Min Read
How The Internet of Things Will Create a Smart World [INFOGRAPHIC]
Uncategorized

How The Internet of Things Will Create a Smart World [INFOGRAPHIC]

2 Min Read
5 of the Most Common IT Security Mistakes to Watch Out For
Uncategorized

5 of the Most Common IT Security Mistakes to Watch Out For

6 Min Read
Data Driven Lingerie?
Uncategorized

Data Driven Lingerie?

4 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
How To Get An Award Winning Giveaway Bot
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-26 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?