Dynamic Pricing and Bias After reading the article, How targeted ads and dynamic pricing can perpetuate bias,  in the Module 5: Lecture Materials & Resources , write a detailed summary on Dynamic Pricing and Bias. Submission Instructions: The paper is to be clear and concise and students will lose points for improper grammar, punctuation, and misspelling. The paper is to be 300 words in length, current APA style, excluding the title, abstract and references page. Incorporate a minimum of 2 current references (published within the last five years) scholarly journal articles or primary legal sources (statutes, court opinions) within your work. Complete and submit the assignment by 11:59 PM ET on Sunday. Late work policies, expectations regarding proper citations, acceptable means of responding to peer feedback, and other expectations are at the discretion of the instructor. You can expect feedback from the instructor within 48 to 72 hours from the Sunday due date. ------------------------------------------------------------------------------------------------------------------------------------------ Marketing   |   How Targeted Ads and Dynamic Pricing Can Perpetuate Bias Subscribe Sign In Diversity Latest Magazine Popular Topics Podcasts Video Store The Big Idea Visual Library Case Selections You have 1 free article left this month. Create an account to read 2 more. Marketing How Targeted Ads and Dynamic Pricing Can Perpetuate Bias by  Alex P. Miller and  Kartik Hosanagar November 08, 2019 Summary.    In new research, the authors study the use of dynamic pricing and targeted discounts, in which they asked if (and how) biases might arise if the prices consumers pay are decided by an algorithm. Suppose your company wants to use historical data to train an algorithm to identify customers who are most...   more Tweet Post Share Save Buy Copies Print In theory, marketing personalization should be a win-win proposition for both companies and customers. By delivering just the right mix of communications, recommendations, and promotions — all tailored to each individual’s particular tastes — marketing technologies can result in uniquely satisfying consumer experiences. While ham-handed attempts at personalization can give the practice a bad rap , targeting technologies are becoming more sophisticated every day. New advancements in machine learning and big data are making personalization more relevant, less intrusive, and less annoying to consumers. However, along with these developments come a hidden risk: the ability of automated systems to perpetuate harmful biases. In new research, we studied the use of dynamic pricing and targeted discounts, in which we asked if (and how) biases might arise if the prices consumers pay are decided by an algorithm. A cautionary tale of this type of personalized marketing practice is that of the Princeton Review. In 2015, it was revealed that the test-prep company was  cha.

1. Dynamic Pricing and Bias
After reading the article,
How targeted ads and dynamic pricing can perpetuate bias,
in the
Module 5: Lecture Materials & Resources
, write a detailed summary on Dynamic Pricing and Bias.
Submission Instructions:
The paper is to be clear and concise and students will lose
points for improper grammar, punctuation, and misspelling.
The paper is to be 300 words in length, current APA style,
excluding the title, abstract and references page.
Incorporate a minimum of 2 current references (published
within the last five years) scholarly journal articles or primary
legal sources (statutes, court opinions) within your work.
Complete and submit the assignment by 11:59 PM ET on
Sunday.
Late work policies, expectations regarding proper citations,
acceptable means of responding to peer feedback, and other
expectations are at the discretion of the instructor.
You can expect feedback from the instructor within 48 to 72
hours from the Sunday due date.
---------------------------------------------------------------------------
---------------------------------------------------------------

2. Marketing
|
How Targeted Ads and Dynamic Pricing Can Perpetuate Bias
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Marketing
How Targeted Ads and Dynamic Pricing Can Perpetuate Bias
by

3. Alex P. Miller
and
Kartik Hosanagar
November 08, 2019
Summary.
In new research, the authors study the use of dynamic pricing
and targeted discounts, in which they asked if (and how) biases
might arise if the prices consumers pay are decided by an
algorithm. Suppose your company wants to use historical data to
train an algorithm to identify customers who are most... more
Tweet
Post
Share
Save
Buy Copies
Print
In theory, marketing personalization should be a win-win
proposition for both companies and customers. By delivering
just the right mix of communications, recommendations, and
promotions — all tailored to each individual’s particular tastes

4. — marketing technologies can result in uniquely satisfying
consumer experiences.
While ham-handed attempts at personalization can give the
practice
a bad rap
, targeting technologies are becoming more sophisticated every
day. New advancements in machine learning and big data are
making personalization more relevant, less intrusive, and less
annoying to consumers. However, along with these
developments come a hidden risk: the ability of automated
systems to perpetuate harmful biases.
In new research, we studied the use of dynamic pricing and
targeted discounts, in which we asked if (and how) biases might
arise if the prices consumers pay are decided by an algorithm. A
cautionary tale of this type of personalized marketing practice is
that of the Princeton Review. In 2015, it was revealed that the
test-prep company was
charging customers in different ZIP codes different prices
, with discrepancies between some areas reaching hundreds of
dollars, despite the fact that all of its tutoring sessions took
place via teleconference. In the short term, this type of dynamic
pricing may have seemed like an easy win for boosting
revenues. But
research has consistently shown
that consumers view it as inherently unfair,
leading to lower trust and repurchasing intentions
. What’s more, Princeton Review’s bias had a racial element:
a highly publicized follow-up investigation
by journalists at ProPublica demonstrated how the company’s
system was, on average, systematically charging Asian families
higher prices than non-Asians.
INSIGHT CENTER

5. AI and Bias
Building fair and equitable machine learning systems.
Even the largest of tech companies and algorithmic experts have
found it challenging to deliver highly personalized services
while avoiding discrimination. Several
studies
have shown that ads for high-paying job opportunities on
platforms such as Facebook and Google are served
disproportionately to men. And, just this year,
Facebook was sued
and found to be in violation of the Fair Housing Act for
allowing real estate advertisers to target users by protected
classes, including race, gender, age, and more.
What’s going on with personalization algorithms and why are
they so difficult to wrangle? In today’s environment — with
marketing automation software and automatic retargeting, A/B
testing platforms that dynamically optimize user experiences
over time, and ad platforms that automatically select audience
segments — more important business decisions are being made
automatically without human oversight. And while the data that
marketers use to segment their customers are not inherently
demographic, these variables are often correlated with social
characteristics.
To understand how this works, suppose your company wants to
use historical data to train an algorithm to identify customers
who are most receptive to price discounts. If the customer
profiles you feed into the algorithm contain attributes that
correlate with demographic characteristics, the algorithm is
highly likely to end up making different recommendations for
different groups. Consider, for example, how often cities and

6. neighborhoods are divided by ethnic and social classes and how
often a user’s browsing data may be correlated with their
geographic location (e.g., through their IP address or search
history). What if users in white neighborhoods responded
strongest to your marketing efforts in the last quarter? Or
perhaps users in high-income areas were most sensitive to price
discounts. (This is known to happen in some circumstances not
because high-income customers can’t afford full prices but
because they shop more frequently online and
know to wait for price drops
.) An algorithm trained on such historical data would — even
without knowing the race or income of customers — learn to
offer more discounts to the white, affluent ones.
To investigate this phenomenon, we looked at dozens of large-
scale e-commerce pricing experiments to analyze how people
around the United States responded to different price
promotions. By using a customer’s IP address as an
approximation of their location, we were able to match each
user to a US Census tract and use public data to get an idea of
the average income in their area. Analyzing the results of
millions of website visits, we confirmed that, as in the
hypothetical example above, people in wealthy areas responded
more strongly to e-commerce discounts than those in poorer
ones and, since dynamic pricing algorithms are designed to
offer deals to users most likely to respond them, marketing
campaigns would probably systematically offer
lower
prices to
higher
income individuals going forward.
What can your company can do to minimize these socially
undesirable outcomes? One possibility for algorithmic risk-
mitigation is formal oversight for your company’s internal
systems. Such “AI audits” are likely to be complicated

7. processes, involving assessments of accuracy, fairness,
interpretability, and robustness of all consequential algorithmic
decisions at your organization.
While this sounds costly in the short term, it may turn out to be
beneficial for many companies in the long term. Because
“fairness” and “bias” are difficult to universally define, getting
into the habit of having more than one set of eyes looking for
algorithmic inequities in your systems increases the chances
you catch rogue code before it ships. Given the social,
technical, and legal complexities associated with algorithmic
fairness, it will likely become routine to have a team of trained
internal or outside experts try to find blind spots and
vulnerabilities in any business processes that rely on automated
decision making.
As advancements in machine learning continue to shape our
economy and concerns about wealth inequality and social
justice increase, corporate leaders must be aware of the ways in
which automated decisions can cause harm to both their
customers and their organizations. It is more important than
ever to consider how your automated marketing campaigns
might discriminate against social and ethnic groups. Managers
who anticipate these risks and act accordingly will be those who
set their companies up for long-term success.
Read more on
Marketing
or related topics
Pricing
and
Technology
AM
Alex P. Miller

8. is a doctoral candidate in Information Systems & Technology at
the University of Pennsylvania’s Wharton School.
KH
Kartik Hosanagar
is a Professor of Technology and Digital Business at The
Wharton School of the University of Pennsylvania. He was
previously a cofounder of Yodle Inc. Follow him on Twitter
@khosanagar.
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